strategic multi scale october 2010

VisionVisionVisionVision PaperPaperPaperPaper

“Strategic Multiscale:

A New Frontier for

R&D and Engineering”

Alessandro Formica

October 2010

All rights reserved

Alessandro Formica – October 2010 All rights reserved

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TABLE OF CONTENTS

1. Introduction pag. 3

2. Strategic Multiscale Framework pag. 4

2.1 R&D and Engineering Scenario: From Computational To Strategic Multiscale pag. 4

2.2 Strategic Multiscale Framework Architecture pag. 8

2.3 Strategic Multiscale Framework Goals pag. 9

3. Integrated Multiscale Science - Engineering Framework pag. 11

3.1 Architecture pag. 11

3.2 Multiscale Data, Information and Knowledge Analysis and Management System pag. 13

3.3 Multiscale Science – Engineering Information Space pag. 19

3.4 Modeling & Simulation as Knowledge Integrators and Multipliers pag. 23 3.4.1 The New Computational Modeling Vision pag. 23

3.4.2 Extension of the Multiscale Approach to the Experimental, Testing and Sensing Worlds pag. 27

3.4.3 Methodologically Integrated Multiscale Science – Engineering Strategies pag. 30

3.4.4 Multiscale Knowledge – Based Virtual Prototyping and Testing pag. 32

3.5 Designing the R&D and Engineering Process pag. 33 3.5.1 The Information - Driven Concept pag. 33

3.5.2 The R&D and Engineering Process Design Management System pag. 35

3.5.3 Multiscale R&D and Engineering Information - Driven Strategies pag. 38

3.6 Integrated Multiscale Science – Engineering Analysis Strategies pag. 39

4. Integrated Multiscale Science – Engineering Technology, Product and Process pag. 43 Development (IMSE-TPPD)Framework 4.1 Overview and Architecture pag. 43

4.2 Multiscale Systems Engineering pag. 47

4.3 Multiscale Process Engineering pag. 49

4.4 Multiscale Environmental, Safety and Extreme Engineering pag. 51

4.5 Innovative Technology and Systems Development Planning pag. 54

5. Integrated Multiscale R&D and Engineering Infrastructural Framework pag. 57

Biography pag. 60

Contacts pag. 61


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1. Introduction

This document intends to be a first attempt to analyze the “structural” impact of the “Strategic Multiscale”

view on the R&D and engineering organization and the way complex innovative technology

developments and engineering solutions are planned, managed and implemented.

Relationships between science and engineering, basic and applied research, Innovation and industry are

deeply changing, accordingly, a new language and theoretical framework to understand and manage this

evolving scenario and drive technology innovation well into the 21st century, is a reasonable step forward.

Dramatic advances in Computing Information and Communication (CIC) technologies are reshaping

research, Innovation and industry. However, significant methodological advances are needed to take full

advantage of this technological “Revolution” and effectively cope with educational, industrial, economic,

environmental and societal challenges.

The fundamental thesis of this White Book is that, to meet 21st century innovative technology development

and complex systems engineering analysis and design challenges, we need important improvements in

Methodology and the way Information is dealt with inside R&D and engineering. This development process

can be started by implementing what we call a “Strategic” view of the Multiscale concept and method.

Computational Multiscale is today widely regarded as a “New Frontier” for Computational Science and

Engineering. “Strategic Multiscale” can be a “New Frontier” for Science and Engineering and Science-

Engineering Integration.

The term “Strategic” means that multiscale will be the catalyst to deeply change R&D and Engineering and

Education organization, structure and strategies.

Strategic Multiscale is not only a new methodology, but a unifying paradigm to enable integration of

science and engineering as it was defined by Villermaux, Ka, Ng, Formica, in the mid of nineties. Central

elements of the Strategic Vision of Multiscale are a new concept of Modeling and Simulation as “Knowledge

Integrators and Multipliers” and “Unifying Paradigm” for Scientific and Engineering Methodologies and a

new set of Multiscale Science - Engineering Data Information and Knowledge Schemes and Strategies. This

Vision directly leads to the extension of the multiscale concept to the experimental, testing and sensing

worlds and a comprehensive integration of a full spectrum of multiscale computational and experimental,

testing and sensing methodologies and related knowledge domains. The ultimate goal is to define more

general “Methodologically Integrated Multiscale Multidisciplinary R&D and Engineering Strategies”. The

“Strategic Multiscale” Vision embodies three Frameworks:

� The “Integrated Multiscale Science – Engineering Framework” which represents the theoretical,

conceptual and methodological basis

� The “Integrated Multiscale Science – Engineering Technology, Product and Process Development

(IMSE-TPPD) Framework” which is constituted by a set of Software Environments that implement

theories, methods and concepts described in the previously quoted Framework

� The “Integrated Multiscale R&D and Engineering Infrastructural Frameworks”

Application of “Strategic Multiscale” to the Education, Information and Communication areas is described

by a Vision Paper enclosed to this Document: Multiscale Science – Based Language”.


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2. Strategic Multiscale Framework

2.1 R&D and Engineering Scenario:

From Computational to Strategic Multiscale

In mid of nineties several researchers in the Chemical Engineering field (Sapre and Katzer, Lerou and Ng,

and Villermaux) and the author of this White Book (Alessandro Formica) highlighted the need of a

comprehensive multiscale approach as a key strategic step to establish a new “Unifying Paradigm” to enable

integration of science and engineering. This vision was highlighted, at the 5th World Congress of Chemical Engineering (1996), San Diego, CA,

USA, by the late lamented Prof. Jacques Villermaux (at that time Vice President of European Federation of

Chemical Engineering) Later on, Prof. Charpentier, past European Federation of Chemical Engineering

President, illustrated similar concepts:

Taking advantage of these conceptual advances, in the White Book “Multiscale Science – Engineering

Integration – A New Frontier for Aeronautics, Space and Defense (May 2003) sponsored and published by

Italian Association of Aeronautics and Astronautics (AIDAA), Formica introduced the concept of “Strategic

Multiscale” and he described the related Integrated Framework.

Strategic Multiscale is the theoretical and methodological basis to reshape R&D and Engineering

organization, structure and strategies integrating science and engineering analytical, computational and

experimental, testing and sensing methodologies and techniques. New Frameworks and new Data,

Information and Knowledge Management Systems, based on the multiscale science-engineering integration

concept, can contribute to redefine knowledge transfer along the whole chain: basic research, applied

research, technology development and integrate R&D, engineering, manufacturing and operational testing.

Multiscale as “Unifying Paradigm for Chemical Engineering

Prof. Charpentier, past European Federation of Chemical Engineering (EFCE) President, at the

6th World Congress of Chemical Engineering - Melbourne 2001, described his Vision of

Multiscale as “Strategic Paradigm” for Chemical Engineering.

We report his words :

“One key to survival in globalization of trade and competition, including needs and challenges,

is the ability of chemical engineering to cope with the society and economic problems

encountered in the chemical and related process industries. It appears that the necessary

progress will be achieved via a multidisciplinary and time and length multiscale integrated

approach to satisfy both the market requirements for specific end use properties and the

environmental and society constraints of the industrial processes and the associated services.

This concerns four main objectives for engineers and researchers:

(a) total multiscale control of the process (or procedure) to increase selectivity and productivity,

(b) design of novel equipment based on scientific principles and new methods of production:

process intensification,

(c) manufacturing end-use properties for product design: the triplet ‘processus-product-process’

engineering,

(d) implementation of multiscale application of computational modeling and simulation to real-

life situations: from the molecular scale to the overall complex production scale.”


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The drivers for a new vision of Multi scale come from some specific features which characterize modern

R&D and Engineering Scenario. Three key issues, in particular, play a key role:

a) Integration of Science and Engineering (nanotechnology is only the most evident sign of this process)

b) Performance and Optimization pushed to the limits

c) Growing complexity of technological and engineering systems and of the related R&D and Engineering

Processes

These issues heavily condition innovative technology and system development times, costs and risks and

programs organization, structure and management.

a) Science-Engineering Integration: Science has become a key Technological and Engineering

Variable Scientific knowledge is increasingly at the root of new technology developments in key fields : materials,

materials processing, electronics, optics, combustion,…..Innovative technologies are directly based on

scientific principles and phenomena but, unified scientific and engineering environments are in the early

development phase. Technological Systems integrate a wide spectrum of sub-systems, components and

devices working not only at the classical engineering scales (macro and meso), but, also, at scientific space

and time scales (micro and nano). Nano and Micro technologies are territories where science and engineering

meet together. Nano technologies open the way to the definition of a new generation of “inherently

hierarchical multiscale materials, devices and systems”. We are seeing the birth of new fields: “Quantum

Engineering” and “Hierarchical Multiscale Engineering”

b) Performance, Requirements and Optimization pushed to the limits Pushing performance, requirements and optimization to the limits means that understanding, predicting, and

controlling systems dynamics is increasingly dependent on understanding, predicting, and controlling a

hierarchy of physical (chemical and biological) mutually interacting phenomena occurring at a wide range of

space and time scales. In this context, small phenomena at the lowest scales can have a major impact on the

behaviour of a macro system. Accordingly, classical homogenization and averaging procedures, as well as

semi empirical or simplified formulations, which worked well in the past, are, in many cases, no longer up to

the challenge

c) Complexity “Complexity” is a very general, if not generic at all, term. It is possible to relate “Complexity” to our

capability to understand, predict and control the dynamics of a “System. In the context of this White Book,

we consider five interrelated types of complexity :

Physics Complexity (directly related to multiscale and the science-engineering integration issue) Multiscale Multiphysics hierarchies of physical phenomena and processes underlie the behaviour of systems,

sub-systems, components, devices and states of matter (materials, fluids, plasmas).

Functional and Operational Complexity

Widening range of functions to be performed by systems, widening operational envelope and widening

spectrum of requirements to be met (energy efficiency, environmental compliance, safety, development and

operational costs, life – cycle issues,…)

System (or Architectural) Complexity

A technological System is constituted by a full hierarchy (from macro to nano) of subsystems, components

and devices which use a wide range of different technologies (mechanical, bio, info, electronics,

optoelectronics, fluidics,…) and operate across a widening range of scales. That implies a network of

interactions among the full hierarchy of subsystems, components and devices which span a wide spectrum of

different space and time scales and, globally, condition the macro life – cycle performance of the system

R&D and Engineering Process Complexity This kind of complexity can be identified as: the “Fragmentation Issue” for R&D and Engineering. Said in

more specific terms, it refers to the always continuously growing spectrum of models, methods, data, and

information characterizing R&D and Engineering Processes. The “Fragmentation Issue” heavily condition

architecture, organization, and structure of the R&D and Engineering processes associated with innovative

technology and system development.


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Uncertainties Management Complexity Uncertainties are related to any aspect of the R&D and Engineering process. The lack of a comprehensive

and rigorous strategy to deal with uncertainties, in a systematic way, inside the R&D and Engineering

process, seriously limits our capability to reliably predict systems behavior, select alternative technological

and engineering solutions, validate computational and experimental, testing and sensing methods/techniques,

define the right mix among theory, modeling & simulation, and experimentation & testing. Uncertainty is a

function of physics (scales and disciplines), systems and process complexity.

Sources of Uncertainties (the list is not exhaustive) :

− Physics : If you do not know physics you are not able to assess model reliability, predictive capabilities

and applicability conditions.

− Geometry : The continuous reduction of the dimension of devices, components, and subsystems makes

even small geometric errors ever more critical

− Manufacturing : The ever growing relevance of even very small structural and compositional variations

on properties and performance of processed/manufactured materials and parts.

− Modeling : Uncertainty characterizes hypotheses computational models are formulated upon as well as

input data , initial and boundary conditions.

− Operational Environment (Loading Conditions) : Interactions between an high-tech system and its

operational environment involve highly complex physical phenomena and a multitude of different

nominal and off-nominal cases and situations.

− System Complexity : Interactions among devices, component, and subsystems making up a high-tech

systems involve a highly entangled pattern of multiscale, multimedia, multidisciplinary phenomena

which is practically impossible to characterize in a deterministic way.

− Information Uncertainty. Last but not least, we take into account what we can call the “Information

Uncertainty Challenge”. An often neglected uncertainty is linked to the determination of what

information is needed in critical tasks of the R&D and engineering process, what is the needed level of

accuracy, what is the range of validity and reliability level of (computational and experimental & testing)

models, what is the right mix between modeling & simulation and experimentation & testing to

accomplish tasks.

Two particularly critical issues are : “known unknowns” (unknown solutions to known problems) and the so

called “unknown unknowns” (unknown sources of uncertainty). Both have a critical relevance for R&D and

Engineering.

The Figure is drawn from : “Modeling and Simulation In Support of T&E and Acquisition” Dr. Frank Mello,

OSD/DOT&E Presented at: The International Congress & Exhibition on Defense Test and Evaluation and

Acquisition: The Global Marketplace Vancouver, British Columbia, Canada


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It is possible to identify three fundamental stages in the development of Multiscale and Science –

Engineering Integration :

a) Multiscale Computational tools and methods to address specific R&D and Engineering issues. It is the

basic development stage. There is an increasing activity to improving existing multiscale methods and

develop new schemes and strategies (adaptive, concurrent, hierarchical,….)

b) Integrated Computational Multiscale Environments to address complex R&D and Engineering tasks.

Materials and Biology are key application fields. It can be considered as the “State of the Art”.

c) “ Strategic Multiscale Science – Engineering Frameworks”, based upon the “Strategic Vision of

Multiscale”, which could be a starting point to change organization and structure of the R&D and

Engineering landscape. A possible structure of this kind of Frameworks and related application fields is

outlined in this White Book.

The theoretical and methodological basis of the “Strategic Multiscale” Vision is constituted by the following

key elements:

− The Multiscale concept and method as basic theoretical and methodological element, extended, in this

context, to the experimental, testing and sensing fields

− A new “Vision” of Modeling & Simulation as “Knowledge Integrators and Multipliers” and “Unifying

Paradigm” for Scientific and Engineering Methodologies. In this perspective “Modeling & Simulation”

integrate the full spectrum of science and engineering methodological approaches and knowledge

environments.

− The “Science-Engineering Information Space” concept to integrate computational models and methods

and experimental, testing and sensing models and techniques

− The “Information – Driven Analysis” concept and scheme which, together with the Science –

Engineering Information Space” concept is a key element to shape Multiscale Methodologically

Integrated R&D and Engineering Analysis Strategies (Designing the R&D and Engineering Design

Process)

− New Multiscale Science – Engineering Data, Information and Knowledge Management Systems based

upon the Maps concept

− New methods to “Design” the R&D and Engineering Process

The “Integrated Multiscale Science-Engineering Framework” represents the basis to perform the transition:

� From traditional CAD, CAE, CAM systems to Multiscale Science - Engineering CAD, CAE, CAM

systems

� From “Integrated Product and Process Development (IPPD)” Frameworks to a new generation of

Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-

TPPD)”Frameworks

It is of fundamental importance to highlight that the definition of an “Integrated Multiscale Science-

Engineering Framework” does not mean that specific aspects and peculiarities of Basic Research, Applied

Research, Technology Development and Engineering should be canceled and/or neglected and that all the

activities in these different Scientific and Engineering domains should be tightly correlated and inserted

inside global rigid schemes. Not at all. The author of this White Book is convinced that, for the progress of

Science and Engineering, is, and, it will be, of fundamental importance that a significant part of basic and

applied research activities are to be carried out for the “sake of knowledge” outside of rigid guiding schemes.

The fusion between a “science-driven engineering” and an “engineering-driven science” approach means a

reasonable balance between “directed” and “freely motivated” research activities

Note: Multiscale is a general term, it incorporates, as a special case, classical single scale methods and

models. Multiscale stands for Multiscale Multiresolution Multiphysics in the most general case.


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2.2 Strategic Multiscale Framework Architecture

The overall “Strategic Multiscale Framework” is constituted by three specific interrelated Frameworks:

A. “ Integrated Multiscale Science - Engineering Framework” which describes the fundamental concepts

and methods to develop new R&D and Engineering Strategies, SW Frameworks and related R&D and

Engineering SW and HW Infrastructures (B and C items)

B. “Integrated Multiscale Science – Engineering Technology, Product and Process Framework” which represents the Integrated Product and Process Development (IPPD) Frameworks next Generation

C. “Integrated Multiscale R&D and Engineering Infrastructural Framework

The impact of the “Strategic Multiscale Vision” on the Education, Information and Communication World is

analyzed in the enclosed “Multiscale Science – Based Language and Framework” Vision Paper.


