CosiMate + Saber Multi Physic analysis for validation of vehicle platform

CosiMate + Saber Multi Physic analysis for validation of vehicle platform 7 th April 2016 Detroit Marriott Troy

Agenda Introduction Chiastek and Cosimulation(5 ) FMI : an open standard for model exchange (5 ) Monte Carlo Analysis for the Validation of Modern Automotive Vehicle Platforms (10 ) DOE with SaberRD and FMU s (10 ) Table top demo aside

Chiastek CosiMate Clients Chiastek : Company Profile SMB, privately held, headquarter in Toulouse, France Chiastek Inc (Chicago), Chiastek GmbH (Dusseldorf) Distributors in Japan (AZAPA), China (Get Technology, Greatalent), Korea (Dahan Tech) VAR/Partners : Altran, DPS in France and US Current release : CosiMate 2014.12 (v8.1) 250 + licenses, 150+ active users Worldwide customer support Boeing, Airbus (F, Ge, Ind,) Altran, Safran (Hispano-Suiza) GM, PSA, Toyota, Denso, Continental, OPEL Ge, Hitachi Automotive, Mazda Parker, Hydro-Quebec, EDF, Nikon, Hitachi 3

What is CosiMate? CosiMate is a simulation framework based on an open bus architecture to support multiphysic simulation at all level of abstraction. CosiMate is part of the System Engineering solution - own process and data management capabilities (archiving, versioning, DOE.) - easily instantiated in a SLM/PDM tool (Autonomie, Sysdm, System Synthesis, adhoc)

What is CosiMate? Complete co-simulation framework Development platform Supports native and non native simulation environment Test platform Integrates test & measurement tools (e.g. Labview, LabWindows/CVI) C/C++ debuggers/monitors (Eclipse) Verification platform Supports co-simulation between different abstraction levels Non regression of model functionality along the design flow

CosiMate is an open multiphysic platform Supports all field of simulation thanks to various coupling: Coupling standards : FMI (1.0 and 2.0), DIS, HLA 1D : AMEsim, Dymola, Easy5, GT-SUITE, KULI, ModelSim (MG), Saber, Simulink, OpenModelica,... 3D : Inventor, IDEAS/NX-TMG, Vlab.Motion, Adams, Nastran, Ansys MP CARSIM, Rhapsody, Virtualizer, EMTP-RV, PSIM, C/C++, SIL Allows modeler to work in native environment No need for translation or DLL Instances can be Black Box Easily instantiated within Process Management tool Autonomie, SysDM ModelCenter

FMI Overview The FMI development is part of the ITEA2 MODELISAR project (2008-2011; 29 partners, Budget: 30 Mill. ) FMI development initiated, organized and headed by Daimler AG Improved Software/Model/Hardware-in-the-Loop Simulation, of physical models from different vendors. Open Standard 14 Automotive Use-Cases to evaluate FMI. etc. Engine with ECU Gearbox with ECU Thermal systems Automated cargo door Chassis components, roadway, ECU (e.g. ESP) functional mockup interface for model exchange and tool coupling courtesy Daimler

CosiMate supports FMI The first version, FMI 1.0, was published in 2010 Version 2.0 was released in early 2015. Compliance tests: CosiMate is currently executing all tests of Compliance Checker 2.0 MVS does work with FMU Committed to 2.1, 3.x and after 10

Chiastek and FMI Committed to 2.1, 3.x and after We participate in FMI meetings There are some limitations will need more independent standardization (IEEE, OMG?) FMI is a virtual integration capability FMI is good solution for IP protection Can be used for deployment

Why Multiphysic Simulation Multiphysic To validate a full system made of several components (Hydraulic, electrical, mechanical, etc ) Simulation To avoid prototype To manage scenarios for system optimization» Explore infinite number of case scenarios» Complete study in hours instead of months

DOE and Multiphysic Design exploration Iterative method Monte carlo, What if studies, etc Parametric analysis available with simulation tool (Saber, Amesim, etc ) Design optimization Formal or semi formal method Requires specialty tool (Modelcenter, Isight, etc ) Easy to implement on CosiMate platform

Leveraging Cosimulation between Saber and Simulink in conjunction with Monte Carlo Analysis for the Validation of Modern Automotive Vehicle Platforms Raphaël COMTE Engineer Automotive Electrical Architecture Modeling

