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Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction

Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction,10.1016/j.joc

Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction   (Citations: 7)
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A computationally efficient design methodology for transonic airfoil optimization has been developed. In the optimization process, a numerically cheap physics-based low-fidelity surrogate (the transonic small-disturbance equation) is used in lieu of an accurate, but computationally expensive, high-fidelity (the compressible Euler equations) simulation model. Correction of the low-fidelity model is achieved by aligning its corresponding airfoil surface pressure distribution with that of the high-fidelity model using a shape-preserving response prediction technique. The resulting method requires only a single high-fidelity simulation per iteration of the design process. The method is applied to airfoil lift maximization in two-dimensional inviscid transonic flow, subject to constraints on shock-induced pressure drag and airfoil cross-sectional area. The results showed that more than a 90% reduction in high-fidelity function calls was achieved when compared to direct high-fidelity model optimization using a pattern-search algorithm.
Journal: Journal of Computational Science , vol. 1, no. 2, pp. 98-106, 2010
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    • ...Researches [11], [13-15] have unveiled that this technique could provide an excellent trade-off between accuracy and cost in aerodynamics shape optimization...

    Junpeng Liet al. Data fusion of multi-fidelity model and its application in low speed r...

    • ...However, Leifsson and Koziel [25], have recently proposed the use of physics-based surrogate models in which, they are built relating the design variables with pressure distributions (instead of objective functions)...

    Alfredo Arias-Montañoet al. Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape O...

    • ...In this chapter we describe a computationally efficient variable-fidelity airfoil shape optimization methodology [15, 16, 17], which employs physics-based lowfidelity surrogate models created by means of the shape-preserving response prediction (SPRP) technique [18]...

    Slawomir Kozielet al. Airfoil Shape Optimization Using Variable-Fidelity Modeling and Shape-...

    • ...For example, even in the case of a two-dimensional airfoil shape optimization with three design variables, a gradient-based optimization method can require over one hundred function evaluations, and optimization process could take as long as one week [26]...
    • ...In particular, the main emphasis of the chapter is on the SBO approach exploiting surrogate models constructed from corrected physicsbased low-fidelity models [26, 34-38]...
    • ...One of the recent techniques is shape-preserving response prediction (SPRP) introduced in [26]...
    • ...Shape-preserving response prediction (SPRP) is a relatively novel technique which was introduced in the field of microwave engineering [77], but it has been recently applied to airfoil shape optimization [26, 78]...

    Leifur Leifssonet al. Variable-Fidelity Aerodynamic Shape Optimization

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