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2.3 Strategic Multiscale Framework Goals

As quoted in the Introduction, the fundamental goal of the Strategic Multiscale is to change in a qualitative,

not, quantitative way, organization, structure and strategies of the R&D and Engineering landscape and

catalyze and foster a spectrum of innovation trends:

Innovation for the For the Computing World

Fostering the design, development and application of a new generation of HW Systems b ased upon

Multiscale Quantum Engineering Architectures and Technologies

Fostering the design, development and application of a new generation of Integrated SW Frameworks

which realize:

− a comprehensive Multiscale Science – Engineering Integration [already established]

− a comprehensive Methodological Integration (computation, experimentation, testing and sensing)

[to a large extent to be still comprehensively developed]

and that incorporate new Multiscale Science – Engineering Data, Information and Knowledge Analysis,

Integration and Management Schemes

Catalyzing the development of new SW Frameworks for the “/Modeling” of complex Integrated R&D

and Engineering Processes (Designing the R&D and Engineering Processes)

Catalyzing the development of new SW Frameworks for the “/Modeling” of Technology and Systems

Development Planning Processes

Innovation For Technology and Engineering

Promoting and Easing the development a New Field for R&D and Engineering: Multiscale

Experimentation, Testing and Sensing (Paragraph 3.4.2) Advances in computational modeling and technological fields have created the opportunity to extend the

multiscale approach from the computational world to the experimental, testing and sensing ones. Activities

are already started in Europe (Max Planck, and European Synchrotron Research Facility, for instance), US

and Japan. What is needed today are integrated large scale and scope initiatives. This new field entails the

development of new multiscale experimental and testing characterization protocols, technologies and

operational modes and new sensing network architecture and operational methodologies. Methodologically

Integrated Multiscale Science – Engineering Strategies described in this document allow to take full

advantage, in a synergistic way, of progress in both the fields: Modeling and Simulation and

Experimentation, Testing and Sensing, to define a real new way to do Research, Technology Innovation and

Engineering.

New Technological and Engineering Solutions (Multiscale Technology and Engineering: From

Multiscale Analysis to Multiscale Design) (Paragraphs 4.2, 4.3, 4.4). New Methodologically Integrated Analysis and Design Strategies and New Integrated Data, Information and

Knowledge Analysis and Management Frameworks put the bases to design “inherently” Hierarchical

Multiscale Systems (materials, structures components and products) which is a fundamental condition to

fully exploit in the industrial environment the potentialities of Nano and Micro Technologies. “Multiscale

Systems”, are. systems organized following a Hierarchical strategy where structures at the different scales

interact in a synergistic way to determine an extended spectrum of functionalities and performance.

The EU NMP (Sixth Framework) Integrated Multiscale Process Units Locally Structured Elements

(IMPULSE 2005 – 2009) Program is a very interesting example of this trend. IMPULSE is Europe’s flagship

R&D initiative for radical innovation in chemical production technologies. In the Materials Field, the

Hierarchic Engineering of Industrial Materials (HERO-M) Center has been recently set up at the Royal

Institute of Technology, Stockholm


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Innovation For Research, Technology Development and Engineering Process Organization,

Strructure and Strategies

Knowledge Transfer along the R&D and Engineering Technology Readiness Levels Scale Strategic Multiscale Strategies and Frameworks allow to Organize and Structure Scientific Knowledge is

such a way to make it directly applicable to Analyze, Design and Manufacturing Innovative Technology and

Industrial Systems. New Data, Information and Knowledge Management Systems (Paragraph 3.2), based on

the multiscale science-engineering integration concept, can contribute to redefine knowledge transfer along

the whole chain: basic research, applied research, technology development and integration, engineering,

manufacturing, operational testing. That leads to accelerate the pace of the insertion of research

achievements inside technology development and engineering design and improve effectiveness and

efficiency of the whole process. Multiscale means “Multiscale Multiphysics”. Multiscale is intrinsically

Multidisciplinary and Interdisciplinary as to become a very powerful integrator of knowledge.

Definition of Unified Principles and Schemes to Analyze and Organize R&D and Engineering Data,

Information and Knowledge

New schemes described in the document allow to organize data, information and knowledge from a full

spectrum of “Information Sources” (computational models, experimentation, testing and sensing) in such a

way that different research and engineering disciplinary and application fields can take advantage from

information and knowledge from other disciplines and application sectors to develop “Integrated

Multidisciplinary Science - Engineering Knowledge Domains” are described in the Paragraph 3.4.3 and

Paragraph 3.6.

Development of Methodologically Integrated Multiscale R&D and Engineering Strategies The concept of Modeling & Simulation as “Knowledge Integrators and Multipliers”, the “Multiscale Data,

Information and Knowledge Analysis and Management System and the development of Multiscale

Experimentation and Testing Strategies open the way to the definition of “Methodologically Integrated

Multiscale Strategies” (Paragraph 3.4.3) to implement a full integration of different Multiscale Multilevel

Computational, Experimental, Testing and Sensing methods and techniques not only in the computational

models development and validation phases, but, also, in the application one

A New Generation of Computational Centers (Chapter 5)

The Vision of “Modeling and Simulation as “Knowledge Integrators and Multipliers” and “Unifying

Paradigm” for the full spectrum of R&D and Engineering Methodologies (Experimentation, Testing and

Sensing) open the way to the creation of a New Generation of “Multiscale Multidisciplinary Science –

Engineering Knowledge Integrator and Multiplier” Centers. In this new context Computational Centers

integrate themselves with Experimental and Testing Facilities and Field Monitoring Systems operating over

a full range of scientific and engineering scales.

Building a new Generation of Cyberinfrastructures (Chapter 5) The new generation of Cyberinfrastructures which can be referred to as “Integrated Multiscale

Multidisciplinary Knowledge Integrator and Multiplier Cyberinfrastructural Environments” foresee a

comprehensive on-line integration of the full spectrum of Scientific and Engineering Methodologies and

related Teams. The Unifying Conceptual Context if offered by the new Modeling and Simulation Vision

(Modeling and Simulation as Knowledge Integrators and Multipliers) and the related Knowledge

Management schemes.


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3. Integrated Multiscale Science - Engineering Framework

3.1 Architecture

Main elements of the Conceptual and Methodological Framework are:

� Multiscale Science - Engineering Data, Information and Knowledge Analysis and Management System

� Multiscale Science – Engineering Information Space

� Modeling & Simulation as “Knowledge Integrators and Multipliers” and Unifying Paradigm for

Scientific and Engineering Methodologies

The role of Multiscale as “Unifying Paradigm and Language” for Science and Engineering was discussed

by Alessandro Formica) some years ago in the book - Computational Stochastic Mechanics In a Meta-

Computing Perspective – December 1997 - Edited by J. Marczyk – pag. 29 – Article: A Science Based

Multiscale Approach to Engineering Stochastic Simulations.

� Information – Driven Multiscale Science – Engineering Analysis Concept and Schemes

� Methodologically Integrated Multiscale Science – Engineering Methodologies

� New Methods, Tools and Strategies to Design the R&D and Engineering Process

� Integrated Multiscale R&D and Engineering Analysis Strategies


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3.2 Multiscale Science – Engineering

Data, Information and Knowledge Management System

A critical issue for a wide diffusion of the science – based engineering analysis and design approach in the

industrial field is the availability of Software Environments (CAD/CAE/CAM) specifically conceived for

this kind of approach. Today, notwithstanding the growing diffusion of multiscale inside university,

research, and even industry, software environments (CAD/CAE/CAM) specifically conceived to implement

multiscale science-engineering integration visions and strategies are only in their starting phase. The lack of

software environments specifically conceived to implement a multiscale science-engineering integration

strategy represents a “fundamental” hurdle to a large scale implementation of multiscale inside innovative

technology development and engineering fields. The growing complexity of Materials, Devices,

Components, Systems and Systems of Systems which embody mutually interacting hierarchies of

technological, natural and human elements, suggest some specific extensions to classical CAD/CAE and

related Data, Information and Knowledge Management Systems.

The new Data, Information and Knowledge Management System proposed in this Vision Paper hinges on

the concept of “Map”. Map is a “Information and Knowledge Structure” which allows to integrate, link

and analyze Data from the full spectrum of scales (from atomistic to macro), the full spectrum of disciplines

and from a wide range of “Data Sources” (analytical and computational models, data bases, experimentation,

testing and sensing).

The Data, Information and Knowledge Management System correlates and fuses inside a coherent and

comprehensive framework data and information coming from different scientific and engineering teams,

from different methodologies, from the different tasks in the different stages of the whole Technology

Development and Engineering process. “Maps” allow for an effective insertion and management of the

more fundamental knowledge (basic and applied research) inside Technology Development and Engineering

phases. The concept of “Multiscale Map”, in the context of a Multiscale Vision and Framework, was

described by Alessandro Formica in the “Multiscale Science – Engineering Integration A new Frontier for

Aeronautics, Space and Defense” White Book, published by Italian Association of Aeronautics and

Astronautics (AIDAA) on March 2003.

Multiscale Maps (we would like to state again that Multiscale is a general term that includes as particular

case the single scale case)

Multiscale Maps describe (2D, 3D, or 4D representations) relationships between:

− Data (Multiscale Data Maps). Multiscale Data Maps are built applying statistical analysis schemes

(multivariate, PCA) or other techniques like neural networks. Multiscale Data Maps can be referred to as

Analysis and Design Variable Maps and they describe relationships between variables and parameters

used to characterize “Systems Behaviour”

− Physics Multiscale Physics Maps describing relationships between Physical (Chemical and

Biochemical) Phenomena

− System Architectural/Structural Elements (Multiscale Science – Engineering System Maps) describing

relationships between the hierarchy of Sub-Systems, Components, Devices, Elementary Structures

constituting a System

− Functions (Multiscale Science – Engineering Functional Maps) describing relationships between

System Architectural/Structural Element sand Functions performed

− Requirements - Performance – Properties – Architectural/Structural Elements (Requirement -

Performance – Property – Structure Maps) describing relationships between Requirements,

Performance (measured or calculated), Architectural/Structural Element and related Properties over the

whole scales and resolution levels

− Processing/Manufacturing Techniques – System Structures (Processing/Manufacturing – Structural

Maps) describing relationships between Processing/Manufacturing techniques and structures properties,

composition and characteristics (measured or calculated)


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Main objectives:

� Developing new schemes allowing for a more in-depth analysis of data, information and knowledge

and related correlations and interdependencies

� Integrating the full spectrum of “Data Sources” (Data Bases, Analytical Theories, Computational

Models, Experimentation and Testing). The “Information Space” and the “Modeling and Simulation

as Knowledge Integrators and Multipliers” ease this kind of Integration

� Developing new CAD/CAE Environments specifically conceived to Design new Hierarchical

Multifunctional Systems in the context of an Integrated Science – Engineering Approach

� Developing new Integrated Science – Engineering CAD/CAE Environments able to be applied

inside the whole R&D and Engineering Process.

Maps are indexed and related to specific R&D and Engineering Tasks and Phases and Design Hypotheses

and Decisions

A Multiscale Science – Engineering Data, Information and Knowledge Management System records,

organizes and manages: Information about all the previously defined Maps

This figure, drawn from “Overview of the Fusion Materials Sciences Program Presented by S.J. Zinkle, Oak

Ridge National Lab Fusion Energy Sciences Advisory Committee Meeting February 27, 2001

Gaithersburg”), depicts a “Structure” like the proposed Multiscale Maps. In this case the Multiscale Map

describes relationships between physical phenomena and chemical/physical structural transformations linked

to Radiation Damage Process for Metals

Several Multiscale Maps can define what can be called a “Knowledge Domain”. “Knowledge Domains”

can be organized in a “Hierarchical Way”. For instance, A Physical “Knowledge Domain” linked to a

specific Process (Hypervelocity Impact or Explosion) can be constructed by assembling a range of

Multiscale Physical Maps describing more elementary physical (chemical and biochemical) phenomena

(fracture, fragmentation, phase change,..) for a material or component of a specific System.

Knowledge Domains are managed by a Multiscale Science – Engineering Knowledge Management

System.


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Hierarchical Multiscale Multilevel Architectural and Structural Maps

Any “System” of arbitrary degree of complexity (an air transportation system, an energy production system,

an aerospace vehicle, a chemical plant, a structure, a nanotechnology device, a nanostructured material), can

be recursively broken down in a set of simpler (macro, meso, micro, nano and atomistic) “Architectural and

Structural Elements”. The following figure (from EADS) illustrates a two dimensional multilevel multiscale

view of an aircraft.

Two new features distinguish this kind of Maps and related Multiscale Multilevel Science – Engineering

CAD Systems:

− They should describe Architectural and Structural Elements of a System (or System of Systems) and

interconnections among all its constituents including the “Operational Environment” which is considered

as a “Architectural Element”. This feature is of particular importance if we like to assess the impact of

the System upon the environment where it operates and the effects of the Environment on the System for

the whole Life Cycle and the whole spectrum of operational conditions including extreme ones and

accidents.

− Zooming and Selected Multilevel Multiscale view capabilities. Users should have the possibility to select

a full spectrum of views at different levels of resolution, scales and abstraction. Multiple views should be

visualized in order not to lose connections among different levels of abstraction, resolution and scales.

The zooming function should allow users to transition from a levels of abstraction, resolution and scales

in an interactive way.

This kind of “Maps” gives a comprehensive picture of the:

– “Architectural and Structural Elements” which constitute a “system” and related interconnections: from

the System (or System of Systems) down to elementary structures (atoms/molecules, groups of atoms

and molecules)

– Analysis and Design Variables their relationships and interdependencies and links between “ Analysis

and Design Variables” and Architectural and Structural Elements

– Properties of the full set of Architectural and Structural Elements

– Performance and Requirements for the full set of Architectural and Structural Elements. Performance are

calculated and/or measured during the R&D and Engineering Process while, Requirements are imposed

by designers.


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Maps represent the Hierarchical Multiscale Multilevel Science - Engineering CAD/CAE Systems Next

Generation or to better say Hierarchical Multiscale Multilevel Computer Aided Research, Development

and Engineering (CARDE) Systems.

Architectural and Structural Maps evolve along the Technology Development and Engineering Analysis and

Design Process thanks to Analysis and Design Modules (described in the paragraph 3.5.2), globally referred

to as “Strategy Modules”. “Maps” are built using the available knowledge; as analysis and design activities

proceed, they are interactively modified. Different Maps can be linked to different Architectural

Hypotheses and Decisions for different purposes and tasks during the R&D and Engineering Process. Maps

are recorded, organized and managed in specific “Architectural and Structural Map Data Bases”.

Architectural and Structural Elements Maps are related to:

− Functional Maps

− Physics and Process Maps

Functional Maps

We define two types of Functional Maps.

− The first one, which can be called “Direct Functional Map”, describes “Functions” carried out by the

System and the full hierarchy of its Elements. Direct Functional Maps link Architectural/Structural

Elements to Functions and they describe what functions are performed by Architectural/Structural

Elements.

− The second one, which can be called “Inverse Functional Map” relates Functions to

Architectural/Structural Elements over the full spectrum of hierarchy levels

Functional Maps can be linked to:

− Architectural and Structural Maps

− Physics and Processes Maps

“Functional Maps” defined during the Technology Development and Engineering Process are recorded,

organized and managed by specific “Functional Maps Data Bases”. Maps are indexed in such a way as to

relate them to specific R&D and Engineering Processes, Phases and Tasks.

Physics and Process Maps

We use the term “Process” to indicate a cluster of elementary physical and biochemical phenomena.

Processes are, for instance, failure, stress corrosion cracking erosion, phase transformation,…… A Process

can be broken down in a full hierarchy of more elementary Processes and Phenomena. The distinction

between “processes” and “phenomena” is, to some extent, arbitrary. It is a matter of opportunity. Processes

can concern more Architectural/Structural Elements. Physics and Process Maps are linked to:

− Architectural/ Structural and Functional Maps.

− Requirement - Performance – Property –Architecture/ Structure Maps and Processing – Structure

Maps

“Physics and Process Maps” are “software environments” which describe :

� the full set of physical (biological and chemical, as needed) phenomena and processes which rule the

dynamics of Architectural/Structural Elements of a “System” under analysis/design for a specific

Task and their interactions inside a scale and over different scales.

� The full hierarchy of (geometrical, physical and biochemical) Architectural/Structural

transformations related to a specific set of Phenomena/Processes related to a specific R&D and

Engineering Task .

