# SIXTH FRAMEWORK PROGRAMME PRIORITY [6

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1 Key technology : Confidence E. Fournier J.M. Crepel - Validation and certification - QA procedure, - standardisation - Correlation with physical tests - Uncertainty, robustness - How to eliminate a gateway - Certification of engineers, training

2 Key technology : Confidence E. Fournier J.M. Crepel - Validation and certification - QA procedure, - standardisation - Correlation with physical tests - Uncertainty, robustness - How to eliminate a gateway - Certification of engineers, training Initial motivation : - How to save money using CAE? (back to design process)

3 Copyright : Renault 2005 Auteur : J.M. CREPEL /DDIV/DCRPV Renault - JM. Crepel - Oct 05 DCRPV Ingénierie Véhicule

4 Copyright : Renault 2005 Auteur : J.M. CREPEL /DDIV/DCRPV Developed in 28.5 months, with only 1 set of prototypes, built with production tooling -20 M Renault - JM. Crepel - Oct 05 DCRPV Ingénierie Véhicule

5 Reduced design time 90 Prototype lot >3 Style selection SOP 40 to 50 months to 30 months

6 Driving changes toward digital design process - Developing simulation knowledge - Introducing Digital Mockup and Performance review - Reducing number of prototypes lots Confidence in in simulation remains the basic point for for driving changes «Nobody believes simulation results except the the one one who who did did the the simulation and everybody believes the the test test results except the the one one who who did did them»

7 How far to trust CAE? A step-by-step process : Renault experience JM CREPEL & E FOURNIER, RENAULT (France)

8 CAE Process : in order to describe a physical phenomenon Mathematical models and differential equations Input Data Results

9 Software (1/2) : tool for conception for engineers Given a mathematical model and equations Imprecision due to software and algorithm / numerical method : Algorithm : choice of approximation (1st order, 2d order ) Stability Convergence Gap control Confidence in an algorithm / numerical method

10 Software (2/2) Given a appropriated algorithm Software validation Analytical solution Validation on academic cases / test cases Validity domain for software and accuracy evaluation Benchmark between different software Confidence in a software describing the physical phenomenon Gap between CAE and reality under control

11 CAE Process : software and hardware association Input Data Results Hardware

12 Hardware / Software Given a appropriated software Quality insurance Current status in software vendors Functionality tests acceptance Hardware / Software integration Implementation tests Evolution of material and software : Configuration management Test of non-regressions

13 Which confidence for which decision? Physical phenomenon Methodology & Numerical Methods Decision Input Data Results Hardware

14 Given a physical phenomenon Given a decision to take at a fixed milestone : Choice of S/H tools in order to take the right decision / milestone Choice of mathematical model : Mathematical approximation : Equations describing accurately the phenomenon Physical reality should be represented as well as needed Limit of CAE / experience comparison : Observation / measures Decision criteria & application domain Margin Goal : Not to reproduce physical observation! But to take the right decision, depending on project phase Standardization of methodology as well as for experimental method

15 CAE knowledge database CAE procedure sheet Fiche N 65XXX/YY/ZZ Métier : Sujet : Support métier Type : Outils/logiciels (KULI AC) Mise à jour le Description générale Sorties attendues (indiquer le nom du métier au sens DIEC= découpage GFE ou nom de la prestation) (titre global) (Exploratoire : en cours de création de l outil ou de la méthode, pilote : 1 ère mise en situation en conservant en parallèle la méthode traditionnelle en précisant le projet pilote, opérationnelle : validé, corrélé, limites connues, officiellement reconnu) (on peut préciser qui a fait la mise à jour) présentation de la philo du calcul réalisé il s agit des sorties «physiques» du calcul (ex : cartographie de pression), on précisera s il y a lieu les destinataires (ex : DICAP) Entrées Source Disponibilité souhaitée des entrées Identifier Type Why When How Comparison physical/virtual tests Complexity 4 1 Accuracy Skills and and human resources A E Research/ First applied Advanced devpt Used in project Maturity Used in in vehicle design process Le circuit de climatisation du véhicule UET Confort Thermique / itérations : Pré-contrat, Gel Design 100%, PPP3 Caractéristiques du vase d expansion : Délai pour la mise en donnée et le calcul Gain estimé... (1 semaine, préciser le plus de chose possible : calcul unitaire ou vague de calcul, préparation des données, l idée étant de donner un ordre de grandeur sur le délai entre mise à dispo de données et premier retour) par rapport à un essai physique remplacé en terme de gain QCDP,dans un 1 er temps on exprime de façon factuel le gain (ex : résultat connu 6 mois plus tôt et intégration de modification en PPP3 vague 1 au lieu de PSP) sans chercher à chiffrer on peut préciser si le gain est constaté (en opérationnel) ou attendu (pilote ou exploratoire) Coût du calcul Charge ETP, investissement matériel... Document de référence Remarques référence à une procédure... Défouloir...par ex les sorties intermédiaires servant à d autres secteurs (maillage servant aussi à la CAP par ex) Who Other info linked with additional technical documents this sheet characterizes the virtual test procedure

16 Conception uncertainty & optimisation Physical phenomenon Methodology & Numerical Methods Decision Input Data Results Hardware

17 Conception uncertainty & Optimisation Other factors - manufacture - use/ environment -ageing -project Load factors SYSTEM Answers Optimal analysis : Sensibility Optimisation Robustness Design parameters

18 HR /Skills Training/Organisation Conception uncertainty & optimisation Physical phenomenon Methodology & Numerical Methods Decision Input Data Results Hardware

19 HR / Skills / Training / Organisation Human Resources Competence scale / Users qualification Training Organisation / Optimisation of work tasks/ Repeatability Capitalisation Control organisation /Quality control /Validation of CAE notes, procedures Data Management Data management / Data Control Modification management, Error control Organisation

20 Summary : Best Practices on 4 stages HR /Skills Training/Organisation Conception uncertainty & optimisation Physical phenomenon Methodology & Numerical Methods Decision Input Data Results Hardware

21 Q/A session

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