4500 4000 3500 3000 2500 2000 1500 1000 500 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Introduction For Stop and Start application Develop a new collaborative platform for sizing devices using SABER and SIMULINK Monte Carlo analyses : Set up a cosimulation process Associated challenges With SABER : Set up a new approach of design by using the cosimulation Produce a mock up with Monte Carlo approach in cosimulation Set up the associated tools and processes Systems : Engine with Starter (Simulink) Electrical Power System (SABER) Methodogies : Worst Case sizing Approach Oversize devices Additional constraints on the EE devices : feasability risk Higher costs, weights for very few cases Systems Sizing Methodologies Goals : Guarantee Engine services and Electrical constraints Study the impacts of the Stop&Start function on the other services Statistical sizing approach Risk : Quantify levels Determine the number of cases failing to reach a working threshold (PPM device) Reach an agreement with the device-specialized teams on tolerable and quantified risks Possible approach : MONTE CARLO analysis

Quality associated with the Onboard Electrical Network Numerical solutions Goals : Guarantee Engine and Electrical services Full SABER RTW import MONTE CARLO Analysis Modeling Simulink system in SABER Time development and validation No import of Simulink S-function Full MATLAB Modeling electrical Power System (SABER) in SIMULINK Time development and validation No import of Saber s Model No MONTE CARLO Analysis SABER <-> SIMULINK Co Simulation SABER <-> SIMULINK with SaberSimulinkCosim Master simulator : SABER Local HOST Simulink : Model Size limited SABER <-> SIMULINK with COSIMATE Master simulator : SABER OR SIMULINK OR COSIMATE Local HOST or NETWORK Simulink : Model Size > 30Mb An additional tool : COSIMATE Possible approach : MONTE CARLO analysis using cosimulation between SABER and SIMULINK / Simulink Stakes linked with the Cosimulation use in Monte Carlo analysis

CosiMate : Standard use at PSA personalised batch from each simulator «Standard Use» Launch platform from each simulator (local or network) collaborative Platform Platform Simulink 1 Simulink2 For each simulator : Reuse existing batch Saber (manual or automatic run) manual Change models parameters automatic Each user can exploit the platform from his simulator and with his own tools Minimize cosimulation s impacts on the platform s exploitation Need to know simulators languages

Summary Quality Closed Loop System : Multi Monte Carlo Analysis with a minimum of two simulator in cosimulation (local or network) One result for 2 systems Cost Reduce cost development Re-use methodology for another platform Schedule According to the models and method : Increazing time simulated Performances Size of results / possibility post-processing Model & analysis : Facilitate creation or modification for non-expert users of SABER or SIMULINK Low impact for users

Design of Experiment and Co-Simulation A SaberRD-CosiMate application Chiastek

CosiMate for DOE : a real life example «Standard Use» Launch platform from each simulator (local or network) collaborative Platform Platform Amesim/FMU Amesim/FMU For each simulator : Reuse existing batch SaberRD (manual or automatic run) manual Change models parameters automatic Optimize services according to constraints Impact studies

Purpose of this demo Workflow : 1. Show SaberRD capabilities when designing components using the Experiment Analyzer 2. Use of co-simulation with multi-physics transient simulation with CosiMate

SaberRD-CosiMate use SaberRD is the master of cosimulation Several type of analysis Sensitivity analysis Vary analysis Monte carlo analysis Generate/integrate FMU s

Purpose of this study (2) Use case : Suspension parameter design example 1. What are the critical parameters to be monitored in this design? 2. Design parameters of the simulation

1. Sensitivity Analysis From given values for customizable parameters in the design, find what parameters have the strongest influence on the design objectives (overshoot and rise time) Sensitivity analysis (parameters) Transient analysis Measurements

1. Sensitivity Analysis Parameters: 2 masses and 2 spring stiffnesses Design parameter stiffness k (spring 1)

2. Vary Analysis Determine an order of magnitude of the design parameter Parameters to measure Test : comparison to specifications Report Graphical outputs Curves and measurements

2. Vary Analysis Report Overall completion Unit test Graph results Choice: k1 = 5000 N/m

3. Monte Carlo Analysis Assess the impact of risk under uncertainty! Criteria: rise time and overshoot Parameters: both masses and springs stiffnesses. Nominal values +/- 10% Parameters and uncertainty Test : comparison to specifications Report Graphical outputs Curves and measurements

3. Monte Carlo Analysis Completed with failure : xx out of 100 tests failed Compare to requirements Report Parameters and discrepancy Graph results

4. FMU Integration Create a new component

4. FMU Integration The new component has to be connected Simple of use for a few variables design only Connection with native simulator is made simple with Chiastek s MVS Tool for complex design with many inputs/outputs

4. FMU Integration Workflow becomes : 1. Validate process using native simulator 2. Transform model into FMU (IP) 3. Validate process using FMU 4. Share your model Starting simulation with the native simulator is easier. Since the FMU is a «black box», one cannot modify it if something goes wrong