10 good reasons to go for model-based systems engineering in your organization
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This presentation explains the concepts of model-based systems engineering and introduces the LMS Imagine.Lab platform and how it supports the mbse implementation. For more information, please visit our website: www.siemens.com/plm/simcenter-amesim
![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.]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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10 good reasons to go for model-based systems engineering in your organization
- 1. 10 good reasons to go MBSE in your organization Renaud Meillier Business Development Director Realize innovation.Unrestricted © Siemens AG 2016
- 2. 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.]
- 3. Unrestricted © Siemens AG 2016 Page 3 Siemens PLM Software 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
- 4. Unrestricted © Siemens AG 2016 Page 4 Siemens PLM Software 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
- 5. Unrestricted © Siemens AG 2016 Page 5 Siemens PLM Software 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
- 6. Unrestricted © Siemens AG 2016 Page 6 Siemens PLM Software 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
- 7. Unrestricted © Siemens AG 2016 Page 7 Siemens PLM Software Till facts be grouped and called there can be no prediction Charles Darwin Species Notebook
- 8. Unrestricted © Siemens AG 2016 Page 8 Siemens PLM Software 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
- 9. Unrestricted © Siemens AG 2016 Page 9 Siemens PLM Software Market leading value proposition From disconnected models and data … Usage dataUsage data 3D SIMULATION TEST MODELING CONTROLS Benchmark dataBenchmark data Analysis dataAnalysis data Test dataTest data CFD 1D SIMULATION
- 10. Unrestricted © Siemens AG 2016 Page 10 Siemens PLM Software Analysis dataAnalysis data TEST MODELING Market leading value proposition To the “Digital Twin” … Integrating across simulation and test domains, models & data 1D SIMULATION Benchmark dataBenchmark data 3D SIMULATION Usage dataUsage data CONTROLS Test dataTest data CFD DIGITAL TWIN
- 11. Unrestricted © Siemens AG 2016 Page 11 Siemens PLM Software SYSTEMS DRIVEN PRODUCT DEVELOPMENT Simulation & Test Solutions (STS) business focus Enabling verification and validation in the age of system engineering PREDICTIVE ENGINEERING ANALYTICSSYSTEM MOCK-UP MULTI-DOMAIN TRACEABILITY, CHANGE AND CONFIGURATION 3D TEST ANALYTICS - REPORTING Digital twin VERIFICATION & VALIDATION 1D CONTROLSCFD
- 12. Unrestricted © Siemens AG 2016 Page 12 Siemens PLM Software Introducing Simcenter™ Portfolio for Predictive Engineering Analytics Simcenter™
- 13. Unrestricted © Siemens AG 2016 Page 13 Siemens PLM Software 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
- 14. Unrestricted © Siemens AG 2016 Page 14 Siemens PLM Software Simcenter™ Portfolio for Predictive Engineering Analytics LMS Imagine.Lab LMS Imagine.Lab Amesim LMS Imagine.Lab System Synthesis
- 15. Unrestricted © Siemens AG 2016 Page 15 Siemens PLM Software ConfigurationSimulation 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
- 16. Unrestricted © Siemens AG 2016 Page 16 Siemens PLM Software 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
- 17. Unrestricted © Siemens AG 2016 Page 17 Siemens PLM Software Frontloading the controls development process Virtual calibration to frontload full vehicle calibration Calibration - Validation Controls Modifications 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
- 18. Unrestricted © Siemens AG 2016 Page 18 Siemens PLM Software Frontloading the controls development process Virtual calibration to frontload full vehicle calibration Calibration - Validation Controls Modifications 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
- 19. Renault deploys model-based development for powertrain control
- 20. Unrestricted © Siemens AG 2016 Page 20 Siemens PLM Software 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 calibration1 2 3 4 5 6 Model-based development for powertrain control at Renault Enabled by scalable behavioral models and real-time
- 21. Unrestricted © Siemens AG 2016 Page 21 Siemens PLM Software 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 calibration1 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
- 22. Unrestricted © Siemens AG 2016 Page 22 Siemens PLM Software Choice of architecture and sensors/actuators Conception of controls strategy & early evaluation of reliability 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? HP EGR valve failure 1 2 0 2000 4000 6000 8000 10000 12000 14000 0 1000 2000 3000 4000 5000 NOxcum[mg] 0% 20% 25% 30%
- 23. Unrestricted © Siemens AG 2016 Page 23 Siemens PLM Software 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 14000 0 1000 2000 3000 4000 5000 NOxcum[mg] 0% 20% 25% 30% HP EGR valve failure Impact on NOx different level of failure 1 2
- 24. Unrestricted © Siemens AG 2016 Page 24 Siemens PLM Software 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 61 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
- 25. Unrestricted © Siemens AG 2016 Page 25 Siemens PLM Software MiL modeling for functional validation of the complete controller Complete powertrain plant model for closed loop control algorithm prototyping 3
- 26. Unrestricted © Siemens AG 2016 Page 26 Siemens PLM Software 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
- 27. Unrestricted © Siemens AG 2016 Page 27 Siemens PLM Software 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