� Relationships between the full hierarchy of processes, phenomena and Architectural/Structural

transformations for a specific Task


16

Maps are indexed in such a way as to relate them to specific R&D and Engineering Processes, Phases and

Tasks.

Physics Maps are linked to Integration Strategy Maps described in the Paragraph 3.6.4. Integration Strategy

Maps describes what Computational Models, Experimentation, Testing and Sensing Techniques/Procedures

are applied to analyze specific physics phenomena/processes and their interconnection networks and

sequence of execution. Physics Maps are built using the available knowledge, as R&D and Engineering

proceed, they are interactively modified.

“Physics Maps” defined during the R&D and Engineering Process are recorded, organized and managed by

specific “Physics Map Data Bases”.

Integration of the previously defined Maps allow to correlate:

− architectural and structural elements to functions performed and functions to architectural and structural

elements (linking Architectural and Functional Maps)

− functions to physical phenomena and processes (linking Functional Maps with Phenomena and Processes

Maps

− functions to design hypotheses (linking Functional Maps with Architectural/Structural Maps)

− properties to phenomena and processes

“ Performance – Properties – Architecture/Structure – Processing” Maps

The definition of the Performance – Properties – Architecture/Structure – Processing relationships has

become a cornerstone of the modern Materials Science and Engineering and R&D and Engineering at all.

Prof. Gregory Olson, Northwestern University has been one of the pioneers of this strategy. Prof. Olson

described this approach in a Science article: Vol. 277 (29 August 1997) pp. 1237-1242. The following

figure illustrates the application (by Prof. Olson – Northwestern University) of the Performance – Properties

- Structure – Processing Integration Strategy to the design of new alloys.

Performance – Properties – Architecture/Structure - Processing Maps are indexed in such a way as to relate

them to specific R&D and Engineering Processes, Phases and Tasks. Performance – Properties - Processing

– Structure Maps defined during the R&D and Engineering Process for different purposes and tasks are

organized and recorded in the “Performance – Properties - Structure - Processing Map Data Bases”


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Processing – Architecture/Structure Maps This kind of software environments contribute to characterize and manage relationships between processing

and manufacturing activities and the resulting architecture/sstructures

. These Maps identify :

� defects (typology, physical and chemical characteristics, density and distribution : statistical and

deterministic analysis) linked to specific processes and manufacturing activities and steps

� bio – chemical and structural features and transformations linked to specific processes and manufacturing

conditions, procedures and technologies

Maps are indexed in such a way as to relate them to specific R&D and Engineering Processes, Phases and

Tasks. Processing – Structure Maps defined during the R&D and Engineering Process for different purposes

and tasks are organized and recorded in the “Processing - Structure Map Data Bases”


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3.3 Multiscale Science - Engineering Information Space

This concept was presented by Alessandro Formica in the Report “Fundamental R&D Trends in Academia

and Research Centres and Their Integration into Industrial Engineering” (September 2000), drafted for

European Space Agency (ESA). The “Science-Engineering Information Space” is associated to any

analytical, computational model/method, and experimental, testing and sensing procedure and technique.

The “Multiscale Science-Engineering Information Space” defines:

− what spectrum of information about physical/biological/chemical phenomena and processes

− at what level of accuracy and reliability (uncertainty level definition),

can be get by a computational model or experimental/testing/sensing technique/procedure applied in a

specific context for a specific task.

A set of “model variables” characterize analytical and computational models. A set of “method variables”

characterize the specific method applied to perform simulations. A set of “system variables” characterizes

the system to be modeled and simulated or subjected to experimental, testing and sensing analyses.. A set of

“experimental, testing and sensing variables” characterize experimental, testing and sensing techniques and

procedures.

The ”Science – Engineering Information Space” applies also to cluster of computational models and

experimental/testing/sensing techniques/procedures linked through multiscale multiphysics coupling

schemes. In this case we can define “coupling scheme parameters” which describe the method used to

couple models and/or experimental/testing/sensing techniques/procedures.

− With the term “system” we refer to the system (materials, device, component,….) under analysis.. A set

of “variables” describe the geometrical, biological, chemical and physical structure of the system.

− With the term “Operational Environment”, we refer to “External Fields and Loading Conditions”

− With the term “model” we refer to the mathematical/computational representation of the “system” under

investigation. A set of “variables” characterize and describe the models (boundary conditions, external

fields, space and time dimensions, discretization techniques, particles number and typology,…….). In

the proposed framework we extend the concept of “Model” to the Experimental/Testing/Sensing world

as explained in the Paragraph 3.4.2

− With the term “method” we refer to the specific deterministic and statistical analytical and computational

method (Monte Carlo. Classical Molecular Dynamics, Quantum Molecular Dynamics, Density

Functional Theory, Dislocations Dynamics, Cellular Automata,…).

− With the term “experimental/testing/sensing technique and procedure variables” we refer to the

“variables” which describe technical characteristics of the experimental and testing apparatus and the

specific operational modes and conditions (globally referred to as “procedure”)

Information Space Construction

To build the “Information Space” of a specific (single scale or multiscale) computational model with

reference to a specific system and analysis task (fracture, delamination, oxidation,…), we perform a set of

simulations, varying in a systematic way parameters/variables which are related to the specific task. Then,

we validate computational models using a set of experiments, tests and sensing measures to track the

“boundaries” of the Information Space and evaluate accuracy and fidelity. Validation procedures can also be

applied to experimentation, testing and sensing. In this case a “Cross Validation” strategy is applied which

foresee the comparison of different experimentation and testing techniques.


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The “Information Space”, should also include Multiscale Data and Physics Maps worked out during the

construction process..

It is possible to apply different schemes to build the “Information Space” for a specific task. For instance:

− fixing model and methodology variables and varying in a systematic way external conditions and/or

system variables (typology and architecture of a material or device)

− fixing external conditions and system variables and varying in a systematic way model and/or

methodology variables (for a molecular dynamics model: simulation time, force fields typology, number

of particles,…).

− any other possible combinations

Information Space Relevance

Three considerations underlie the definition of the “Multiscale Science – Engineering Information Space”

concept and method:

� rationally correlating advances for models and possible multiscale multiphysics coupling schemes with

the capability of getting information thought to be important to carry out specific R&D and Engineering

tasks.

� rationally defining the role of models and related multiscale multiphysics coupling schemes inside a

more general R&D and Engineering analysis and design process and the interdependencies among

different models, methods, techniques and coupling schemes.

� formally tracking and planning the development path (roadmap) for models, methods, techniques and

related coupling schemes as linked to specific R&D and Engineering analysis and design tasks, and

assessing the relative importance of the different models and related coupling schemes to get some

Information at a specific level of accuracy and uncertainty.

We can consider an aerodynamic design task, for instance. The ability to run a 30/50-million grid points

Navier Stokes simulation in the same lapse of time, or less, as a 1-million grid points simulation, is surely an

important result from an engineering analysis and design point of view. But, what is the relative “weight”

between model dimension and physics (turbulence) modeling as function of a particular task (calculation of

aerodynamic coefficients, for instance) at a certain level of accuracy and reliability?

In this way, can we get more reliable and accurate information instrumental to reducing cost and

development time and introduce innovative technological solutions? The answer is not so straightforward.

Turbulence plays a key role in flow dynamics phenomena of critical importance for the design of a wide

range of systems. Suppose the biggest simulation model used the same turbulence model (or a slight

modification) as the one employed in the smallest one, what is the relationship among the number of grid

points, turbulence modeling (model variables) and the capacity of getting the needed engineering

information at the right level of accuracy (for instance : CP - CL or vortex dynamics – look at the V-22 vortex

ring state story ) ? Is the number of grid points or the turbulence modeling the dominant knowledge factor

from a designer point of view?

The situation becomes even more critical when the physics and chemistry to be taken into account are highly

complex (aerothermodynamics and combustion, for example). It is sufficient to think at a combustion

chamber or an hypersonic vehicle. Several variables such as complex thermo chemical phenomena, the

interaction between turbulence and chemistry, multiphase and phase change phenomena, condition the

information space linked to a model.

We introduce, now, the “Range of Validity” concept for the “Multiscale Science-Engineering Information

Spaces” associated to models and experimental, testing and sensing techniques and procedures.


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“Range of Validity” is the range of the “Multiscale Science-Engineering Information Space” inside which

we can get a set of information from specific models and experimental and testing procedures/techniques and

possible coupling schemes at a certain level of accuracy and reliability.

It is of fundamental relevance to determine how the “Range of Validity” changes as model, method,

experimental & testing and coupling scheme variables change. The “range of validity” is a key element to

determine (for a specific task) :

� how “good” computational models and experimental, testing and sensing techniques and coupling

schemes are and

� how to define the right mix of computational models/methods and experimental & testing

procedures/techniques and coupling schemes to get what we think to be the right information at the right

level of accuracy and uncertainty to perform a specific R&D and Engineering analysis and design task..

The importance to define in a formal way the “Range of Validity (or Applicability Domain)” of a model is

highlighted in the following figure (Center for Computational Materials Design – NSF)

The “Multiscale Science-Engineering Information Space” formalizes what, today, is being performed in an

empirical and semi-empirical way. Such a formal procedure allows us to rigorously evaluate the relative

weight of the several “variables” as function of the “Information Space” and the best research/development

paths for computational models/methods and experimental & testing techniques to address specific

challenges. The “Multiscale Science-Engineering Information Space” concept and method enables

researchers and designers to jointly define development roadmaps for computational models and

experimental & testing techniques. The need to define the “Information Space” associated to computational

method and experimental techniques, in the context of the Verification & Validation process, has been

analyzed, for instance, by Tim Trucano in “Uncertainty in Verification and Validation: Recent Perspective

Optimization and Uncertainty Estimation, Sandia National Laboratories Albuquerque, NM 87185-0370

SIAM Conference on Computational Science and Engineering, February 12-15, 2005, Orlando, Florida -

SAND2005-0945C”. The following figure is drawn from this document:


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Thanks to the “Multiscale Science – Engineering Information Space” concept and method, it is possible to

define “Costs/Benefits Function” for models and related coupling schemes as referred to different

Technology Development and Engineering tasks. “Benefits” are referred to the Information get and “Costs”

to the resources needed to develop, validate and apply models/methods/techniques/coupling schemes. This

kind of Function could be useful to Technology Development and Engineering Project Managers to better

manage and allocate human, organizational and financial resources. The “Multiscale Science-Engineering

Information Space” is of fundamental importance to define and implement “Methodologically Integrated

Multiscale Science-Engineering Strategies” which foresee the simultaneous use of several different single

and multiscale computational models and methods, and several different single and multiscale experimental

techniques working over a full range of scales. According to the previous analysis, the “Multiscale Science-

Engineering Information Space” concept and method is instrumental to identify:

� shortcomings and limitations of computational models and related multiscale multiphysics coupling

schemes for specific R&D and Engineering tasks

� development lines (roadmaps) for computational models and methods and multiscale coupling schemes

to achieve specific R&D and Engineering objectives

� development lines (roadmaps) for experimental, testing and sensing techniques and procedures and

related multiscale multiphysics coupling schemes

� integrated roadmaps for jointly developing multiscale multiphysics analytical, computational and

(multiscale) experimental & testing techniques to deal with specific R&D and Engineering Tasks

� integrated strategies for jointly applying multiphysics multiscale analytical, computational and

(multiscale) experimental & testing techniques to deal with specific R&D and Engineering Tasks


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3.4 Modeling and Simulation as Knowledge Integrators and Multipliers and

Unifying Paradigm for Scientific and Engineering Methodologies

3.4.1 The New Computational Modeling Vision The “Vision” of “Modeling & Simulation” as “Knowledge Integrators and Multipliers” (KIM) and

“Unifying Paradigm” for Scientific and Engineering (Experimentation, Testing and Sensing) Methodologies

characterizes the “Integrated Multiscale Science-Engineering Framework” and it represents the conceptual

context inside which the Framework is applied to R&D and Engineering Processes. The KIM notion was

presented by Alessandro Formica in the: “HPC and the Progress of Technology : Hopes, Hype, and Reality”

– RCI. Ltd Management White Paper – February 1995

“Multiscale Multiphysics Modeling and Simulation” can be regarded as “Knowledge Integrators and

Multipliers” (KIM) and “Unifying Paradigm” for Scientific and Engineering Methodologies because they

are able to integrate and synthesize, in a coherent framework, Data, information, and Knowledge from:

�� a number of different disciplines,

�� a wide range of scientific and engineering time and space domains,

�� multiple scientific and engineering models (science-engineering integration) linked by a spectrum of

coupling schemes.

�� Multiscale Science – Engineering Information Spaces

�� Maps generated by a wide range of methodologies (analytical theories, experimentation, testing and

sensing)

In this vision, we propose to extend the concept of “Model” to include not only its mathematical formulation,

but, also, Information Spaces and Maps linked to it for specific tasks. Computational Model Information

Spaces and Maps embody and organize Information and Knowledge get by the full spectrum of analytical

theories, computational models of different accuracy and resolution levels, experiments, tests and sensing

measures used to develop and validate Models. It is to be highlighted that all the existing Modeling and

Simulation concepts, application strategies and methodologies, such as “Virtual Prototyping” , “Simulation -

Based Design”, “Simulation - Based Acquisition”, Simulation Based Engineering Science (SBES) and

“Virtual Engineering”, can be considered as particular cases of this more general concept and strategy. A

The concept of “Model” as “Knowledge Integrator” is certainly not new. This view, in the mid of nineties,

was clearly described in the chemical engineering field by James H. Krieger, in the article “Process

Simulation Seen As Pivotal In Corporate Information Flow” - Chemical & Engineering News, March 27,

1995, reported the following statement of Irving G. Snyder Jr., director of process technology development,

Dow Chemical : "The model integrates the organization. It is the vehicle that conveys knowledge from

research all the way up to the business team, and it becomes a tool for the business to explore different

opportunities and to convey the resulting needs to manufacturing, engineering, and research." . In the same

article other companies such as BNFL and Du Pont expressed similar points of view.

Note: Continuous advances in computational modeling and computing power makes it possible to build

computational models which simulate the experimental or testing apparatus, the system to be probed and

related interactions. This kind of modeling is an interesting asset to plan experimentation, testing and

sensing and analyze results.

key element of the KIM Vision is the extension of the concept of “Model” to the Experimental, Testing and

Sensing World as detailed in the next page:


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The Concept of Experimental, Testing and Sensing “Model”

In the proposed theoretical and methodological framework it is necessary to extend the concept of

“Model” from the Computational to the Experimental, Testing and Sensing World. In the context of the

Experimental and Testing World, for “Model”, as referred to a specific Experiment or Test carried out

with a specific experiment working in a specific operational mode and probing a specific system for a

specific tasks, we mean an “Information and Knowledge Structure” that define:

− Characteristics (structure, composition, initial dynamics state, boundary conditions, external loadings)

of the System to be probed

− Characteristics of the equipment in terms of resolution, scale, physical and biochemical phenomena

which can be probed

− Characteristics of the specific Experimental, Testing and Sensing operational conditions and modes

applied for specific R&D and Engineering Tasks

− The “Multiscale Science – Engineering Information Space” related to it

− Multiscale Data and Physics Maps .

As in the Computational World, it is easy to define the concept of “Multiscale Experimental, Testing and

Sensing Model”. In this case the “Information/Knowledge” Structure refers to a cluster of different

equipments and it embodies information about:

− Interaction schemes and modes among the different equipments

− Integration schemes

− Data and Information Flow among the different equipments


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3.4.2 Computation and Experimentation/Testing/Sensing Integration Even if attention to integration is positively increasing, particularly for models development and validation

phases, there are still conceptual and methodological relationships not thoroughly examined between

challenges and advances in modeling and simulation, and progress and challenges in experimental, testing

and sensing techniques. Experience is showing us that ever more complex and large scale computations call

for increasingly sophisticated and expensive experimental techniques both in the model development and

validation phases. Advances in modeling and simulation are intimately linked to progress in experimental

methods and techniques and vice versa. A direct correlation and strong mutual dependencies, both in the

model development and validation phases, exist between the two fields sometimes regarded as antithetic. It

is important to take into account that, if computational methods and computing technologies are continuously

progressing, also experimental, testing and sensing techniques are making continuous significant progress.

