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THE AUTOSIM PROJECT • Digital model - Choice of elements and element formulation, discretisation of the model/mesh density and quality. • Mathematical model – Variety and accuracy of available material models and model assumptions. • Facilities - Hardware, processor and software dependencies. It is evident from the above list that CAE confidence is influenced by a broad variety of topics. Part of the objective of the AUTOSIM project was to identify the principal topics influencing CAE confidence and to establish the Current/Best Practices and Breakthrough Technologies in these principal topic areas. Engineering and design orientation H Ress. & organisation Data Physical Model Digital Model Math Model Facilities Simulation results Fig. 4.4.1.1: Foundations of CAE confidence (courtesy of Renault) 4.4.2 Current engineering perceptions and concerns with CAE confidence Enhancements in CAE confidence will encourage greater use of CAE in design, development and research activities, with the prospect of it being applied in broader and newer areas of interest; e.g. coupled fluid-structure interaction investigations. The benefits that could be expected with improvements in CAE confidence include: • Reducing the number of required physical prototypes • Less testing • Improvements in the quality and robustness of engineering designs • Virtual testing and certification • Better understanding of physical phenomena SIXTH FRAMEWORK PROGRAMME PRIORITY [6.2] [SUSTAINABLE SURFACE TRANSPORT] 012497 AUTOSIM 31 |
CURRENT & FUTURE TECHNOLOGIES IN AUTOMOTIVE ENGINEERING SIMULATION Further to the benefits it is important to consider that CAE confidence is highly dependent on the time and resources available for developing CAE models. As indicated in Fig.4.4.2.1 the return on invested time and cost to improve model confidence diminishes as the level of model confidence increases. Model confidence is therefore a balance between the available time and resources to develop a model against the eventual application of the model and the relevance and value of its predictions. Fig. 4.4.2.1: “Cost of confidence”(Ref. [10]) 4.4.3 Outcomes from the AUTOSIM project concerning CAE confidence Based on the results of a questionnaire conducted in the early stages of the AUTOSIM project the principal factors registered as priority areas in terms of improving CAE confidence included: • The physical model • Human resources and organisation • Data validity • Digital model 32 | SIXTH FRAMEWORK PROGRAMME PRIORITY [6.2] [SUSTAINABLE SURFACE TRANSPORT] 012497 AUTOSIM
Page 1 and 2: Current & Future Technologies in Au
Page 3 and 4: The Consortium Steering Committee w
Page 5 and 6: 4.5 How To Move On ................
Page 7 and 8: CURRENT & FUTURE TECHNOLOGIES IN AU
Page 35: CURRENT & FUTURE TECHNOLOGIES IN AU
Page 85: APPENDIX 1: GLOSSARY OF TERMS • U

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