10 good reasons to go for model-based systems engineering in your organization

10 good reasons to go MBSE

in your organization Renaud Meillier

Business Development Director

Realize innovation. Unrestricted © Siemens AG 2016

Unrestricted © Siemens AG 2016

Siemens PLM Software

It is not the strongest of the species that survives,

nor the most intelligent that survives.

It is the one that is

MOST ADAPTABLE TO CHANGE

[Modern paraphrase; Darwin never wrote with these words.]



YOUR CAE DEPARTMENT WILL ONLY REMAIN RELEVANT IN THE

FUTURE IF ITS ABLE

• TO ACCURATELY MODEL SYSTEMS BEHAVIOR WITH DIGITAL TWINS THAT ARE

• As close to reality as possible

• Cover all critical performance characteristics

• Evolve over time to remain in-sync with the product and its’ operating environment

• BECOME PREDICTIVE AND DRIVE DESIGN DECISIONS

• Use analytics to deliver new insights

• Provide results in time with the design cycle

Change is Happening



A challenging agenda ...

Balancing CO2 emissions and brand performance

Global fuel economy & emission regulations drive major speed of change

Maximize propulsion efficiencies Innovative lightweight designs - new materials

Brand value through mechatronic systems Brand value through performance



A challenging agenda ...

Mastering product development complexity

0

50

100

150

2000 2010 2015

Cost of Software

Dramatic Growth of Electronics Systems Exploding Requirements and Test Cases

Multiple Variants and System Architectures Multiple Sites, Multiple Participants

€25b

€95b

€126b




FUTURE IF ITS ABLE








Product Engineering must evolve



Till facts be grouped and called there can

be no prediction

Charles Darwin

Species Notebook



Evolution of product engineering

Digital Mockup

CAE & Test

Managed

Product

Drafting

Requirements

Performance

Paper-based

Physical Test

Richer

System Mock-up

Digital Twin

+ Predictive

Integrated



Market leading value proposition

From disconnected models and data …

Usage data

3D SIMULATION

TEST

MODELING

CONTROLS

Benchmark data

Analysis data

Test data

CFD

1D SIMULATION



Analysis data TEST

MODELING

Market leading value proposition

To the “Digital Twin” … Integrating across simulation and test domains, models & data

1D SIMULATION

Benchmark data

3D SIMULATION

Usage data

CONTROLS

Test data

CFD

DIGITAL TWIN



SYSTEMS DRIVEN PRODUCT DEVELOPMENT

Simulation & Test Solutions (STS) business focus

Enabling verification and validation in the age of system engineering

PREDICTIVE ENGINEERING ANALYTICS SYSTEM MOCK-UP

MULTI-DOMAIN TRACEABILITY, CHANGE AND CONFIGURATION

3D TEST

ANALYTICS - REPORTING

Digital

twin

VERIFICATION & VALIDATION

1D CONTROLS CFD



Introducing Simcenter™ Portfolio for Predictive Engineering Analytics

Simcenter™



Cloud

Licensing

flexibility

Simcenter™ Portfolio for Predictive Engineering Analytics

Cornerstones for a future-proof engineering approach

Covering full range of

methods

Analytics, reporting &

exploration

Deployment flexibility Openness &

Scalability User experience

Industry &

engineering expertise Systems approach

Collaboration &

workflow

Multidiscipline

& multiphysics

R

F

L

P

Controls

1D

3D

TEST

CFD



Simcenter™ Portfolio for Predictive Engineering Analytics

LMS Imagine.Lab

LMS Imagine.Lab Amesim

LMS

Imagine.Lab

System

Synthesis



Configuration Simulation Architecture

Deployment of

System Engineering

LMS Imagine.Lab

Product suite & positioning in Systems Engineering

Product Life Management

Stand Alone

or PLM Plugin

Functional

Architecture

LMS Imagine.Lab Amesim

Other CAE Disciplines

Engine Specialist

Chassis Specialist Controls Specialist

Transmission Specialist

LMS Imagine.Lab System Synthesis

Requirements

Functions

Logical

Physical

PLM platform




FUTURE IF ITS ABLE








Industry IS adopting



Frontloading the controls development process

Virtual calibration to frontload full vehicle calibration

Calibration - Validation

Co

ntr

ols

Mo

dif

icati

on

s

Physical

Prototypes

Available

Algorithm Dev. SW Dev. SW Ver.

