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Open AccessArticle

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

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Enertime, 1 rue du Moulin des Bruyères, 92400 Courbevoie, France
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Laboratoire Dynfluid, Arts et Métiers ParisTech, 151 blvd de l’hopital, 75013 Paris, France
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Author to whom correspondence should be addressed.
Algorithms 2020, 13(10), 248; https://doi.org/10.3390/a13100248
Received: 31 July 2020 / Revised: 22 September 2020 / Accepted: 24 September 2020 / Published: 30 September 2020
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. View Full-Text
Keywords: robust design optimization; uncertainty quantification; gradient enhanced kriging; method of moments; multi-fidelity surrogate; continuous adjoint; discrete adjoint robust design optimization; uncertainty quantification; gradient enhanced kriging; method of moments; multi-fidelity surrogate; continuous adjoint; discrete adjoint
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Serafino, A.; Obert, B.; Cinnella, P. Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics. Algorithms 2020, 13, 248.

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