CAD-BASED MULTI-OBJECTIVE SHAPE OPTIMIZATION METHOD WITH META-PARAMETERS

CAD-BASED MULTI-OBJECTIVE SHAPE OPTIMIZATION METHOD WITH META-PARAMETERS

Froment, P.; Gillot, F.; Jezequel, L.; Tourbier, Y.

Full Article:

Current design loops for shape optimizations allows significant improvements in relation to the functions that need to be optimized, and are widely used in industry. Among these approaches, parametric shape optimization allows rapid enhancement of the shape, on the condition that the design space is confined enough in order to be explored within a reasonable computational time. This paper introduces a CAD-based large-scale shape optimization method for products requiring significant computational cost, for instance in multiphysics simulations. A two-step scheme is used to achieve the intended results. The first step is to build a base of meta-parameters from an initial learning set. The number of meta-parameters is defined according to the final computational cost that is intended. These meta-parameters are generated by an artificial neural network pre-trained on a sample set of simulations. The meta-parameters represent a combination of parameters having a similar influence towards the variation of objective functions. Subsequently, the previous meta-parameters are used to rapidly reach a limited design space close to the optimum. To prove the efficiency and accuracy of the method, the workflow schedule is applied on shape optimization of a car body. The objective is to optimize both the side force coefficient and the yaw moment coefficient, all while taking into account the drag coefficient.

Current design loops for shape optimizations allows significant improvements in relation to the functions that need to be optimized, and are widely used in industry. Among these approaches, parametric shape optimization allows rapid enhancement of the shape, on the condition that the design space is confined enough in order to be explored within a reasonable computational time. This paper introduces a CAD-based large-scale shape optimization method for products requiring significant computational cost, for instance in multiphysics simulations. A two-step scheme is used to achieve the intended results. The first step is to build a base of meta-parameters from an initial learning set. The number of meta-parameters is defined according to the final computational cost that is intended. These meta-parameters are generated by an artificial neural network pre-trained on a sample set of simulations. The meta-parameters represent a combination of parameters having a similar influence towards the variation of objective functions. Subsequently, the previous meta-parameters are used to rapidly reach a limited design space close to the optimum. To prove the efficiency and accuracy of the method, the workflow schedule is applied on shape optimization of a car body. The objective is to optimize both the side force coefficient and the yaw moment coefficient, all while taking into account the drag coefficient.

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DOI: 10.5151/meceng-wccm2012-19050

Referências bibliográficas

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Como citar:

Froment, P.; Gillot, F.; Jezequel, L.; Tourbier, Y.; "CAD-BASED MULTI-OBJECTIVE SHAPE OPTIMIZATION METHOD WITH META-PARAMETERS", p-2854-2866. In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 23580828, DOI 10.5151/meceng-wccm2012-19050