Simultaneous meta-modeling for both input and output spaces for rapid design space exploration in structural shape optimization

Balaji Raghavan (1) , Piotr Breitkopf (2) , Pierre Villon (2)

1 Geosax [LGCGM]
2 Roberval - Roberval

Engineering design problems generally involve a high-dimensional input space of design variables yielding an output space by means of costly high-fidelity evaluations. In order to decrease the overall cost, reduced-order models for the output space such as Proper Orthogonal Decomposition (POD) and proper Generalized Decomposition (PGD) are a promising area of research. However, little research has been conducted into alleviating the problems associated with a high-dimensional input space. In this paper, we present a simultaneous meta-modeling protocol for both spaces : reparametrization of the input space by constrained shape interpolation using the α-manifold of admissible shapes (finite element/CFD meshes, etc), and constrained Proper Orthogonal Decomposition for reducing the output space and apply the approach to two industrial shape optimization problems.

Mots clés: input space reduction; model reduction; design; diffuse approximation

Domaines: Mécanique [physics]

Balaji Raghavan, Piotr Breitkopf, Pierre Villon. Simultaneous meta-modeling for both input and output spaces for rapid design space exploration in structural shape optimization. 11e colloque national en calcul des structures, CSMA, May 2013, Giens, France. ⟨hal-01717026⟩