Traditional Controls Development In Vehicle Full

Calibration

Calibration

Validation Algorithm Dev. SW Dev. SW Ver. Virtual Calibration

Model Based Controls Engineering Selective In-

Vehicle Final

Calibration

Early enough

to impact physical design

Shortening in-vehicle

calibration

Renault deploys model-based

development for powertrain control



Automatic code

generation

Scalable

behavioral

models

Architecture choice Understanding of physics

Definition of sensor / actuator

(Dys)functional analysis

Reliability & safety

Requirements for

control Functional / dysfunctional

Control synthesis Virtual sensors

Executable specifications

MiL validation

First settings

Functional MiL

validation Simulation module or

complete controls

HiL validation Verification & validation

Tuning level 1 First calibration step

Tuning support Final calibration 1

2

3 4

5

6

One platform needed

across full development cycle

Model-based development for powertrain control at Renault

Enabled by scalable behavioral models and real-time



Choice of architecture and sensors/actuators

Conception of controls strategy & early evaluation of reliability

Q4 : what is the risk on air path and after treatment control

of an exhaust temperature sensor failure ? In this case,

can I estimate a value to replace the measured signal.

Q2 : with a dual loop EGR, can I estimate the EGR flow of both circuits?

And can I use the air mass flow sensor to control the two loops ?

Q3 : what is the severity level of an intake throttle failure?

No impact / risk on air path control / risk on pollutants

emissions / risk to stall the engine / risk for the safety?

Q1 : on two stage turbochargers can I control the boost

pressure with only one intake pressure sensor? should I

introduce an additional sensor between the two compressors?

0 2000 4000 6000 8000 10000 12000 140000

1000

2000

3000

4000

5000

temps [sec] *10

NO

x c

um

[m

g]

Essais

0 2000 4000 6000 8000 10000 12000 140000

1000

2000

3000

4000

5000

temps [sec] *10

NO

x c

um

[m

g]

Modèle

0%

20%

25%

30%

0%

20%

25%

30%

Blocage de la vanne EGR hpHP EGR valve failure

Impact on NOx

different level

of failure

1

2



Automatic code

generation

Scalable

behavioral

models





Requirements for




MiL validation

First settings

Functional MiL


complete controls



Tuning support Final calibration

3 4

5

6 1

2

Architecture choice Requirements engineering, link with systems modeling

Software design How to develop diesel engine software by applying an

MPC (Model Predictive Control) approach supported by

an LMS Amesim model

• Develop almost optimal

controls in a few days

• Select the best

architecture in 1 month

instead of 10 prototypes

Model-based development for powertrain control at Renault

Enabled by scalable behavioral models and real-time



MiL modeling for functional validation of the complete controller

Complete powertrain plant model for closed loop control algorithm prototyping

HF Engine physical model

(crank angle degree resolution)

Automatic

transmission

(6 gears)

Longitudinal 2D vehicle

carbody

Driver and mission profile

Simulink interface

Simulink interface

3



1

2

Automatic code

generation

Scalable

behavioral

models





Requirements for




MiL validation

First settings

Functional MiL


complete controls



Tuning support Final calibration

4

5

6

Software validation (MiL) Model-in-the-loop (MiL) validation of hybrid

vehicle controls software to check if

specifications have been met

• 6 millions kilometers in

few days

• 80% of the validation with

models

3

Powertrain controls engineering

MBSE supporting control development process



0 20 40 60 80 100 1200

0.5

1

1.5

Pcol

Accuracy +/- 5%

0 20 40 60 80 100 120-50

0

50

100

150

200

Couple

Accuracy +/- 6 N.m

0 20 40 60 80 100 1200

0.02

0.04

0.06

0.08

0.1

Qakgs

Accuracy +/- 6%

0 20 40 60 80 100 1200

10

20

30

40

50

Qekgs

Accuracy +/- 5%

ECU validation (HiL)

4

Plant model

EXPORT

Control model

RT INTEGRATION HiL test bench

Remote

access to HiL

systems in

Romania

TEST AUTOMATION

Torque +/-6%

Intake

Pressure +/-

5%

AirFlow

+/-6%

Injected fuel

+/-6%



1000 1500 2000 2500 3000 3500 4000

50

100

150

200

250

300

N [tr/min]

