10 good reasons to go for model-based systems engineering in your organization
TRANSCRIPT
10 good reasons to go MBSE
in your organization Renaud Meillier
Business Development Director
Realize innovation. Unrestricted © Siemens AG 2016
Unrestricted © Siemens AG 2016
Page 2 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.]
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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
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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
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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
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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
Product Engineering must evolve
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Till facts be grouped and called there can
be no prediction
Charles Darwin
Species Notebook
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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
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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
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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
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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
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Introducing Simcenter™ Portfolio for Predictive Engineering Analytics
Simcenter™
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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
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Simcenter™ Portfolio for Predictive Engineering Analytics
LMS Imagine.Lab
LMS Imagine.Lab Amesim
LMS
Imagine.Lab
System
Synthesis
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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
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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
Industry IS adopting
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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
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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
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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
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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
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
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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
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1
2
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
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
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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%
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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
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Page 26 Siemens PLM Software
3
1
2
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 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
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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
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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
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Page 29 Siemens PLM Software
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
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The 10 good reasons to go for MBSE (1)
Facilitate
communication Improve
quality
Enable greater
innovation Increase
productivity
Reduce
design
risks
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Page 31 Siemens PLM Software
The 10 good reasons to go for MBSE (2)
Cover all
engineering levels
Preserve
knowledge
Enable
collaboration Reduce development
times & costs
Provides
interoperability
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Page 32 Siemens PLM Software
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