It is sufficient to think at the impact on materials research that the Scanning Tunneling Microscopy (STM)

and Atomic Force Microcopy (AFM) techniques have had. It is important to highlight that Computational

Development Strategies should be jointly conceived with Experimentation, Testing and Sensing

Development Strategies and vice versa. That is very seldom carried out. A priority target is to develop a

unified conceptual context to synergistically take advantage of advances in both the fields and not only for

the computational models development phase, as it occurs today, but, also, in the application phase.

An effective R&D and Engineering Strategy should find the way to synergistically take advantage of

advances in both the fields.

In many cases, today, advanced HPC/Modeling/Simulation and experimental/testing/sensing programs are

conceived and managed, if not as antithetic entities, surely, as separated realities. This situation can lead to

costs increase and hamper and limit the effectiveness of both the programs. The new Vision reconcile

development streams and roadmaps in the two fields.

In the R&D and Engineering Process, today and, more and more, in the future, we have to integrate a full

spectrum of (interdependent and interlinked) scientific and engineering models and codes with a full

spectrum (tsunami) of experimental, testing and sensing (scientific and engineering) data with a full

spectrum of scientific and engineering analytical formulations. Data get from experimentation, testing and

sensing covers several physical and biochemical disciplines and domains and several different space and

time scales. It is clear that, increasingly, we have to deal with very complex interaction patterns “intra” the

experimentation, testing and sensing world, “intra” the computational modeling world and “inter” the

experimentation, testing, sensing and computational modeling worlds. “Multiscale Science – Engineering

Information Spaces and Multiscale Maps are key elements to realize this integration.

Experimental, Testing and also Sensing Data Networks have to be analyzed, interpreted and correlated

according to a unified vision which can be offered by new Modeling Strategies and Frameworks.

The KIM concept is a fundamental theoretical and methodological basis Methodologically Integrated

Multiscale Science - Engineering Strategies are built upon. Three key objectives characterize the KIM

concept:

�� putting on different bases relationships between Modeling and Simulation, from one side, and

Experimentation, Testing and Sensing, from the other side.

� easing the development of multiscale multidisciplinary experimental, testing and sensing technologies

and strategies which fully integrate a spectrum of experimental, testing and sensing techniques and

related application procedures

� shaping a full integration of multiscale modeling and simulation with multiscale experimentation, testing

and sensing to define “Methodologically Integrated Multiscale Science - Engineering” Strategies.


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Modeling & Simulation Application strategies in the innovative technology development field are

significantly hampered and limited the following fundamental contradiction:

“when we develop innovative technologies, we often (practically always) enter a territory where theories

are not well developed and reliable, and the availability of experimental and testing data is fragmented or

lacking at all. Accordingly, we face a fundamental and intrinsic problem: Modeling & Simulation is the

reference strategy to limit risks, costs, and development times by heavily reducing the resort to complex and

expensive experimental and testing activities. However, contrary to what happens in the mature or

evolutionary technology environment, we cannot adopt this strategy because we still need very significant

experimental and testing activities to develop and validate the needed computational models.”

That is what is called a classical “Catch 22” situation (i.e.) a situation which involves intrinsic

contradictions.”

This contradiction is certainly not ignored. In the presentation “Modeling and Simulation in the F-22

Program” held on 3 June 98, Bgen Michael Mushala, F-22 System Program Director, highlighted this issue.

We quote his exact words :

A Catch 22 :

>> Increased Reliance on Simulation Requires High Confidence in the Modeling

>> High Confidence in the Modeling Requires High Quality Flight Test Data

How to get out of this contradiction? We think that single scale and independent computational and

experimentation & testing science and engineering strategies are not up to the challenge. Key elements of

the new Vision of Modeling and Simulation are:

� Multiscale Maps

� the “Multiscale Science – Engineering Information Space” concept. which enables the definition in a

formal way of what kind of information at what level of accuracy and reliability can be get by single

and multiscale computational, experimental, testing and sensing models and techniques.

� A new concept of computational model which include not only mathematical and physical (chemical

and biochemical, as needed) formulations, but, also, Data, Information and Knowledge (Multiscale

Maps) linked to it when applied to a specific task

� The extension of the “model” concept to the experimental, testing and sensing world

� Definition of the “Applicability Conditions” and “Predictability Criteria” for (single and multiscale)

Computational models which guide the application of Modeling and Simulation and their

integration with experimentation, testing and sensing (Methodologically Integrated Multiscale

Application Strategies) [Paragraph 3.4.3]


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3.4.3 Extension of the Multiscale Approach to Experimental and Testing World

The following issues are motivating the birth and they are driving the development of the Experimental

Multiscale and Testing fields:

� Just the continuous development of Computational Multiscale has put the basis and established the need

to extend, in a systematic way, the Multiscale concept and Method to the experimental and testing

fields.

� The development and validation of ever more complex multiscale computational models and methods

increasingly call for the integration of data, information and knowledge from a wide range of

experimental and testing equipments working over an extended spectrum of space and time scales and

physical domains. We can state that a direct relationship between the Computational and Experimental

and Testing Multiscale World exists. Advances in Multiscale Computational Models and Methods is

directly linked to advances in experimental and testing multiscale.

� Hierarchical Materials and Devices and Complex Systems made up of a Wide spectrum of sub-systems,

components, devices call for Multiscale Integrated Experimental and Testing Equipments to get an in-

depth and Comprehensive understanding that, in several cases cannot be given only by computational

models

� The behaviour of Materials, Devices and Systems inside widening operational envelopes and related

requirements for “Extreme Performance” levels (Extreme Engineering) is critically dependent upon a

full spectrum of multiscale physical and biochemical phenomena. In this context, the classical approach

to Life Cycle issues (damage, fracture, properties degradation, corrosion, failure..) is increasingly

showing specific limits. This situation makes a science – based (multiscale) experimental, testing and

sensing approach a specific target for Technology Development and Engineering.

� New and more powerful experimental, testing and sensing equipments are continuously developed.

Technology advances allow, today, to design experimental, testing and sensing equipments with inherent

capabilities to probe systems over an extended range of scales (Free Electron Laser, X Ray Synchrotron

are two examples of this trend). Advances in wireless and wired sensor network and the Integration of

Distributed Processing and Sensing put the bases to the design of a new Generation of Multiscale Sensor

Networks.

For “Experimental/Testing/Sensing Multiscale” we mean:

�� Single Experimental/Testing/Sensing Equipments able to probe “Systems” (also using different

operational modes) over a range of space and time scales.

�� Integration of multiple experimental/testing/sensing equipments. Integration can occur off-line. In this

case data get from a set of experimental/testing equipments is integrated to give a comprehensive picture

of phenomena/processes. Multiscale Maps can represent an interesting tool to accomplish this task.

Integration can also be implemented over a Cyberinfrastructure. In this case the set of

experimental/testing/sensing equipments is connected through a network. A key problem is

synchronization.


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An important recognition of the key strategic relevance of the development of multiscale experimental

techniques and their integration with multiscale computational modeling comes from the article “Three-

Dimensional Materials Science: An Intersection of Three-Dimensional Reconstructions and Simulations

(Katsuyo Thornton and Henning Friis Poulsen, Guest Editors), published in the Materials Research Society

(MRS) Bulletin June 2008.

“..For example, by combining a nondestructive experimental technique such as 3D x-ray imaging on a

coarse scale, FIB-based 3D reconstruction on a finer scale, and 3D atom probe microscopy at an even finer

scale, one has an opportunity to capture materials phenomena over six orders of magnitude in length scale.

This will bring materials researchers closer to the ultimate dream of a direct validation of multiscale

models, both component by component and ultimately as an integrated simulation tool. In conjunction with

the advances on the modeling side, such comprehensive experimental information is seen as very promising

for establishing a new generation of models in materials science based on first principles…..”

Examples of Multiscale Experimental and Testing Techniques and Systems

� Synchrotron Radiation from European Synchrotron Radiation Facility has been already applied

several times in a multiscale mode. Scientists from Max Planck Institute (Germany) and the ESRF

discovered the way deformation at the nanoscale takes place in bones by studying it with synchrotron

X-rays.. A bone is made up of two different elements: half of it is a stretchable fibrous protein called

collagen and the other half is a brittle mineral phase called apatite.. In order to understand how this

construction is achieved and functions, scientists from the Max Planck Institute of Colloids and

Interfaces in Potsdam (Germany)ESRF. Used X-rays from ESRF to see for the first time the

simultaneous re-arrangement of organic and inorganic components at a micro and nanoscale level

under tensile stress. Scientists carried out experiments on ID2 beam line at the ESRF. They tracked

the molecular and supramolecular rearrangements in bone while they applied stress using the

techniques of X-ray scattering and diffraction in real time. The high brilliance of the X-ray source

enabled the tracking of bone deformation in real time. Researchers looked at two length scales: on

one side they observed the 100 nanometers sized fibres, and on the other, the crystallites embedded

inside the fibre, which are not bigger than 2 to 4 nanometers. This Multiscale approach is relevant for

the whole Biomaterials field.

� ESRF was also involved, jointly with the Laboratoire de Physique et Mécanique des Matériaux - FRE

CNRS 3236, the Institut Laue-Langevin and the Institute of Physics of the ASCR, v.v.i. Laboratory of

Metals – Praha – Czech Republic, In the Multiscale analysis by neutron and synchrotron X-ray

diffraction of the mechanically-induced martensitic transformation of a CuAlBe shape memory alloy.

The objectives were to determine stresses and orientations evolutions from macro to micro scale

during a stress-induced martensitic transformation

� MUSTER (Multi-scale Testing and Evaluation Research) Facility Institute of Advanced Energy,

Kyoto University The facility covers from atomic to real application scale on material performances

and structural / chemical / physical features and it is dedicated to R&D of innovative and advanced

energy materials. Multi-scale testing and evaluation is considered as critical for R&D of advanced

materials. Structural, chemical, physical and mechanical features of the advanced materials are

studied atomic through real application scale.

� W. M. Keck Laboratory for Combinatorial Nanosynthesis and Multiscale Characterization at

University of Maryland has built an experimental system which consists of a scanning electron

microscope, atomic force microscope, and scanning tunnelling microscope combined in one state-of-

the-art instrument (JEOL JSPM-4500A). This instrument allows for multi-scale microscopy at

variable temperatures and proximal probe measurements of devices, growth structures and attendant

fields


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Strategic Research and Development Agenda for Multiscale Experimentation and Testing:

� Development of new Experimental and Testing Systems which are, inherently, Multiscale

Multiresolution, i.e. able to operate over different scales and or at different resolution levels inside a

scale.

� New Techniques to analyze Data from a spectrum of Single and Multi Scale Experimental and

Testing Systems

� The development of new Strategies and related Frameworks to “rationally” integrate a wide range

of Single and Multi Scale Experimental and Testing Fields for specific R&D and Engineering Tasks

� Development of new “two – way” Strategies and related Frameworks to integrate a wide range of

Single and Multi Scale Experimental and Testing Equipments for specific Technology Development

and Engineering Tasks

� The design of new Integrated Schemes and related Frameworks to realize a comprehensive two-way

integration between Multiscale Computational and Multiscale Experimental and Testing

Methodologies, Strategies and Environments to define really “Integrated Multiscale R&D and

Engineering” Strategies and Frameworks.


29

3.4.4 Methodologically Integrated Multiscale Science – Engineering Strategies

The “Multiscale Science – Engineering Information Space” and the “Information – Driven” concept

(described in the paragraph 3.5.1) allow us to define new “Applicability Conditions” and “Predictability

Criteria” for Computational Models which shape “Application Strategies” for Modeling and Simulation and

their integration with related “Experimentation, Testing and Sensing Application Strategies” The final goal

is the development of “Methodologically Integrated Multiscale Science - Engineering Strategies” which

represent a very important element of the New Framework here described.

The definition of “Applicability Conditions” and related “Predictability Criteria” for computational models

implies the ability of establishing specific rules and schemes that allow researchers, designers, and planners

to evaluate, with a high degree of reliability, where, when, and to what extent, it is possible to safely

(quantifying in probabilistic terms risks and uncertainties) substitute modeling & simulation for

experimentation and testing and where, when, how and to what extent we need to integrate modeling with

experimentation, testing and sensing for specific tasks.

Applicability Conditions. Two basic conditions which rule the development and the implementation of

highly predictive models and their integration with experimental and testing techniques can be defined:

� researchers and engineers are able to formulate hypotheses about what Information is needed to

accomplish a R&D and Engineering task:

� what physical length scales and phenomena/processes and relationships are important for specific

R&D and Engineering tasks and purposes.

� at what level of accuracy phenomena/processes should be modeled and simulated

� researchers and engineers are able to define the range of validity of the models and, inside this range,

the degree of and reliability of models [Multiscale Science – Engineering Information Space].

Applicability Conditions can be applied to the Experimental, Testing and Sensing Fields. A derailed

comparison of the “Information” which can be get bythe respective analyses with the “Information” we

think it is needed to accomplish a specific Task is an important element to shape “Methodologically

Integrated” Strategies

Predictability Criteria

When we discuss about predictive capabilities of models in the R&D and Engineering context, we should

carefully take into account two critical issues: predictive consequence and confidence.

�� Predictive Consequence: what is the impact of errors and uncertainties for specific tasks? Errors and

uncertainties can be fairly large but their impact can be low. On the contrary, errors and uncertainties can

be limited but their impact can be very large.

�� Predictive Confidence: how to assess models uncertainty and evaluate the level of confidence in models?

[Multiscale Science – Engineering Information Space and Validation methods]

Application Conditions and Predictability Criteria are important “Guiding Principles” to define Multiscale

Modeling and Simulation Application Strategies and to shape “Methodologically Integrated Multiscale

Science – Engineering Strategies”.

The final objective is to define “Integration Strategy Maps” which describe:

� What single and multi scale computational models and what single and multi scale experiments, tests and

sensing measures have been selected to deal with a specific task

� What is the order of execution and the overall Integration Scheme as shaped by the “Applicability

Conditions” (Multilevel Network of Computational, Experimental, Testing and Sensing Models and

Techniques)

� What is the flow of input and output data and information among the full spectrum of models and

experiments/tests/sensing techniques.


30

Several hypotheses can be taken into account and interactively changed

For each specific task, “Integration Strategy Maps” describe:

� The full set of Computational, Experimental, Testing and Sensing Models/Techniques applied to deal

with specific task

� The order of execution and Integration Scheme: Multilevel Network of Multiscale Computational,

Experimental, Testing and Sensing Models and Techniques. For each Model

� Multiscale Science – Engineering Information Spaces

� Input and Output Data and Information Flow

� Multiscale Maps

“Integration Strategy Maps” defined during the R&D and Engineering Process are recorded, organized and

managed by a specific Integration Strategy Maps Data Base

The following figure (US Department of Energy (DoE) Fusion Materials program : Aspects of Multiscale

Modeling Primary Damage and Rate Theory Models Presentation – R. E. Stoller – Metals and Ceramics

Division Oak Ridge National Laboratory, is a representation of a possible (simplified version) combination

of the proposed Multiscale Map of Physics and Multiscale “Models Integration Strategy Map” of

Computational Models and Experiments & Tests


31

3.4.5 Multiscale Knowledge – Based Virtual Prototyping and Testing In the new conceptual and methodological context, classical “Virtual Prototyping” concept should be

complemented by a new concept which can be referred to as “Multiscale Knowledge – Based Virtual

Prototyping”. Classical concepts can be regarded as a particular case of this more general concept and

strategy. Classical “Virtual Prototyping” approach is applied when “Applicability Conditions” can be met

and “Predictability Criteria” can be reliably evaluated also thanks to the “Knowledge” gained with

Multiscale Maps and “Multiscale Science – Engineering” Information Space” concept and method. For this

reason we can add the term “Knowledge – Based”.

The previously described theoretical and methodological apparatus allows us to formulate rational

hypotheses about what experiments, tests and sensing measures are really needed to get the information we

think to be necessary to characterize the behaviour of a “System” at a predefined level of accuracy and

reliability, and, accordingly, assess the “risk” associated to replace experimentation, testing and sensing

with computation for a specific task. This a fundamental condition to replace in a “rational” way testing with

computation. In this new context, we can design and plan highly complex Multiscale Multilevel Testing

Strategies guided by “Multiscale Computational Models” and related Multiscale Maps and Multiscale

Information Spaces.

Data, Information and Knowledge “flow” in a seamless and fully integrated way between the Testing and

Computational Worlds and vice versa. Not only, with the new theoretical and methodological apparatus, we

can easily integrate inside Testing Strategies even “Information Capabilities” of several Experimental and

Sensing Facilities. Multiscale Maps from Experimentation can also contribute to understand possible Testing

Anomalies and Problems. This kind of integrated analyses can, in turn, suggest new Experimental activities.