Cou

ple

[N.m

]

Phasage Main [deg]

-2

0

2

4

6

8

10

12

N PMEBR NOX FUMBO HCHU CODIES

1750 3,00 56,83 1,31 285,90 964,80

1750 3,00 55,22 1,23 299,20 1038,00

1750 2,99 31,02 2,68 593,30 2090,00

1750 3,00 188,16 0,27 118,80 400,60

1750 3,02 35,74 2,32 664,40 1895,00

1750 3,01 53,69 0,49 389,00 1023,00

1750 2,98 54,77 0,27 417,30 2099,00

1750 2,99 152,73 0,49 275,80 828,40

1750 3,02 69,06 1,65 443,60 1126,00

1750 2,99 95,92 0,41 339,00 806,20

1750 2,98 71,82 1,52 281,40 609,40

1750 3,00 36,28 1,35 424,60 1066,00

1750 2,99 43,50 0,28 423,40 1069,00

1750 2,99 72,52 0,39 440,30 1846,00

Off-line virtual pre-calibration

Plant model

EXPORT MODEL

IDENTIFICATION

CONCATENATION OF

REAL & VIRTUAL

DATA SETS

USUAL

OPTIMIZATION

PROCESS

RUN DOE ON

VIRTUAL ENGINE

5



3

1

2

Automatic code

generation

Scalable

behavioral

models





Requirements for




MiL validation

First settings

Functional MiL


complete controls



Tuning support Final calibration 6

Software validation (HiL) How to check the quality of controls codes once

integrated into the ECU

• 20,000 parameters

• 20% of the calibration

done by simulation

5

4

Calibration and tuning How to use LMS Amesim models to pre-calibrate

controls software parameters

Powertrain controls engineering

MBSE supporting control development process



Operating complex multi-domain analyses

Renault

Reaching high energy savings in hybrid vehicles using LMS Imagine.Lab Amesim

“LMS Imagine.Lab Amesim enables us to get a deep insight on energy

performance of hybrid architectures and helps us select optimal architectures that

fit our requirements early in the design process.” Eric Chauvelier, Method and Simulation Manager

• Facilitate communication and decision-making thanks to a common platform

• Implement co-simulations to assess the energy synthesis of any hybrid configuration

Internal combustion engine analysis Battery behavior simulation

• Delivered high-quality product on-

time and with reasonable costs

• Created flexible development

platform to support future projects

• Shortened time-to-market



IRKUT

Building virtual integrated aircraft using LMS Imagine.Lab Amesim

Predicting system behavior once integrated into aircraft

• Reduced modeling time by a factor

of 5

• Enhanced model, architecture and

configuration management “…LMS Amesim allows us to reduce time spent in building our most complex

models by a factor of 5.”

Marina Grishina, Engineering and Simulation Engineer

• Minimize the number of errors discovered at the verification phase

• Obtain optimal design within the shortest timeline

Hydraulic system analysis Virtual integrated aircraft



Combined simulation of excavator dynamic behavior

Liebherr Group

Stepping beyond prototyping with LMS Imagine.Lab and LMS Virtual.Lab

• Analyzed behavior of subsystem

without building expensive

prototype

• Determined best possible design to

avoid backlash and reliability issues

• Saved time and money, helping to

maintain Liebherr strong

competitiveness

“The design table functionality is extremely helpful for changing the mechanical

system very easily and quickly using LMS Virtual.Lab Motion.”

Martin Bueche, Head of Calculation and Simulation Department

• Use LMS Imagine.Lab Amesim™ together with LMS Virtual.Lab™ Motion

• Simulate several system versions, including diverse mechanical systems

Visualization in LMS Virtual.Lab Motion Model in LMS Imagine.Lab Amesim



The 10 good reasons to go for MBSE (1)

Facilitate

communication Improve

quality

Enable greater

innovation Increase

productivity

Reduce

design

risks



The 10 good reasons to go for MBSE (2)

Cover all

engineering levels

Preserve

knowledge

Enable

collaboration Reduce development

times & costs

Provides

interoperability



Contact

Renaud MEILLIER

1D Simulation Solutions

Siemens Industry Software S.A.S.

Digital Factory Division

Product Lifecycle Management

Simulation & Test Solutions

DF PL STS CAE 1D

siemens.com