- 28. Unrestricted © Siemens AG 2016 Page 28 Siemens PLM Software 0 20 40 60 80 100 120 0 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 80 100 120 0 0.02 0.04 0.06 0.08 0.1 Qakgs Accuracy +/- 6% 0 20 40 60 80 100 120 0 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%
- 29. Unrestricted © Siemens AG 2016 Page 29 Siemens PLM Software 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0 3 0 0 0 3 5 0 0 4 0 0 0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 N [ t r/ m in ] Couple[N.m] P h a s a g e M a in [ d e g ] -2 0 2 4 6 8 1 0 1 2 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
- 30. Unrestricted © Siemens AG 2016 Page 30 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
- 31. Unrestricted © Siemens AG 2016 Page 31 Siemens PLM Software 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 analysisBattery behavior simulation • Delivered high-quality product on- time and with reasonable costs • Created flexible development platform to support future projects • Shortened time-to-market
- 32. Unrestricted © Siemens AG 2016 Page 32 Siemens PLM Software 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
- 33. Unrestricted © Siemens AG 2016 Page 33 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 MotionModel in LMS Imagine.Lab Amesim
- 34. Unrestricted © Siemens AG 2016 Page 34 Siemens PLM Software The 10 good reasons to go for MBSE (1) Facilitate communication Improve quality Enable greater innovation Increase productivity Reduce design risks
- 35. Unrestricted © Siemens AG 2016 Page 35 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
- 36. Unrestricted © Siemens AG 2016 Page 36 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
Editor's Notes
- Good morning to all of you. This is my great pleasure to be here with you. Today, I’ll talk about how we Siemens and our customers are willing to tackle current and future challenges of Model Based Systems Engineering.
- Darwin may not have said it exactly like this but he was to the point really when saying that the one that survives is the one that is able to change …
- And believe me or not, change is happening in the industry.
- Let us consider the Automotive industry context: We as individuals are requiring powerful cars but clean and cheap in terms of fuel consumption. We also want these cars to be safe, robust, comfortable, more autonomous. Because OEMs and suppliers want us to be satisfied, because regulation policies are more and more strict, they need to reflect upon ways to design quicker and more efficiently such ideal cars.
- What does this mean in terms of Product Development ? Being able to cope with growing complexity of electronic systems and interaction with the physics, Managing increasing number of requirements and use cases, Innovating with new types of architectures, managing the ever increasing number of vehicle variants. And all of this in an international and multi-cultural work environment …. Definitely …
- Can Darwin help us again here ?
- Over the last 30 years, product engineering has evolved. To start, requirements management has developed from ad-hoc and paper-based methods to a managed approach within PLM systems. Product representation has evolved from drafting to full digital mock-ups of the product assembly. Performance verification has evolved from a build-and-break approach to the current practice that includes significant CAE work as well as test. But with the complexity trends, engineering must evolve again. Product representations must evolve to full system mock-ups that cover not just mechanical but also electrical, software, and controls representations. Product performance engineering must evolve to be more predictive so it can drive the creation of the design and the system representation. This is represented in the arrows under the Product Representation row. And these must be fully integrated into an overall PLM system to ensure we can close the loop from requirements to as-designed behavior and beyond to manufacturing and usage.
- Today engineering data and models are scattered in different silos No ability to easily draw inferences across these silos. This must change in order to meet the new demands of today’s products.
- When we take all these different silos of models and data and link them together, we create the digital twin. The digital twin includes simulation models and data that cover different types of behaviors. It has models that are relevant to different stages of the development process. Data from one model may be used as inputs to a different model that is built later in the process. The models themselves can be matured over time.
- Let me illustrate this slide with the example of ADAS (Advance Driver Assistance Systems). Car passengers want cars that are safer and safer. They require emergency braking features, active cruise control, … Do you know how many kilometers a car has to be driven in order to validate an ACC with tests ? More than 1 million km. Meaning that at the start of the product design process, there are many requirements to be fulfilled that translates into functions and some logics to control these functions. If you combine this with later the CAD representation of the product you end-up with what we call the System Mock-up. From this Mock-up, you are going to create analysis requests to check that requirements are verified. This analysis can be performed based on Simulation (CFD, 3D, 1D) but also test. Once you have checked that requirements are verified and that the analysis performed shows no major issues with the performance of the subsystems involved, you have closed the loop. This is the System Driven Product Development Iterative process.