An interesting application field for methods and tools described in this document is what can be called “Multiscale Science – Engineering System Testing”. A problem is to transfer, in a structured way,

information and knowledge get from testing at a scale to the higher scales of a System along the whole chain:

from testing carried out to characterize behaviour of materials (basic constituents of any System) to testing

for devices, components, sub-systems and the global system. We should correlate Multiscale Maps for all

the scales and resolution levels. Correlation works in both the directions: bottom – up and top – down:

− Bottom – Up: Multiscale Maps built from materials testing can be a useful basis to develop upon testing

strategies for devices, Multiscale Maps built from devices testing are applied to improve testing

strategies for components and so on along the scale.

− Top – Down: results from testing at a scale can be better analyzed taking advantage of Multiscale Maps

get from testing at lower scales

The following figure, drawn from the “Validation Pyramid and the failure of the A-380 wing” Presentation

given by I. Babuska (ICES, The University of Texas at Austin), F. Nobile (MOX, Politecnico di Milano,

Italy), R. Tempone (SCS and Dep. of Mathematics’, Florida State University, Tallahassee) in the context of

the context of the Workshop “Mathematical Methods for V&V SANDIA , Albuquerque, August 14-16,

2007, shows an “Integrated Multiscale Multilevel Testing Strategy for a Complex System”: from coupons to

the full System.


32

3.5 Designing the R&D and Engineering Process

3.5.1 The Information – Driven Concept

The relevance of “Information”, as a key element to shape R&D and Engineering Strategies is winning an

increasing attention. Several studies have been performed, for instance, by Jitesh H. Panchal, Janet K. Allen,

David L. McDowell and colleagues at Georgia Institute of Technology. Alessandro Formica highlighted the

role of Information to drive modeling and simulation strategies in the White Paper “HPC and the Progress of

Technology : Hopes, Hype and Reality” published in US by RCI Ltd on February, 1995. In this document

he introduced the concept of “Engineering Information – Analysis”. The issue was also dealt with in the

context of the Accelerated Insertion of Materials (AIM) Program (1999) managed by US DARPA. The

following text is drawn from DARPA Proposer Information Pamphlet BAA 00-22 clearly describe the

theme and related challenges:

“The need for an “Information-Driven” strategy . “….There are many interrelated technical challenges

and issues that will need to be addressed in order to successfully develop new approaches for accelerated

insertion. These include, but are not limited to, the following:

The construction of the designer’s knowledge base: What information does the designer need and to what

fidelity? How does one coordinate models, simulations, and experiments to maximize information content?

What strategies does one use for design and use of models, computations, and experiments to yield useful

information? How can redundancies in the data be used to assess fidelity ? The development/use of models

and simulation: What models are required to be used and/or developed in the context of the designer

knowledge base? How can models of different time and length scales be linked to each other and to

experiments? How can the errors associated with model assumptions and calculations be quantified? How

can models be used synergistically with experimental data ?

The use of experiments: Are there new, more efficient experimental approaches that can be used to

accelerate the taking of data? How can experiments be used synergistically with models? How can legacy

data and other existing data base sources be used ?

The mathematical representation of materials: How can one develop a standardized mathematical language

to: describe fundamental materials phenomena and properties; formulate reliable, robust models and

computational strategies; bridge interfaces; and identify gaps between models, theory and experimental

materials science and engineering? How can this representation be used to develop hierarchical principles

for averaging the results of models or experiments while still capturing extremes ?……”

In the context of the “Integrated Multiscale Science – Engineering Framework”, “Information” is a key

element which, to a large extent, drives and shapes R&D and Engineering Strategies.

The term “Information – Driven” means that R&D and Engineering strategies have to address what can be

called “The Information Challenge for R&D and Engineering” :

– What information at what level of accuracy and reliability is needed to accomplish a task

– What Relationships and Interdependencies between analysis and design variables should be tracked over

a full range (as needed) of space and time scales to accomplish a task

– What kind of information sources (analytical, computational, experimental & testing models/techniques)

are needed and how they can be combined to get the previously identified information


33

Accordingly, the following key issues define the “The Information - Driven Analysis Scheme for R&D

and Engineering”

�� What Information at what level of accuracy and reliability is thought to be needed to accomplish a R&D

and Engineering task . “Thought to be needed” means that the process is iterative, we start with some

hypotheses and just Multiscale Science Engineering Strategies and related Data, information and

Knowledge Analysis schemes and tools give us the possibility to improve evaluation about the

Information needed to execute the task. Example : What Information (what physical and chemical

phenomena and processes related to materials, structures and chemically reacting flow and their

interactions) at what level of accuracy and uncertainty should we know to analyze the dynamics of a

Thermal Protection Systems of an Hypersonic Vehicle for a specific operational environment?

� What physical length scales and related physical and biochemical phenomena rule the dynamics of the

“system” under analysis, what is the relative weight, what are relationships and interdependencies

between phenomena and processes inside a scale and between different scales (to be described thanks to

Multiscale Maps).

� What Information at what level of accuracy and uncertainty can existing analytical, computational

models experimental, testing and sensing techniques and related coupling scheme give us (to be

described using the “Multiscale Science – Engineering Information Space”).

�� How good analytical and computational models, experimental, testing and sensing techniques and related

coupling schemes should be to get the previously identified information thought to be needed to

accomplish a task. How “good” means evaluating how much “physical realism” should be incorporated

into the models and what scales hierarchy has to be taken into account. Not in all the cases, of course,

we really need complex multiscale methodologies going down to the Schrödinger equations: simple

single scale models can be accurate and reliable enough.

Note: This kind of Information is critical to evaluate what new analytical and computational models and

what new experimental and testing procedures/techniques should be developed and integrated to deal

with a specific analysis task. It is absolutely fundamental to identify not only what we know, but, in

particular, what we do not know, what we should know, how we should know it (what combination of

scientific and engineering methodologies and technologies should be needed). In this context is the

“lack of Knowledge” to guide Strategies.

�� What is the right combination and the right sequence of application (Integration Strategy: Designing the

Analysis and Design Processes) of single and multi scale analytical and computational, models/methods

and single and multi scale experimental & testing procedures/techniques to get Information thought to be

needed to accomplish a specific analysis/design task. A critical step for the “Rational Design” of R&D

and Engineering Processes is a proper selection, integration, and sequencing of computational and

analytical models and experimental/testing/sensing techniques and procedures with varying degrees of

complexity and resolution to deal with a specific “Task”. To do that we have to define the “Multiscale

Science-Engineering Information Space” associated to computational models and experimental and

testing procedure/technique and related coupling scheme. Application Strategies defined in the Paragraph

3.4.3, and Integration Strategy Maps guide the Integration Strategy.

� Furthermore, another very critical issue is that we need a rational approach to link advances in the

different methods at the different scales with the new information we need to meet challenges in the

different tasks in the different stages of the R&D and engineering process. How do we effectively and

timely evaluate the impact of scientific methodological and information advances at an atomic,

molecular, and grain (for materials) level on new technological and engineering solutions if we do not

have conceptual and methodological (multiscale) frameworks to link methods and information at the

different scales: from atomic to continuum? The “Science-Engineering Information Space” and the

“Multiscale Scientific and Engineering Information Analysis” concepts and methods can represent a first

step to deal with these critical issues. If we like to shape new cooperative schemes between industry,

from one side, and academia and research, from the other side, we have to define specific methodologies

to evaluate the “industrial and technological value” of new scientific methodological advances.


34

3.5.2 The R&D and Engineering Process Analysis and Design Architecture

A first step to “Design the R&D and Engineering Process” is to identify the “elements” which characterize

its structure and track relationships and interdependencies:

� R&D and Engineering Process Analysis and Design Basic Constituent Elements Multilevel Networks of Phases (Temporal Period inside which specific activities are accomplished)

and Analysis and Design Modules and Tasks. Any R&D and Engineering Process can be

subdivided in a Multilevel Network of Phases, In turn any Phase subdivided into a Multilevel

Network of Analysis and Design Modules and Tasks . At the highest level a Phase can corresponds

to a specific R&D and Engineering Project.

.

� R&D and Engineering Process Analysis and Design Basic Information Structures and Sources

− Library of Computational, Analytical Codes, Experimental and Testing Equipments

− Data Bases

− Computational, Analytical, Experimental and Testing Models

− Analysis and Design variables (projected over the full set of levels and scales)

− Multilevel Multiscale Relationships between Requirements/Performance –

Architecture/Structures Properties – Physics

− Multilevel Network of Relationships between Analysis and Design Variables

− Multilevel (Multilevel) Multiscale Network of Physical Phenomena and Processes

�� R&D and Engineering Process Performing Entities

All the activities needed to achieve objectives inside each R&D and Engineering Phase are accomplished

by a Multilevel Network of “Physical Entities” and “Human Entities:

Human and Management Entities:

− Organizations/Institutions

− Teams

Physical Entities

− Theoretical, Computational, Experimental, Testing, Sensing Centers and Facilities,

Cyberinfrastructural Frameworks

The Multilevel Network of Entities defines what can be called a “Collaboratory Framework”

�� Architectural/Structural System Architecture (detailed over the full set of levels/scales)

− Multilevel Multiscale Network of Architectural/Structural Elements: Systems (in case of System of

Systems) – Sub – Systems – Components – Devices – Structures (Materials, Fluids, Plasmas)

�� R&D and Engineering Objectives (projected over the full set of System Architectural/Structural

Elements at all the levels and scales)

− Requirement

− Performance

− Properties

− Functions

− Requirement/Performance – Structure – Property Relationships

− Structure – Processing Relationships

− Architectural/Structural Element – Function Relationships


35

R&D and Engineering Process Analysis and Design Process Strategy

The Strategy to carry out a generic “R&D and Engineering Project” is described by a multilevel network of

R&D and Engineering Strategy Modules and Hypothesis Modules. Data and Information collected by the

full spectrum of Maps and Modules are managed by a specific Multiscale Science – Engineering Maps and

Modules Management System.

Hypothesis and Decision Modules

The “Hypotheses Module” tracks, for a specific R&D and Engineering Project, three Hypothesis/Decision

classes and related relationships and interdependencies:

�� Hypotheses/Decisions related to possible set of System Requirements/Performance and Functions

�� Hypotheses/Decisions related to a possible set of System Architectural/ Structural tentative solutions

�� Hypotheses/Decisions related to a set of possible Analysis and Design Strategies

The full set of Hypotheses/Decisions linked to specific R&D and Engineering Project and related

relationships is described by a Multilevel Network of Hypothesis/decisions Modules and Maps

Each Hypothesis linked to a specific set of System Requirements/Performance and Functions can be related

to a set of System Architectural/Structural tentative solutions, in turn, each Hypothesis related to a specific

Architectural/Structural solution can be related to a set of possible Analysis and Design Strategies which are

described by “R&D and Engineering Strategy Modules.

The Grid of Relationships is described by a specific “Hypothesis/Decision Maps”

All the Information related to the full set of Hypotheses taken into account inside a specific R&D and

Engineering Project (and related relationships) is managed by a “Hypotheses Data Base Management

System”

We introduce the concept of “R&D and Engineering Strategy Modules” which shape the “Architecture” of

a R&D and Engineering Process. Two classes of “R&D and Engineering Modules” are defined:

− R&D and Engineering Design Modules

− R&D and Engineering Analysis Modules

R&D and Engineering Design Modules

Research, Development and Engineering Design Modules represent the overall architecture of any Research,

Technology Development and Engineering Process. Any R&D and Engineering Process can be describe by

a Multilevel Network of R&D and Engineering Design Modules. Any Module can be broken down in a

multilevel network of R&D and Engineering Tasks

R&D and Engineering Design Modules describe:

�� The full set and hierarchy of R&D and Engineering Design Modules and Tasks linked to them

�� The full set of Architectural/Structural and Functional Maps linked to them

�� R&D and Engineering Design Variables, Objectives and Analysis – Design Variable relationships

�� The full set of Analysis Modules linked to them

R&D and Engineering Design Modules are recorded and managed in a specific “R&D and Engineering

Design Modules Data Base”


36

R&D and Engineering Analysis Modules

Analysis Modules are organized in a hierarchical and recursive way (Multilevel Network). An Analysis

Modules of high level can embody “Analysis Modules” of lower level. in a recursive way “Analysis

Modules” can embody a multilevel network of “Analysis Tasks”.

At the lowest level, “Analysis Tasks” define what Analysis Strategies (multilevel network of analytical

formulations, computational models and experimental/testing Models/Techniques) are applied to achieve

Analysis Objectives. “Integration Strategy Maps”, described in the Paragraph 3.6, are defined to describe

these strategies. Analysis Modules and Tasks are linked to Design Modules and Tasks

It is possible to develop Analysis Modules tailored for specific issues and tasks such as durability or

producibility of composites or metallic or hybrid materials or structures, or the dynamical analysis of a sub-

system, a component or a device. Analysis Modules track and organize Data, Information and Knowledge

inside and between the different tasks in the different phases of the Technology Development and

Engineering process and, for each task, correlate data and information with information sources

(experiments, tests, computations, analytical formulations)

R&D and Engineering Analysis Modules and Tasks are recorded and managed in a specific “R&D and

Engineering Analysis Modules and Tasks Data Base”

Computational Code Libraries

Libraries are software environments which allow to catalogue and manage a whole set of :

�� Computational codes which implement different methods [Molecular Dynamics, Coarse Grained MD,

Monte Carlo, Density Functional Theory, Phase Fields, Dislocation Dynamics, Continuum Finite

Elements,….]

For each Computational Code, Libraries describe:

�� Characteristics, applicability conditions and what kind of information can be get from them for specific

application domains and conditions.

�� Single and Multi-scale (space and time) Multiphysics coupling methods and schemes.

Experimental, Testing and Sensing Technique/Equipment Libraries

Libraries are software environments which allow to catalogue and describe:

�� Experimental, Testing and Sensing Techniques (STM, AFM, TEM, SEM,…) and related specific

application methods which implement them

For each Experimental, Testing and Sensing Technique/Equipment, Libraries detail:

� Characteristics, applicability conditions and what kind of information can be get from them for specific

application domains and conditions

� Single and Multi-scale (space and time) coupling methods and schemes.


37

3.5. 3 Multiscale R&D and Engineering Information – Driven Strategies

Two interrelated Phases characterize the “Designing of the R&D and Engineering Process” Strategy

Multiscale Information Analysis (linking information with R&D and engineering tasks)

�� identify the critical (scientific and engineering) information for the full spectrum tasks of the R&D

and Engineering analysis process [Multiscale Science-Engineering Information-driven Strategies]

�� define the related levels of resolution, accuracy, and reliability (uncertainties management) for the

previously identified information [Multiscale Science-Engineering Information-driven Strategies]

� correlate phenomena occurring at different time and space scales (from continuum down to atomic

scales, as needed) and elucidate the related relationships. This step enables researchers and engineers to

correlate science and engineering issues inside a really unified vision [Multiscale Maps]

� Analyze Information and turning raw data and Information into Knowledge [Multiscale Maps]

These steps are fundamental to achieve a comprehensive overall picture of the information which

characterize and condition technology development and advanced engineering and assess when, where, to

what extent, and if multiscale is really needed.

Multiscale Information Flow Analysis (Linking information with information sources and define the

overall Integration Strategy)

� correlate information and information sources (experiments, tests, sensing measures, computations,

analytical formulations) identify the most critical methodological (analytical theories, modeling &

simulation, experimental & testing methods and techniques) shortcomings and related development

paths [Multiscale Science – Engineering Information Space]

� identify the relationships and the interdependencies among the several information sources at different

scales (experiments, tests, sensor measures, computations, analytical formulations) to define R&D and

engineering strategies [Multiscale Information Driven Strategy, Multiscale Science – Engineering

Information Space, Integration Strategy Maps]

� analyze the overall information flow pattern inside the spectrum R&D and Engineering phases and for

the full spectrum of multilevel network of tasks [ “Multiscale Design and Analysis Modules]

�� Define Integrated R&D and Engineering Strategies [what is the more effective mix of Information

Sources (Multiscale Science-Engineering analytical, computational and experimental, testing, sensing

models/methods/techniques)] [Integration Strategy Maps and Methodologically Integrated

Multiscale Science-Engineering Strategies]


38

3.6 “Integrated Multiscale Science – Engineering Analysis Strategies”

“Integrated Multiscale Science – Engineering Analysis Strategies”: are the last component of the “Integrated

Multiscale Science – Engineering Framework” and they synthesize and take advantage of all the previously

described concepts and methods.

