This project has received funding from the European Union’s Horizon 2020 research and i n n o v a t i o n f ra m e wo r k p ro g ra m m e u n d e r g ra n t a gr e e m e n t N o 636 202

TOWARDS THE NEXT GENERATION COLLABORATIVE MDO THE AGILE PROJECT

ARTHUR RIZZI

AIRINNOVA AB & KTH ROYAL INST TECHNOLOGY

WITH SUPPORT OF

AGILE CONSORTIUM

MDO Challenges for current aircraft development applications

2

Aircraft Product Development large number of parts and design sub-processes

Cross-organizational distributed and heterogeneous knowledge and expertise

Emerging novel technologies higher level integration of components

How to enable effective Collaborative MDO?

source: Lockheed P-80 Shooting Star

1943: 143 days!!!

Number of parts: 6 million Design changes per year: 150 000

source: Boeing

Today Yesterday Tomorrow

Towards the next Generation MDO

3

• HPC capabilities and simulation distribution

• Automation of analysis capabilities

2) second (~00-today)

• Integration of expertise in the collaborative optimization

• Knowledge formalization of processes and disciplinary domains

3) third (next gen)

Optimization

Sensitivities

Iterati

on

Meta-Models Database

Requirements,

Targets

Parameter

Performance,

Properties

Analysis 2

Analysis n

Analysis 1

Central

Product Model

OAD process

Analysis Domains

Optimization

Sensitivities

Iterati

on

Meta-Models Database

Requirements,

Targets

Parameter

Performance,

Properties

Central

Product ModelAnalysis 2

Analysis 1

Analysis n

OAD process

Distributed Analysis

Competence 1

Competence 2

Competence n

Optimization

Sensitivities

Iterati

on

Meta-Models Database

Requirements,

Targets

Parameter

Performance,

Properties

Central

Product Model

OAD process

Distributed Competence

3rd Gen. MDO:

system of distributed competences 1) first (~80)

• Disciplinary Simulation and optimization capabilities

• Optimization Strategies for low computational power

Details

AIAA-2017-4137

MDO is not new!

3rd Generation MDO Enablers

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design competences

common language

process orchestration

knowledge integration

COM

COM

Power Equation LP Spool

Power Equ. HP Sp.

HPT Cooling

COM

COM

Power Equation LP Spool

Power Equ. HP Sp.

HPT Cooling

TLAR next generation of collaborative MDO

People Software Data Communication

N2 Functional Breakdown/Interface Chart

Functional Flow Diagram

AGILE Ambition

5

PhysicsKnowledge

Abstraction

Knowledge

Abstraction

Design and Optimization Process

Time

AGILE

setup operational solution

Objectives: • Realize the 3rd generation MDO

• Reduce aircraft development time\costs • Enable Collaborative Aircraft Design

Accelerate the setup of large scale collaborative distributed processes

Support collaborative operation of design systems: people and tools

Efficient collaborative Optimization techniques

AGILE Use-Case Configurations

Design

1. Complexity - Preliminary aircraft design process

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All-at-Once Optimization X: Parametric Variables Xmin ≤ X ≤ Xmax

Xbaseline: As-Drawn Values U(X): Calculated Properties Maximize: F[U(X)]: Calculated MOM

Subject to constraints G[X, U(X)] ≥ 0 …

& busy work & errors

Intractable !

Decompose sequence of processes with some feedback

Orchestration N2-diagram shows: • Functional Breakdown • & Interface Flow

AGILE – Service Oriented Architecture

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Hierarchal workflow of “provided services” - Each service

- @ Partner site\network - collaborates across discipline & location - formalizes entire process - enabled by BRICS & RCE framework

Details

AIAA-2017-4138

MDO Process formalization

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MDO

Architecture?

Integration

Framework? Stored as CMDOWS Automated Translation

MDF

IDF

CMDOWS Common MDO Workflow Schema

MDO process description developed in AGILE project. Formalizes the MDO processes in a “neutral format” Enable to share MDO processes between MDO frameworks Provides the MDO process model which can be manipulated

cmdows-repo.agile-project.eu

Details

AIAA-2017-4139

AIAA-2017-3663

AGILE reference aircraft

Your

optimization

framework!

Orchestrate the workflow

Example DC1-MDA Aircraft

Target of AGILE Design Campaign 1 (DC 1):

Medium-range commercial airliner

• Baseline :

DC 0 by semi-empirical methods

• No experimental data available

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Parameter DC1-MDA Unit

Wing span 28.1 m

MAC 3.73 m

Wing area 82.8 m2

MTOW 39’750 kg

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Enhancing framework capabilities – Wing Design

Current limitations on wing shape optimization :

• Structural optimization loads constraints sequential aero / structure optimization process

Enhancement:

• Consider multi – objective approach for both aerodynamics and structure

Aero-structures optimization

Details

AIAA-2017-4140

Aero-elastic shape optimization loops

Shape optimization loop

Descartes Model

generation CPACS

Aero CAD

SUMO initial model generation

Descartes uv-mapping

Descartes Database

FEM model

SU2 model

Lagrange sizing

optimization Interface

Objective function evaluation

Punch file

Shape DVs

Fixed loads

SUMO volume mesh

update

Descartes

SUMO

Lagrange

SU2

Interface

Data

Legend:

Interface convergence

check

SU2 Aerodynamic

calculation

Lagrange Static analysis

FSI Matrix Interface convey

deformatio

Interface calculate

forces

Aero-elastic

loop

Descartes model update

Hierarchical schema definition Product and Process information xml based format

Developed since 2005 @ DLR Adopted by External Partners

Open source: https://cpacs.de Supporting Libraries available (visualization, handling, etc.)

CPACS – Data Schema Common Parametric Aircraft Configuration Schema

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Shape

Aerodynamics Low-Fi

DOC

Mission

Emissions

Systems

Structure

Aerodynamics High-Fi

Design Modules

Central Model

ad-hoc vs central interfaces

architecture

Data/Work Flow & Collaboration: DC-1 MDA

We are here!

: updated CPACS file

Flying Qualities

Step 1 – Create Required data

• Aerodynamic dataset – S & C derivatives

• Propulsion data • Mass breakdown data

– mass, cg, Inertia

• Geometric data

Store as CPACS file

AeroData Fusion

• Cannot compute every flight state

• Data fusion S&C aero-database

• CPACS compatible

• Steps include: 1. Initialization

2. Sampling

3. Co-Kriging surrogate model

4. Smart Sampling updates - hi-fi samples at suggested locations

5. Final surrogate model

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Step 2 – Create PHALANX Flight Simulator model

Performance, Handling Qualities, Loads Analysis Toolbox

• Automatic generation of simulation model

• Equations of motion based on multibody dynamics (nonlinear flight dynamics model)

• Selective fidelity (range of sub models is available)

Step 3 – Virtual flight test

• Trim and handling qualities analysis for range of flight conditions

Sample results • Trim attitudes and control settings including crosswind / engine out • Short period, Phugoid, Dutch roll, Spiral, Roll mode • Push-pull manoeuvre • Roll manoeuvre • Gibson criterion • Maximum pitch acceleration (take-off) • Response to turbulence and gusts

Example time domain simulation

Step 4 – Piloted simulation

Tex Johnson, Chief test pilot, Boeing

Movies

• Pitch maneuvers: – Angle of attack – Pitch rate – Pilot input (roll)

• Roll maneuvers: – Roll rate – Pilot input (roll) – Pilot input (pitch)

• Rollover: – Roll rate – Load factor – Pilot input (roll)

Questions ?

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