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Article Dans Une Revue Algorithms Année : 2020

Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics

Aldo Serafino
Benoit Obert
  • Fonction : Auteur
Paola Cinnella

Résumé

Efficient Robust Design Optimization (RDO) strategies coupling a parsimonious uncertainty quantification (UQ) method with a surrogate-based multi-objective genetic algorithm (SMOGA) are investigated for a test problem in computational fluid dynamics (CFD), namely the inverse robust design of an expansion nozzle. The low-order statistics (mean and variance) of the stochastic cost function are computed through either a gradient-enhanced kriging (GEK) surrogate or through the less expensive, lower fidelity, first-order method of moments (MoM). Both the continuous (non-intrusive) and discrete (intrusive) adjoint methods are evaluated for computing the gradients required for GEK and MoM. In all cases, the results are assessed against a reference kriging UQ surrogate not using gradient information. Subsequently, the GEK and MoM UQ solvers are fused together to build a multi-fidelity surrogate with adaptive infill enrichment for the SMOGA optimizer. The resulting hybrid multi-fidelity SMOGA RDO strategy ensures a good tradeoff between cost and accuracy, thus representing an efficient approach for complex RDO problems.
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Dates et versions

hal-03892561 , version 1 (09-12-2022)

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Aldo Serafino, Benoit Obert, Paola Cinnella. Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics. Algorithms, 2020, 13 (10), pp.248. ⟨10.3390/a13100248⟩. ⟨hal-03892561⟩
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