- Simcenter is the Siemens software brand for addressing Predictive Engineering Analytics. The Simcenter portfolio consists of solutions that span 3D simulation, 1D simulation, and testing solutions. It is comprised of a number of well known products such as NX Nastran, STAR-CCM+, LMS Imagine.Lab and LMS Test.Lab. Additionally, it includes Simcenter 3D, our next generation 3D CAE solution that is based on the NX platform and expands on the heritage of NX CAE, LMS Virtual.Lab, NX Nastran and LMS Samtech. The portfolio covers a number of disciplines as represented in the spokes of the inner circle and the solutions are further enhanced with multi-discipline design exploration capabilities. Finally, the solutions are available with integrations to Teamcenter to enable data and process management and complete traceability within the PLM ecosystem.
- To fully realize the Predictive Engineering Analytics vision, we strongly believe that the following 9 cornerstones need to be implemented and further developed to future-proof engineering approaches. [1] The portfolio should support the full development cycle and different technology domains. [2] A multi-disciplinary and multi-physics approach is crucial to receive the required fidelity level to predict phenomenon involving different physics domains. [3] It should embed technology to capture specific industry challenges and include dedicated post-processing capabilities. The portfolio can be augmented with engineering services and technology transfers. [4] Multi-disciplinary design exploration, data mining and optimization provides strong visualization capabilities to communicate insights. [5] A systems approach captures the relationships between the requirements, functional layout, logical implementation and physical implementation. Users can assess how changes at one level impact decisions on another level. This will enable traceability and cascading of key parameters throughout the lifecycle. [6] The portfolio provides flexible licensing mechanisms including tokens and cloud-based options to limit infrastructure hassle. [7] It should be open, taking into account that companies always have a variety of in-house tools, either for legacy reasons or to benefit from the specific strengths of certain tools. [8] Collaborative workflow means that development is interconnected. Sharing data, both internally and externally with suppliers is crucial. At the same time, collaborative workflows support traceability to ensure quality, legal compliancy and improve operational efficiency. [9] The portfolio provides a user experience to maximize ease of use and minimize the learning curve.
- While the previous solutions are heavily dependent on available geometry, LMS Imagine.Lab 1D does systems modeling in a different way. The approach is in theory quite simple. By creating 1D models based on physical equations, one can create a library of components with validated physical behavior. These models can be easily clicked together into a complete system model. The physics embedded in the components can be used to calculate how the energy will flow from one component to another, and how this energy is converted into heat, motion, or a chemical reaction. These 1D models can also be used for 3D co-simulation and control strategy validation.
- Let me show how the digital twin strategy is applied on real cases. I will illustrate adption on 3 different cases: Controls/Energy Management and Attribute Balancing as well as new challenges in driving dynamics performance.
- Traditional mechatronic development - most of controls validation done @ end of dev cycle - expensive in terms of time spent Industry moving to Model based controls engineering that is to say: - Using Plant connected to control models for virtual calibration before prototyping, - Implies Having the possibility to still have an impact on the product design Cheaper alternative.
- Renault has applied our tools and methodologies to support MBD for Powertrain and controls.
- How did Renault Proceed? At any stage of the mechatronics Design Cycle, they used scalable model or what we call the proper plant + control model level to reduce drastically the costs for controls validation.
- At early design and architectural choices stage by combining a model of the plant and its high level control, they were able to answer to questions like: With these actuators and these sensors, can we satisfy on-board diagnosis requirements (Nox leakage detections and failure strategies)
- At the end of phase 1 and 2, they were able to develop optimal control, select best architecture in 1-month. If you compare to the past where 10 prototypes were needed, gain is huge.
- In previous stage, Renault defined the optimal architecture in terms of sensors and actuators valid for different regulation policies. In next stage, they are prototyping the control algorithm and closed loop testing them with a refined model.
- With this strategy, Renault was able to simulate 6 millinon km in a few days and perform 80% of the control software validation.
- In the last phases, Renault validates the implementation of the complete SW inside the real HW: Export Plant on RT targets + connection to ECU HW via I/O boards. On top of this an automation test environment will help script the test execution.
- Finally, Renault initiated the first step of virtual calibration. The idea is to reduce the amount of HW DoE by tuniing High Fidelity model with a reduced amount of test data (orange data). The model is used to perform virtual DoE with parallel processing. Tuning quality Complexity handling R&D cost
- Use of simulation enabled Renault to pre-calibrate 20% of the 20000 controls parameters !
- Renault GREEN case study: http://www.plm.automation.siemens.com/CaseStudyWeb/dispatch/viewResource.html?resourceId=50650
- IRKUT case study: http://www.plm.automation.siemens.com/CaseStudyWeb/dispatch/viewResource.html?resourceId=40548
- Liebherr Group (Complex Excavators) case study: http://www.plm.automation.siemens.com/CaseStudyWeb/dispatch/viewResource.html?resourceId=35349
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