“Integrated Multiscale Science – Engineering Analysis Strategies” are implemented inside the “Analysis

Modules” described in the Paragraph 3.5.2 and they are a key element to support Design Strategies embodied

in the “Design Modules”.

Integrated Multiscale Science – Engineering Strategies are absolutely general Analysis Strategies, they can

be applied to any task, in any context for any purpose in any phase of the R&D and

Engineering/Manufacturing Process.

Analysis Strategies can be applied to analyze dynamics of:

�� any “System” and the interaction among all of its components (any level and scale)

�� interactions between the “System” and other “Systems” (System of Systems)

�� interactions between a “System” and the “Environment” where it operates for nominal and off-nominal

(accident included) situations.

We would like to state that, in this document, multiscale stands for “multiscale multiphysics” and that

multiscale is a general term and it embodies, as a special case, classical single scale models and analyses

which can, in turn, take advantage of “Reduced Order Models” built upon multiscale analysis schemes. The

term “Integrated” is used because R&D and Engineering Strategies are based upon a full integration of

computational and experimental, testing and sensing models, techniques and strategies.

A key goal of Multiscale Information - Driven Strategies is to develop a hierarchy of Multiscale Multilevel

Multiphysics (Computational, Experimental, Testing and Sensing) Models. Each model should be

characterized by the level of complexity thought to be needed to get the Information to accomplish specific

tasks: no more no less. Citing Einstein: A model must be as simple as possible, but not simpler

“Integrated Multiscale Science – Engineering Analysis Strategies” synthesize and take advantage of all the

concepts and methods described in the previous paragraphs:

� Data, Information and Knowledge Structures and Analysis Schemes (Multiscale Knowledge Domains

and Multiscale Maps)

� The “Multiscale Science – Engineering Information Space” and “Information – Driven” concepts

� The “Modeling and Simulation” as “Knowledge Integrators and Multipliers”“ and “Unifying Paradigm

for Scientific and Engineering Methodologies” concept and related “Methodologically Integrated

Multiscale Science – Engineering Strategies” .

Analysis Strategies take advantage of the full spectrum of Multiscale Methods (hierarchical, concurrent,

adaptive,..). The full spectrum of Multiscale schemes can be applied in an integrated way to achieve specific

objectives. The “Computational Materials Design Facility (CMDF), developed at Caltech and MIT,

introduced the term “Multi Paradigm” for this scheme. Top – Down Analyses are integrated, as needed, with

Bottom – Up analyses.


39

Three application lines for Multiscale Analysis Strategies can be devised:

� Multiscale Scientific Analyses finalized to “Understand” Physical and Chemical Phenomena and

Processes and their Relationships a spectrum of multi scale computational, experimental, testing and sensing methods linked using a full

range of coupling schemes (multi paradigm approach) are applied to gain a unified understanding of

scientific and engineering phenomena/processes and elucidate relationships and interdependencies between

phenomena, processes and system architectural/structural elements of varying complexity inside a scale and

across different scales. Multiscale Maps give a coherent view of the network of relationships and

interdependencies among “System Dynamics” variables turning data from different sources into Knowledge

� Reduced – Order Modeling, Sub – Grid Models and Constitutive Equations Development

Reduced-Order Models, Sub – Grid Models and Constitutive Equations are built, taking advantage of

Knowledge get Multiscale Scientific Analyses described in the previous item. Constitutive Equations and

Sub – Grid Models are inserted inside classical Engineering codes. Multiscale Scientific Analyses are an

important element to build “Hierarchies of Multilevel Multiscale Computational, Experimental, Testing and

Sensing Models/Techniques. In this perspective, Reduced – Order Models and “Hierarchies” can be

regarded as a synthesis and integration of science and engineering. The fundamental objective is to improve

reliability, range of validity, and effectiveness of models applied in the different phases of the Research,

Technology Development and Engineering Process and for Systems and Life – Cycle Engineering issues. It

is important to highlight that Knowledge get from Multiscale Scientific Analyses is captured and organized

not only by reduced order modeling, but also by Multiscale Maps. This kind of strategy allows to directly

insert “Multiscale Knowledge” inside classical Engineering/Manufacturing/Processing models and codes

These flexible integration strategies allow Engineering Teams to use, in a systematic way, scientific

knowledge without having to directly manage the complex modeling and simulation process of basic physical

and chemical phenomena. Such task would require highly specialized knowledge which is, normally, outside

the reach of designers.

� Integrated Multiscale R&D and Engineering Strategies

Both the approaches can be integrated in an interactive way inside an overall strategy. Some tasks can be

executed with the Multiscale Scientific Analysis approach. Some other tasks can be carried out by applying

Reduced – Order Modeling and/or the Hierarchical approach.

It is important to emphasize that the application of Multiscale Strategies demands some not secondary

modifications in the projects organization, structuring and management. In particular, a fundamental element

is the definition of “Integrated Multiscale Multidisciplinary Teams”.

Integration develops over three lines:

�� Disciplines: physics, chemistry, electronics, biology,

�� Scales: specialists who operate in various in Scientific and Engineering areas

�� Methodology: specialists who operate in the three methodological contexts: Theory, Computational,

Experimentation & Testing

A fundamental recommendation for all the strategies is to adopt an “Adaptive and Multi Step Selection of

Details and Resolution”


40

Integrated Multiscale R&D and Engineering Analysis Strategies develop over the following phases and

steps:

1) Definition of Analysis Process Architecture

– Definition of the “System” Architecture

– Identification of the reference scales of the “System” to be analyzed (the selection is linked to

specific analysis objectives and tasks)

– Definition of Functions to be performed by the “System” for the full hierarchy of its “Elements”

– Definition of the [Requirements - Performance – Properties – Architecture/Structure Relationships]

– Definition of the overall Architecture of the Analysis Process (Analysis Modules): Multilevel

Network of R&D and Engineering Analysis Modules and Tasks and related relationships and

interdependencies. More hypotheses can be worked out. Hypotheses are tuned and/or modified

following Analysis results.

This Phase is accomplished setting up some hypotheses built upon the knowledge available at the starting

time.

2) Analysis Strategies Definition Multiscale Maps, at the starting time, are built using existing information and knowledge and processing

available data (historical data bases). Then, Analyses deliver new data that allow to iteratively and

interactively modify first Map hypotheses.

For each Task of the previously identified Tasks Network:

– Identification of physical and bio - chemical structures over the selected scales which are thought

to be relevant for the “Objectives” of the Analysis [Architectural/Structural Maps]

– Identification of bio - chemical and physical phenomena/processes and their interdependencies

underlying and characterizing the dynamics of a system and thought to be relevant to meet with

the “Objectives” of the Analysis for the full range of the selected scales [Physics Maps]

– Identification of the “Requirements - Performance – Properties – Architecture/Structure”

relationships inside a scale and over the range of the selected scales [Requirements - Performance –

Properties – Architecture/Structure Map]

– Identification of “ Processing – Architecture/Structures” relationships (if it is needed in the analysis

process) inside a scale and over the range of the selected scales [Processing – Architecture/Structure

Map]

– Definition of what kind of Information is thought to be needed to achieve Analysis Objectives for

the Analysis Tasks. [“Thought to be needed” means that the process is iterative and interactive, we

start with some hypotheses and just Multiscale Science Engineering Analyses give us the possibility

of improving our evaluation]

– assessment of what Information at what level of accuracy and fidelity can, existing analytical

theories, computational models, experimental testing and sensing techniques and related coupling

schemes, deliver (evaluation performed using the “Multiscale Science – Engineering Information

Space” and historical available Information).

– Definition of how good” (Multiscale Science – Engineering Information Analysis) analytical and

computational models, experimental, testing and sensing models and techniques and related

coupling schemes should be to get the previously identified information . That means evaluating if,

where, when and to what extent we have to take into account a hierarchy of scales and develop and

apply new multiscale models and new reduced order models instead of existing single and

multiscale models.

Not in all the cases, of course, we should go down until Schrödinger equations from the continuum.

Don’t Model Bulldozers with quarks (Goldenfeld and Kadanoff, 1999)


41

– Identification of what new analytical and computational models and what new experimental,

testing and sensing techniques should be developed

[Note : A New Approach to Analysis. The “Multiscale Science – Engineering Information Space”

and the “Information – Driven Analysis” concepts and methods allow us to identify not only what

we know, but in particular, what we do not know, what we should know, how we should know it

(what combination of new scientific and engineering methodologies and technologies should be

needed)]

– Development (if it is needed) of new analytical theories, experimental, testing and sensing

techniques and computational (reduced order models included) models, definition of the related

coupling schemes inside a scale and between different scales.

– Identification of experimental, testing and sensing techniques needed to validate computational

models.

– Definition of Verification and Validation strategies for all the models and the coupling schemes

− Definition of “Methodologically Integrated Strategies”: what is the right combination and the right

sequence of application of single (including reduced order models and analytical formulations) and

multiscale computational models and single and multi scale experimental, testing and sensing

models/techniques to get Information thought to be needed to accomplish specific analysis tasks

[Integration Strategy Map]

Note: from a general point of view, it can be advisable to adopt a “Multiscale Multiphysics Multilevel

Multistep Adaptive” Modeling and Experimental and Testing Strategy. Multistep Adaptive means that we

start with some simple models, experiments and tests to get a first acquaintance of the dynamics of the

system. The analysis of data, information and knowledge (Multiscale Maps) get from a first run makes it

possible to adaptively increase complexity levels) of models, experiments and tests only as needed for

specific tasks.

4) Analysis Execution

For each Task:

�� A first run of Computations, Experimentations, Tests and Sensor measures is performed

�� in and out data and information flow linked to the selected computational models and experimental,

testing and sensing models/techniques is tracked

�� Data from Computations, Experimentations , Testing and Sensing is analyzed, correlated and organized

using Multiscale Maps

�� All Maps, and Strategy Modules are updated, as needed, following analysis results

�� New Multiscale Methodologically Integrated Strategies (if it is deemed to be necessary) are formulated

�� Evaluations of the impact of the results over Analysis and Design Hypotheses and the Architecture of

Technology Development and Engineering Analysis and Design Modules are carried out

The Process is iterative. We can have more iteration levels:

�� Inside each Analysis Task of a specific Analysis Module

�� Between Tasks inside a specific Analysis Module: results of a Task analysis can change analysis

conditions for the other Tasks

�� Inside each Analysis Module (Architecture of the Multilevel Network of Tasks can be changed

following results from Tasks Analysis execution)

�� Inside the Multilevel Hierarchy of Modules (Architecture of the Multilevel Network of Modules can be

changed)

Specific Modules and Tasks can be devoted to develop Reduced – Order Models, Sub – Grid Models and

Constitutive Equations.


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4. Integrated Multiscale Science – Engineering Technology, Product

and Process Development (IMSE-TPPD) Framework

4.1 Overview and Architecture

Running programs in Europe, US and Japan are putting the bases for the definition and implementation of a

new generation of “Integrated Product and Process Development” (IPPD) Frameworks which can be termed:

“Integrated Multiscale Science – Engineering Technology, Product and Process Development” (IMSE-

TPPD) Frameworks. We add the term “Technology” because, in the context of “Science – Engineering

Integration”, we would like to stress links between science, technology development, engineering and

manufacturing.

An interesting step towards this strategic direction has been the “Integrated Computational Materials

Engineering” (ICME) Initiative promoted by US National Academy of Sciences and TMS. ICME is

supported by Universities, Research Centers and Industry. In Europe several EURATOM programs are

pursuing similar objectives. Outside, the Materials and Processing Area, the EU “Virtual Physiological

Human” is a noteworthy initiative which foresees the development of a large scale and scope “Integrated

Multiscale Framework” for the Biomedical field.

Materials and Nanostructured Devices and Systems are, more and more, inherently, “Multiscale Systems”,

i.e. systems organized following a hierarchical strategy where structures at the different scales interact in a

synergistic way to give an extended spectrum of functionalities and performance. The development of new

Multiscale Frameworks can give the birth of a new field: Multiscale Technology, Engineering and

Processing/Manufacturing.. This issue is fundamental to meet with an extended range of requirements

(efficiency, safety and environmental compliance).

A very interesting example of this strategic approach has been the EU NMP (Sixth Framework) Integrated

Multiscale Process Units Locally Structured Elements (IMPULSE 2005 – 2009) Program. IMPULSE

is Europe’s flagship R&D initiative for radical innovation in chemical production technologies. Created in

the framework of the SUSTECH program of CEFIC, IMPULSE was a specifically targeted program aimed

at creating a totally new strategy for the design and operation of production systems for the chemical (and

related) process industries. IMPULSE aimed to develop a new approach to competitive and eco-efficient

chemicals production: structured multiscale design. The multiscale design approach of IMPULSE provides

intensification locally only in those parts of a process and on the time and length scale where it is truly

needed and can produce the greatest benefit IMPULSE aimed at the integration of innovative process

equipment such as microreactors, compact heat exchangers, thin-film devices and other micro and/or meso-

structured components, to attain radical performance enhancement for whole process systems in chemical

and pharmaceutical production. It is to be highlighted that the IMPULSE experience can be relevant to many

EUMAT and NMP themes and areas and also outside them: Energy and Aeronautics and Space for instance.

Concepts like “Integrated Product and Process Development” (IPPD) and “Product Life – Cycle

Management” (PLM) have reshaped the way Industry dealt with the development of high-tech products and

related manufacturing processes. The ”Integrated Multiscale Science-Engineering Framework” can be a

suitable basis to develop upon a new generation of Multiscale Science-based CAD, CAM, CAE and PLM

and IPPD Software Environments which can start a new phase for Research and Innovation.

IMSE-TPPD Software Environments are constituted by:

� Multiscale Science – Engineering Data, Information and Knowledge Management Systems

� Multiscale Multiphysics Computer Aided Design (CAD) System (based upon Architectural and

Functional Maps)


43

� Multiscale Multiphysics Computer Aided R&D and Engineering (CARDE) Systems

− Multiscale Maps

− R&D and Engineering Strategy Modules

− Integration Strategy Maps

− Experimental, Testing and Sensing Modules

− Computational Codes Library

− Experimental and Testing Techniques/Equipments Library

� Application Modules and Frameworks (Life – Cycle, Safety & Security, Environmental Impact,…)

� Multiscale Visualization Modules

Software Environments run over “Integrated Multiscale Science – Engineering Cyberinfrastructural

Environments”

The classical “Integrated Product and Process Development (IPPD)“ Framework is linked to the “Extended

Enterprise” concept. The new IMSE-TPPD Framework, proposed in this document, can be related to a new

industrial, economic and societal scenario and context which can be called “ Integrated Multiscale

Multidisciplinary Science – Engineering (or Science – Based) Cyber Extended Enterprise”.

The IMSE-TPPD Framework fundamental goal is to significantly improve identification, generation,

analysis, fusion, integration and interpretation of Data, Information and Knowledge associated to the

different tasks in the different phases of a general R&D and Engineering Process. and for the whole Product

Life – Cycle.

Innovative Technology Products and Processes call for the development and the integration of a so large set

of basic technologies, devices and components and so long development and maturation times that a new

kind of “two way strategic collaborations” among universities, research centers, and high-tech companies

is needed. A “two way strategic collaboration” should

� Enable a long term, systematic, organic, and effective involvement of the scientific community inside

real operational innovation technology programs with specific tasks, responsibilities, and profits

� Compels industry to redefine the whole technology development abd engineering process taking full and

systematic advantage of science knowledge and progress

Advances in Computing, Information and Communication (CIC) technologies shape the “structural and

technological layer” for new cooperative landscapes. At the same time, the new IMSE-TPPD Framework

can represent the “methodological and conceptual layer” which allow to take full advantage of technology

advances (Computer, Information and Communication (CIC) and Experimentation, Testing and Sensing

Technologies).

People often talk, today, about “two-way” relationships between the industrial world, from one side, and the

academic and research world, from the other side. However, we think that is very difficult to set up a real

comprehensive and highly effective “two way” partnership between the two worlds without being able to

Organize and Integrate “Knowledge” get in the different Technology Readiness Level phases: Basic and

Applied Research, Technology Development, Product Design, Product Manufacturing, Product Testing

(Development and Operational Testing).


