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Proceedings of CAD'15, 2015, 58-63
Estimation of CAD Model Simplification Impact on CFD Analysis using Machine Learning Techniques

Florence Danglade, Philippe Véron, Jean-Philippe Pernot, LSIS - UMR CNRS
Lionel Fine, AIRBUS Group Innovations Suresnes France

Abstract. In the field of transfer from Computer-Aided Design (CAD) to Finite Element Analysis (FEA), preparation processes based on CAD model simplification ensure the quality and the reliability of analysis results. For convective heat transfer analysis, the analysis computation is based on a mesh of a fluid volume wrapping the simplified CAD model. A huge number of elements are necessary to mesh all the local details. Without simplification, the CAD model of the fluid volume and its meshing are often impossible to obtain or the computing time is too high. Existing methods and tools for CAD model simplification allow preparing CAD model to produce an appropriate heat transfer analysis model. Thakur and al. propose a classification of simplification technologies based on surface entity operators (SE class), volume entity operators (VE class), explicit features operators (EF class) or dimension reduction operators. We can add to this list, operations based on the simplification of assembly trees (AT class).

Keywords. CAD/FEA transfer, CAD model simplification, Machine learning

DOI: 10.14733/cadconfP.2015.58-63