44

Some elements to improve Knowledge Integration and Organization have been described in the previous

chapters:

− Multiscale Maps

− Integration Strategy Maps

− R&D and Engineering Strategy Modules which define, at several levels of detail, the Architecture of the

R&D and Engineering Process

“Multiscale Science –Engineering Data, Information and Knowledge Management Systems“ are powerful

“Integration Schemes” because they correlate and fuse inside a coherent and comprehensive framework

data, information and knowledge coming from different scientific and engineering teams, from different

methodologies, from the different tasks in the different stages of the whole Technology Development and

Engineering Process.

As “Cyberinfrastructures” become bigger and bigger by connecting an ever increasing number of resources,

facilities and teams, as the “Data Challenge” becomes a critical conditioning factor. In the Virtual

Distributed Environments or Cyberinfrastructures, researchers and designers can access a really huge amount

(tsunami) of data. How can we turn this ocean of data in useful knowledge, taking into account that the

needs and the points of view of the different groups are absolutely not homogeneous? Advanced graphic and

virtual reality technologies are very important assets, but not the ultimate solution to the problem. The

amount of data related to a complex R&D and engineering process and analysis of complex systems is

continuously increasing. It is worthwhile highlighting that, if it is true that the lack of data and information

represents a negative status, it is also true that the a flood of data and information can represent an even more

dangerous situation. Data and information growth reflects, of course, increasingly systems complexity and

science-engineering integration.

The IMSE-TPPD Framework (this one described in this document is only a first proposal not the ultimate

solution) allows to take into account, inside a unified context, all the phenomena, from atomic and

molecular scales to the engineering and operational ones which rule materials design, processing and

application including life-cycle and sustainability issues. Integration of atomic/molecular scales with the

micro, meso and macro worlds is a fundamental challenge for a wide industrial application of the most

innovative nanotechnologies in the materials, engineering and processing areas. Multiscale Collaborative

Frameworks realize a real “two-way” science-engineering integration from an industrial point of view and

put the bases to create a new “Multiscale Quantum Engineering World”.

The IMSE-TPPD Framework deals with the following areas:

�� Research and Technology Development

�� Engineering Analysis and Design

– Mission and Scenario Analysis

– Requirements Definition

– Design (Conceptual, Preliminary and Detailed Design)

– System/subsystem model or prototype demonstration in a relevant environment

– System prototype demonstration in an operational environment

– Full System completed and “qualified” through test and demonstration

�� System Engineering

�� Life – Cycle Engineering

�� Safety Engineering


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�� Manufacturing and Processing R&D and Engineering

�� Environmental R&D and Engineering (Green Engineering and Analysis of the Impact of Product and

Processes on the Environment for nominal and off nominal operating conditions, accidents included)

�� Innovative Technology and Systems Development Planning

Note: Multiscale Maps and Multiscale Knowledge Domains can not only improve the transfer of

Knowledge between Scientific and Engineering Areas (Knowledge Vertical Integration), but, also, improve

the transfer of Knowledge among the spectrum of the previously defined Areas (Knowledge Horizontal

Integration).


46

4.2 Multiscale Systems Engineering

Integration among different technologies and different sub-systems, components and devices is, today, a

fundamental challenge in the development and design of high-tech systems. In the future, the widening use

of a full hierarchy of nano, micro, meso technologies, devices and components will make this issue even

more critical. System Engineering will, more and more become a Hierarchical “Multiscale” Systems

Engineering. Nanotechnology will be a catalyst for this process

Because, today, it begins to be possible to analyze the dynamics of systems at multiple scales, the next step

is to use “Integrated Multiscale Science - Engineering Strategies” to design hierarchical systems at multiple

scales. That means being able to design systems in such a way as to make multiple “structures/elements” at

different scales cooperating to produce an increasingly wider spectrum of properties and functions and

higher performance levels.

In the “System Engineering” field, Analyses Challenges are linked to the following issues:

�� Analysis of the Multiscale Interactions between the “System” and the Operational Environment for the

whole “Operational Envelope” and for extreme and accident conditions in order to define “Systems

Requirements”

�� Analysis of Requirements over the full spectrum of scales (Multiscale Requirements Traceability)

�� Analysis of the “Requirements – Performance – Architecture/Structure” relationships and

interdependencies over the full spectrum of scales

�� Analyzing Multiscale Interactions among different elements at the same scale

�� Analyzing Multiscale Interactions among “System Architectural Elements” working at different scales

To address these challenges is fundamental to develop Hierarchies of Multiscale Multilevel “Variable

Fidelity” Computational Models and Experimental/Testing/Sensing Models and Techniques and efficient

and reliable coupling schemes between codes based upon a range of physico mathematical representations

and principles.

New technological solutions (micro and nanotechnologies) and tighter and tighter requirements pose specific

challenges which change in a qualitative, not quantitative, way the approach to analysis, simulation and

design in the Hierarchical Multiscale Systems Engineering scenario:

�� From a general point of view, the overall performance and operating behavior of systems will be more

and more determined by how multiscale and multi-physics phenomena interact in multi-component and

multimedia environments. The general trend towards miniaturization (micro and nano technologies)

makes it necessary for CAD/CAE/CAM systems to take into account, inside a fully integrated context,

an ever wider range of geometric and physical scales Separated single scale models have only a partial

validity if we like to predict in a correct way the overall behavior of a complex system under real

operating conditions, in particular when side effects, extreme and off-nominal conditions occur. Many

side effects stem from small-scale geometric details and media interactions that are not comprehensively

and adequately modeled by constitutive (engineering scale) equations. All that makes simulations using

classical engineering codes and separated sets of engineering and scientific codes, very difficult and of

limited reliability.


47

�� Off-nominal physical behaviour, such as fatigue, fracture, damage and corrosion a s well as off-nominal

dynamics of components, sub-systems and systems (or system of systems) in extreme operational

conditions (accidents included) occur at multiple space and time scales. The problem is classically

addressed by resorting to expensive physical prototypes for sub-systems and components, and setting up

lengthy, and not in all the cases really exhaustive and conclusive, testing activities. No unified

multiscale and Multilevel variable fidelity hierarchies of models exist that describe behavior across this

huge scale range. Instead, we have separate and non-communicating models at the different scales and

fidelity levels.

The development of “Integrated l Hierarchies of Multiscale Multilevel and reduced – order

computational models and experimental, testing and sensing models/techniques” can be considered as a

key target.

A multiscale system design approach opens the way to new strategies for complex systems control. A

combination of new multiscale sensors, meso, micro and nano systems, and distributed computing systems,

can lead to innovative control schemes. New multiscale sensors will be able to deliver not only "averaged"

data and information, as in the past, about space and time variations of key physical and technological

variables (pressure, temperature, chemical composition,....) but the detailed map of local values and rates at

different levels of resolution and time and space scales. This kind of information can be used to develop and

validate off-line physical models no longer based on an empirical and semi-empirical (averaged) knowledge

but on a first principles understanding of the physical reality. Highly detailed real-time models to control

technological systems will grow out of this new level of understanding and will run on an array of distributed

computing systems.

Hierarchical System Integration and Nanotechnology

Hierarchical System Integration is a fundamental goal for Nanotechnology in order to fully exploit

Nanotechnology potentialities for Engineering and Manufacturing.

The Following text drawn from the “Active Nanostructures and Nanosystems (ANN) Program Solicitation

NSF 05-610” clearly describes this goal

� Nanoscale Devices and System Architecture.

….New concepts, tools and design methodologies are needed to create new nanoscale devices, synthesize

nanosystems and integrate them into architectures for various operational environments….. In order to

systemize the design of complex nanosystems, multiple layers of abstractions and various mathematical

models to represent component behavior in different layers are also required….. A special focus is on new

architectures and improved functionality of large and complex nanosystems, and their integration with

larger scale systems….

� Hierarchical Nanomanufacturing.

….Research in this area will focus on creating nanostructures and assembling them into nanosystems and

then into larger complex structures of at least two length scales where the principles of manufacturing or

operation are different……


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4.3 Multiscale Process Engineering

“Environmental and Climatological Issues” emerged as one of the critical challenges facing the the whole

Manufacturing and Bio-Chemical Processing World. Efforts to reduce the pollution generated by industrial

activities rely in several cases on the "end-of-the-pipe" control strategy. In this context, strategies focus

essentially on pollution cleanup and waste management technologies. A more radical and innovative

approach, which can be defined "clean by design", “green engineering” or “process intensification” , entails

the re-design of manufacturing and process system to eliminate at the root the formation of pollutants and

toxic by-products. This new approach entails a tight multiscale multidisciplinary science – engineering

integration and new Integrated Frameworks like the one here described.

The limits of the single scale approach to Chemical Engineering Design were well described by the words of

Dr. Irving G. Snyder Jr., director of process technology development at Dow Chemical. He highlighted : "In

the chemical engineering field we often know that A plus B makes C but, in many cases, we do not know the

transient intermediates that A and B go through in producing C; the reaction mechanisms of all the by-

product reactions; which of all the steps in the reaction mechanism are kinetically controlled, which mass-

transfer controlled, and which heat transfer controlled; if the reaction is homogeneous, what takes place at

every point in the reactor at every point in time; and if the reaction is heterogeneous, the diffusion

characteristics of raw materials to the catalyst surface or into the catalyst, as well as the reaction, reaction

mechanism, and by-product reactions within the catalyst, the diffusion characteristics of products away from

the catalyst, and the nature of heat transfer around the catalyst particle". This scheme describes a multiple

space and time scenario.

It is important to highlight that the integrated multiscale methodology allows to go beyond the classical

reductionist approach which tries to represent, for instance, a whole plant by decomposition into smaller

units down to catalyst particles, droplets, bubbles, and finally to molecular processes, going into more and

more detail. Multiscale enables the development of systemic models that considers the global behavior of

complex systems as a whole integrating representations and information across multiple scales : _ nanoscale:

molecules and clusters

− microscale: drops, bubbles, and particles

− mesoscale: unit operations such as reactors and heat exchangers

− macroscale: production units and chemical plants

− mega scale: local and global environmental impact studies

Multiscale can also lead to an integrated “Vision” of the classical four areas which characterizes

chemical engineering :

− Process Modeling

− Ecosphere Modeling

− Properties Modeling

− Product Modeling

Two examples of fields which can be affected by Multiscale and even more by “Integrated Multiscale

Science – Engineering Frameworks” are highlighted in the following:

Intelligent operations and multiscale control of processes. The implementation of multiscale modeling jointly with the use of computer-based control schemes and

array of advanced sensors would allow to control events not only at the classical macro scale but at the

microscale level (detailed local temperature and composition control) and also at the molecular levels by

manipulating supramolecular building blocks. New science-based technologies will make possible a very

accurate control of reaction conditions with respect to mixing, quenching, and temperature profile. This

science-based scheme significantly differs from the classical one that imposes boundary conditions and lets a

system operate under spontaneous reaction and transfer processes. The multiscale control of processes

solution would lead to an increased productivity and selectivity and open the way to a "smart chemical

engineering" otherwise referred to as “Process Intensification” to meet, at the same time, tight economic and

environmental requirements. At this level, new functions such as self-organization, regulation, replication,


49

and communication can be created by manipulating supramolecular building blocks. At a higher microscale

level, detailed local temperature and composition control through staged feed and heat supply or removal

would result in higher selectivity and productivity than does the conventional approach that imposes

boundary conditions and lets a system operate under spontaneous reaction and transfer processes. Energy in

specific forms such as microwave or ultrasound also may be supplied locally. The theory of wavelet

decomposition can represent a basis for the multi-resolution description of operating variables and modeling

relations.

.

Design of New Equipments Based on Scientific Knowledge and New Modes of Production The development of an integrated conceptual framework, which links basic scientific understanding to

engineering and technological issues, makes it possible to conceive innovative equipments based on first

principles. The design of new operating modes in chemical engineering can be linked to a science-based

approach. Innovative engineering applications of reversed flow, cyclic processes, unsteady operations,

extreme conditions, high-pressure technologies, and supercritical media, are largely dependent on the ability

to couple scientific and engineering knowledge and formulations. The combined use of new sensors, Micro

Electro Mechanical systems (MEMS) technology and a scientific understanding of physical and chemical

phenomena will lead to microreactors, micro separators, and micro analyzers smaller than a fingernail,

making possible accurate control of reaction conditions with respect to mixing, quenching, and temperature

profile.


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4.4 Multiscale Environmental, Safety and Extreme Engineering

Computational Multiscale is widely used in the Environmental and in the Safety (Nuclear Energy, in

particular) fields. Strategic Multiscale can play a pivotal role in promoting significant innovation and real

breakthroughs not simple improvements in the following areas:

� Analysis of the Dynamics of Ecosystems and two – way relationships between Urban and

Industrial Systems – Ecological Environments.

� Developing unified analysis of the hierarchy (multilevel Network) of interlinked multiscale

multiresolution multiphysics (physics, chemistry and biochemistry) phenomena and processes which

characterize the dynamics of the Ecosystem over a full range of time scales (short, medium long term)

also taking into account the uncertainty issue. This kind of analyses will benefit of advances in new

multiscale multiresolution monitoring Systems and Cyberinfrastructures [Methodologically Integrated

Multiscale Science – Engineering Strategies, Integration Strategy Maps, Information – Driven

Strategies]

� Integrating Multiscale Multiresolution Multiphysics Data from a full spectrum of Space, Aerial, Sea

(and Under Sea), Surface and Sub Surface sensors [Multiscale Maps]

� Integrating (Multiscale) Field Sensor Networks with Laboratory Experimental Facilities [Multiscale

Experimentation, Testing and Sensing, Integration Strategy Maps, Information Space Concept,

Methodologically Integrated Multiscale Science – Engineering Strategies]

The need of integrating science and engineering is even more pressing if we consider a new “Integration

Frontier”: Infrastructures - Environment – Climatology – Health”.

�� Integrated Multiscale Technology and Engineering Systems Design

� The need to meet with and extended range of ever more demanding requirements (safety, resilience,

operation in extreme environments, environmental compliance) put an increasingly pressure on

traditional technology development and engineering solutions. A specific objective is of particular

relevance is the Design of a new generation of “Inherently” Multiscale Infrastructural Technologies

and Engineering Systems. The full spectrum of concepts, methods, environments and strategies

described in the “Integrated Multiscale Science – Engineering Framework” Chapter can be directly

applied in this Area..

�� Extreme Engineering for Extreme Operational Environments (Safety and Security) � Approximations and simplified experimental analyses which can be up to the challenge to characterize

and design materials and systems for normal operational conditions are not well suited when materials

and systems must operate in extreme conditions, accidents included. In these cases, a multiscale

multiresolution science – engineering approach should be applied.

Lessons to be Learnt in the Safety Engineering Field :

− If you do not understand Physics you cannot understand, manage and reduce uncertainties and risks

in a comprehensive and “acceptable” way. The warning pointed out by Nobel prizewinner Richard

Feynman in the appendix F of the Challenger explosion report (Roger’s Commission) about the risk

to use only empirical and semi-empirical models has yet to be fully perceived and evaluated. This

warning has a general value for all the engineering (not only Safety) and far beyond the aerospace

world.


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�� Multiscale Systems Testing for an Extended Operational Envelope

� We implement the concept of “Multiscale Multiresolution Knowledge – Based Development and

Operational Testing” and the related application schemes described in the paragraph 3.4.4

�� Design and Management of Multiscale Environmental and Climatological Monitoring Systems

� A critical element to understand, predict and control “Complex Systems”, with particular reference to

Environmental, Civil, Infrastructural, Aerospace and Defense Systems, is the design of “Multiscale

System Monitoring Networks” . Significant advances in Sensors, Computing, Information and

Communication technologies enable the creation of complex networks of different types of monitoring

sensors/systems operating over a full spectrum of space and time scales and a wide range of physical,

chemical and biological domains. These networks are connected ( also in real time) at a powerful set of

distributed data repositories and computing facilities. . Key issues:

−−−− identify key variables to be monitored over the spectrum of scales and resolution levels accounted

for, and at what level of accuracy and reliability

−−−− identify relationships and interdependencies between physical and bio-chemical phenomena and

processes at the different space and time scales and resolution levels

Integration of available analytical theories, computational models, and data from sensing and

laboratory experimental facilities applying Multiscale Maps and the Information Space method and

concept can be useful to build first hypotheses about this issue

−−−− devise a strategy to select the right type of sensors [Multiscale Science – Engineering Information

Space and Information – Driven strategies] to monitor the previously identified key variables over

the right range of space and time scales at a well defined degree of accuracy and reliability.

−−−− define the field monitoring systems architecture at all the scales and for all the media [Multiscale

Science – Engineering Information Space, Information – Driven strategies and Integration Strategy

Maps].

− define a strategy to integrate fields data and information with laboratory experimental systems,

theory and computational models [Multiscale Maps and Multiscale Science – Engineering

Information Space and Information – Driven strategies]

− define a suitable mix of field sensors and experimental techniques and methods at all the scales and

integrate them in order to improve the knowledge about the dynamics of the (natural, technological,

natural-technological) system under observation and analysis. Two –way relationships between

field monitoring systems and experimental facilities are functional to develop innovative sensing

strategies. [Multiscale Maps, Multiscale Science – Engineering Information Space and Information

– Driven strategies, Integration Strategy Maps]

− devise a general “integration strategy” which allows to link together all the previously quoted items

inside a coherent and comprehensive context Multiscale Maps, Multiscale Science – Engineering

Information Space and Information – Driven strategies, Integration Strategy Maps, R&D and

Engineering Project Strategy Modules]


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�� Integrated Space – Time Environmental/Pollution Analysis

� The Integrated Multiscale Science – Engineering Framework” “ enables a new “Integrated Space-Time”

approach to environmental and pollution issues. “Integrated Space-Time” approach means that in this

new methodological and conceptual context, we can link together inside a unified context data,

information, knowledge and models which characterize the three fundamental phases which characterize

the pollution process :

− Generation Phase (generation of pollutants inside a technological system)

− Transportation/Diffusion Phase through different media (air, water, land)

− Interaction or Biomedical Phase (interaction with natural, urban & industrial and biological systems

(humans included)


53

4.5 Innovative Technology and Systems Development Planning

The following figure illustrates the NASA’s Technology Readiness Level (TRL) Scale. This scale describes

the several phases of an “Innovative Technology and Systems Development Planning Process” linked to a

specific R&D and Engineering Project.

This representation has a general value. It can be applied to any technological and engineering sector

The fundamental goals of new planning processes based upon the “Integrated Multiscale Science-

Engineering Framework” are to :

� improve effectiveness and reliability of alternative “System Architectures” selection process by

identifying in a more comprehensive and reliable way problems linked to interactions among the

different subsystems, components and devices which constitute the overall “System Architecture”.

Interactions are, in many cases, intrinsic multiscale problems

� improve evaluation of the impact of advances in fundamental scientific knowledge over the development

of innovative technology solutions and systems architectures

� improve assessment of how “System Requirements” propagate down the TRL chain following a “Top

Down” approach In this prospect, the definition of performance levels and operational requirements for

the system, enables, following multiscale Multilevel analysis schemes, the identification of what are the

needed features and performance of sub-systems, components, and devices and their relationships.

� improve assessment of the Science-Engineering Information needed to accomplish each step (from TRL

1 to TRL 9) and to transition in a successful way from a step to the next one

� improve assessments of what information can be get by using existing analytical theories, computational

models and experimental & testing techniques, and what not


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� improve identification of the needed development paths in analytical theories, computational models,

and experimental & testing techniques and what mix of resources (theories, modeling & simulation,

experimental & testing techniques) are needed to develop the envisaged sub-system or component.

� Improve Organization of Information inside each TRL Phase in such a way as to make it directly and

comprehensively usable and applicable in the next one along the scale.

Two development lines can be followed:

a) Bottom – Up Approach: the starting point is progress in innovative technologies and devices and

components (advances can be real or hypothesized)

b) Top – Down Approach: more ambitious operational and performance requirements to be met represent

the starting point

To accomplish the previously quoted tasks, we can use the full methodological and theoretical apparatus of

the “Integrated Multiscale Science-Engineering Framework” to build a “Virtual Multiscale Space-Time

Machine” or “Virtual Innovative Technology and Systems Development Planning Framework”. The

term “Virtual” means that:

In both the cases:

� A model of the planned system (and of the hierarchy of sub-systems, components and devices) is

developed using available information that is being organized using the “Multiscale Science –

Engineering Data, Information and Knowledge Management System”. Information are being

progressively increased as we transition from one phase to another one in an incremental way. As data

and information, along the TRL chain, become available from experimentation, testing and sensing, they

are inserted into the models by taking full advantage of the KIM concept and method.

� A model of the R&D and Engineering Process (Designing the R&D and Engineering Process) is built in.

The Model is progressively updated

“Virtual Analyses” can proceed following two strategies :

� “top-down” (from a complex structure and operational environment to its constituents) : requirements

are set for a system at a certain scale (access scale) and the analysis is performed for the hypothetic

system (several hypothesis are taken into account) considering the scales which are under the one for

which requirements (and accordingly levels of performance) are being set

� “bottom-up” (from fundamentals to a complex structure and its operational environment): hypotheses

are formulated about the architecture of the system for scales over the initial scale taken into account.

The analysis proceeds by evaluating how and to what extent performance and properties calculated and

or measured at a certain scale (nano scale, for instance) influence dynamics and architecture/structure at

the scale immediately higher (micro scale, for instance) and so on. This kind of approach is instrumental

to build technology roadmaps and innovative technology development plans

The approaches can be combined. Several different scenarios can be taken into account and evaluated (What

if Strategy)

In this “Virtual Context”, “Multiscale Science-Engineering Information-driven Strategy” is critical to

identify what kind of Information at what level of accuracy and fidelity should be needed to characterize the

complex technological dynamics of the set of subsystems, components and devices and their interactions. It

is important to highlight that even if we are not able to develop highly detailed multiscale models, the new

proposed Framework can represent a valuable tool. Simplified multiscale models can allow researchers and

engineers to jointly perform simple, but still meaningful analyses to identify critical items in the Innovative

Technology Development process and shape more effective cooperation between scientists and engineers.

A critical issue is that present innovative technology development strategies are, in several cases, not fully

able to assess the (multiscale science-engineering) information needed to develop, validate, and integrate

key technologies in more complex systems. Present innovative technology development strategies are not

fully “information-driven” or, to better say, Multiscale Science-Engineering Information-driven.


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The “Science-Engineering Information Space” and the Modeling & Simulation as “Knowledge Integrators

and Multipliers” concepts and methods allow us to define, besides the classical Technology Roadmaps, new

Integrated Multiscale Science-Engineering Information-Driven Theoretical and Methodological

(computational, experimental, testing and sensing) Roadmaps which enable researchers, designers and

managers to jointly identify critical scientific and engineering resources needed to develop innovative

technological systems and shape more effective university-research-industry cooperative scenarios inside a

unified and coherent conceptual context.

Roadmaps of computational methodologies are being already drafted, but they are not fully “Information-

Driven” and, normally, not comprehensively integrated with experimental, testing and sensing roadmaps.

Furthermore, computational and experimental & testing roadmaps are drafted separately without well

defined links and interdependencies.

Said in other words, roadmaps do not comprehensively identify and specify what information at what level

of accuracy and fidelity is needed to reach new engineering and technological achievements and what

information at what level of accuracy and fidelity we can get from the new outlined models and methods. Or,

at least that is accomplished only or mainly at a qualitative level.

The “Strategic Value of the “Integrated Multiscale Science-Engineering Framework” is that this kind of

approach enables a more in-depth and timely identification of the “Scientific and Engineering Critical

Issues and Domains and their relationships and interdependencies” in such a way as to allow for the

definition of timely integrated science-based (or science-engineering) strategies.


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5. Integrated Multiscale R&D and Engineering Infrastructural

Framework

The Integrated Framework described in the previous Chapters is the conceptual and methodological basis to

design a “New Integrated Multiscale R&D and Engineering Infrastructural Framework” which I

constituted by:

� Management Organizations which coordinate the following “Performing and Structural Entities”

− Industrial and Service Companies

− Universities

− Research Centers and Labs

− Computing and Information Centers

− Experimental and Testing Facilities

− Field Monitoring Systems

− Research, Technology Development, Engineering and Management Teams

− Cyberinfrastructures which link together Management Organizations and Performing Entities

Management Organizations and Performing Entities can also be organized in a hierarchical way

The Integrated Multiscale R&D and Engineering Infrastructural Framework foresees:

� New roles and functionalities for some “Performing Entities”

� New Methodological and SW Frameworks which improve the definition and the implementation of

coordinated strategies and activities

Key elements are:

� A New Generation of Cooperative University - Research – Industry Innovation Clusters and

Environments: “Integrated Multiscale Science – Engineering Cyber Extended Enterprise

Frameworks” These are Cooperative Environments where the new IMSE-TPPD Framework and the

related Integrated Multiscale Science – Engineering Framework, proposed in this document, can be

implemented.

− Science –Engineering (or Science – Based) means that the “Integrated Multiscale Science –

Engineering Framework” shape Research, Technology Innovation and Engineering Strategies and

operational activities

− Extended Enterprise means that the IMSE-TPPD Framework shape a new “University – Research

– Industry – Society Cooperative Environments”. This new kind of “Cooperation Contexts”

enables researchers, designers, public and private managers and politicians to synthesize a wide

spectrum of different resources, methods and operational schemes and define comprehensive

strategies to meet common objectives and goals. Multiscale Frameworks can be instrumental to

improve correlation between operational requirements, engineering requirements and technological

and scientific advances promoting accelerating in such a way technological and engineering

innovation

− Cyber means that the “Multiscale Science-Based Enterprise” concept is implemented over

“Integrated Multiscale Science – Engineering Knowledge Integrators and Multipliers

Cyberinfrastructural Environments”


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The “Multiscale Science - Based Cyber Extended Enterprise” concept can offer scientists, researchers,

public and private managers and politicians a “unified context” to better understand the complex pattern

of relationships and interdependencies among the wide range of different aspects and issues which

characterize the research and technological innovation world and, accordingly, synthesize widely

scattered efforts and forge more effective “unified strategies” to deal with problems of increasing

complexities.

� A New Generation of Computational Centres referred to as “ Integrated Computational Multiscale

Multidisciplinary Knowledge Integrator and Multiplier Centres” These Centres would be based upon the new central concept of “Multiscale Multidisciplinary Modeling

and Simulation as Knowledge Integrators and Multipliers” and “Unifying Paradigm” for the full

spectrum of Scientific and Engineering (experimental, testing, sensing) Methodologies. A “two – way”

partnership among the new envisaged Computational Centers and Experimental, Testing and Sensing

Centers and Facilities is a distinguishing feature of this new vision. Furthermore, Computational Centers,

following the “Knowledge Integrators and Multipliers” view will become a key node and catalyst of

multiple interaction patterns between the Theoretical, Experimental, Testing and Sensing Worlds

New previously illustrated concepts, methods and frameworks lead to a new set of Functionalities for the

Centres:

a) Integrated Environment for jointly (cooperating with Experimental, Testing and Sensing, Teams)

“Designing” Integrated Computational and Experimental, Testing and Sensing Roadmaps and

Strategies

b) Integrated Environment for the construction of Multiscale Multidisciplinary Science – Engineering

“Knowledge Domains” which turn Data coming from a full spectrum of scientific and engineering

sources (data bases, computations, analytical formulations, experimentation, testing and sensing)

into and Knowledge

c) Integrated Environment for the “Design” of new Multiscale Methodologically Integrated Application

Strategies and related Frameworks

� A New Generation of Cyberinfrastructures referred to as “Integrated Multiscale Multidisciplinary

Science – Engineering Knowledge Integrator and Multiplier (KIM) Cyberinfrastructures”

The term “Knowledge Integrators and Multipliers” spurs from the previously quoted New Vision of

Modeling and Simulation. New Cyberinfrastructures represent the “Infrastructural and Technological

Layer” for the Integrated Multiscale Science – Engineering Technology, Product and Process

Development (IMSE-TPPD) Frameworks. A new level of Integration is made possible by the KIM

Vision

Integration develops along the following lines:

� Scale integration (Multiscale Science and Engineering Integration) involving teams inside

University, Research Centres, Industry, dealing with research and engineering issues at different

scales and resolution levels. This kind of integration is already underway in the computational world,

it is at an early stage in the Experimental, Testing and Sensing Areas. The design and

implementation of Multiscale Science – Engineering Cyberinfrastructures or GRIDs can give a real

boost to the development of Multiscale Multiresolution Experimental, Testing and Sensing

technologies, procedures and strategies.

� Data, Information and Knowledge Integration: integration of data, information and knowledge from

a full spectrum of sources: theory, experimentation, testing and sensing) to build Multiscale

Multiphysics Science – Engineering Data, Information and Knowledge Data Bases


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� Teams lntegration: teams employing the full spectrum of methodologies (theory, computation,

experimentation/testing/sensing) for a wide range of disciplines (mathematics, physics, chemistry,

biology, electronics,..) work together to apply unified strategies for specific Tasks.

Integrated Multidisciplinary Multiscale Science – Engineering Teams (University, Research,

Engineering) working in the full range of methodologies (theory, computation, experimentation,

testing and sensing) and disciplines are made possible, from a technological point of view, by the

new generation of Cyberinfrastructures and by a theoretical and methodological point of view, by

the Integrated Multiscale Science – Engineering and the Integrated Multiscale Science – Engineering

Technology, Product and Process development (IMSE-TPPD) Frameworks.

All of that explains and justifies the term “Knowledge Integrators and Multipliers”.

Cyberinfrastructural Technology should be paralleled by and complemented by the development of new

“Methodological Frameworks” to take full advantage of technological potentialities. We think that

“ Integrated Multiscale Science – Engineering Frameworks” will be instrumental to:

– Design an “Adaptive Architecture” for the New Cyberinfrastructures: what computational,

experimental, testing and sensing facilities with what functionalities should be connected for specific

tasks and purposes and Define related Cyberinfrastructures – Based Methodologically Integrated

Strategies“ to coordinate the use of a full spectrum of resources and methodologies.

What has been described in the above can give a “New Meaning”, from a Theoretical and Application

point of view, to the “Knowledge and Innovation Communities” (KIC) term already used by the European

Institute of Innovation and Technology (EIT)


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Biography

Alessandro Formica. Formica has thirty years of experience in Computer-Aided Engineering, High

Performance Computing, Modeling and Simulation and R&D and Engineering Project Analysis and Design.

During his career his worked for ARS S.p.A. (ENI Group R&D and Engineering Company) as Director of

Advanced Projects; Engineering Systems International (Head of Italian Branch); Singapore Government

Industrial Group (Consultant); RCI Ltd. [US based International Consortium, operating in the Modeling &

Simulation and High Performance Computing areas] (European Scientific Director), RCI Consulting

Company (Scientific Director); Executive Office of US President (Consultant); European Space Agency

(Consultant); Alenia Space (Consultant); CSCS (Consultant); Daimler Group (Consultant); Alenia

Aeronautica (Consultant); Polytechnic of Milan (Consultant); Polytechnic of Turin (Consultant),. Presently

he is EUMAT Platform Working Group 2 Modeling and Simulation active member. Polytechnic of Turin

School of Doctorate Lecturer for Multiscale Science – Engineering Integration.

This White Book synthesizes several years of studies and consulting activities by the author in the field of

Multiscale Science – Engineering integration and its application to Research, Technology Development and

Engineering. Studies on Multiscale started at the beginning of the nineties when Alessandro Formica held the

position of RCI Ltd (US based HPC International Consortium) European Scientific Director. In the Report

“Fundamental R&D Trends in Academia and Research Centres and Their Integration into Industrial

Engineering” (September 2000), drafted for European Space Agency (ESA), a first version of an “Integrated

Multiscale Science - Engineering Framework” was outlined and its impact on R&D and Engineering

analyzed. The White Book “Multiscale Science – Engineering Integration – A New Frontier for Aeronautics,

Space and Defense (May 2003) sponsored by Italian Association of Aeronautics and Astronautics (AIDAA)

introduced the concept of “Strategic Multiscale” and a more refined version of the related Integrated

Framework.. A Framework version specifically conceived for Industrial Applications: “Integrated

Multiscale Science –Based Technology, Product and Process Development” was drafted in the context of the

consulting cooperation with Alenia Aeronautica and Finmeccanica Group (November 2006). Multiscale

Analyses and Studies were also carried out on behalf of Polytechnic of Milan and Turin and in cooperation

with University of Rome “La Sapienza” and University of Rome “Tor Vergata”.


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Contacts

Alessandro Formica

Via Piazzi, 41

10129 Torino

Italy

Phone. +39 338 71 52 564

E-mail : [email protected] and [email protected]

strategic multi scale october 2010

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