Multi-fidelity aerodynamic design trade-off exploration using point-by-point Pareto set identification

Amrit, Anand; Leifsson, Leifur; Kozieł, Sławomir

doi:10.1016/j.ast.2018.05.023

Cited by 20 publications

(12 citation statements)

References 67 publications

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“…The multi-fidelity point-by-point algorithm (Amrit et al , 2017b) utilizes any single point on the Pareto front as its starting point and is obtained by a space-mapping-based single objective optimization. From the starting point, the MOO algorithm produces a sequence of optimal designs x ( k ) , k = 1, 2, …, point by point.…”

Section: Methodsmentioning

confidence: 99%

“…The two SOO problems, i.e. to minimize the drag coefficient ( C D ) and to minimize the pitching moment coefficient ( C M ), are solved using the SM algorithm (Amrit et al , 2017b), and results are shown in Table IV. The cost in terms of CPU time for each of the SOO process is approximately 9 h on an HPC with 32 processors.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Amrit et al (2017a) developed design strategies for multi-objective aerodynamic design exploration using design space reduction, evolutionary algorithms, cokriging (Laurenceau and Sagaut, 2008; Keane, 2012) and multi-fidelity models. In our recent work (Amrit et al , 2017b), we developed a multi-objective aerodynamic design optimization algorithm using multi-fidelity models and the point-by-point Pareto set identification technique, originally developed for microwave antenna design (Koziel and Bekasiewicz, 2016). The outcome of these work has shown that multi-fidelity methods can significantly reduce the computational overhead of multi-objective aerodynamic design exploration.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents new applications and comparisons of three recently developed surrogate-assisted and multi-fidelity MOO algorithms. In particular, the algorithms are: The surrogate-assisted MOEA (SA-MOEA) developed by Amrit et al (2017a). The multi-fidelity multi-objective aerodynamic design exploration using point-by-point Pareto set identification (Amrit et al , 2017b), and The multi-fidelity sequential domain patching (SDP) algorithm, originally developed by Koziel and Bekasiewicz (2018), for the design of microwave antennas. …”

Section: Introductionmentioning

confidence: 99%

“…The multi-fidelity multi-objective aerodynamic design exploration using point-by-point Pareto set identification (Amrit et al , 2017b), and…”

Section: Introductionmentioning

confidence: 99%

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Applications of surrogate-assisted and multi-fidelity multi-objective optimization algorithms to simulation-based aerodynamic design

Amrit

Leifsson

2019

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Purpose The purpose of this work is to apply and compare surrogate-assisted and multi-fidelity, multi-objective optimization (MOO) algorithms to simulation-based aerodynamic design exploration. Design/methodology/approach The three algorithms for multi-objective aerodynamic optimization compared in this work are the combination of evolutionary algorithms, design space reduction and surrogate models, the multi-fidelity point-by-point Pareto set identification and the multi-fidelity sequential domain patching (SDP) Pareto set identification. The algorithms are applied to three cases, namely, an analytical test case, the design of transonic airfoil shapes and the design of subsonic wing shapes, and are evaluated based on the resulting best possible trade-offs and the computational overhead. Findings The results show that all three algorithms yield comparable best possible trade-offs for all the test cases. For the aerodynamic test cases, the multi-fidelity Pareto set identification algorithms outperform the surrogate-assisted evolutionary algorithm by up to 50 per cent in terms of cost. Furthermore, the point-by-point algorithm is around 27 per cent more efficient than the SDP algorithm. Originality/value The novelty of this work includes the first applications of the SDP algorithm to multi-fidelity aerodynamic design exploration, the first comparison of these multi-fidelity MOO algorithms and new results of a complex simulation-based multi-objective aerodynamic design of subsonic wing shapes involving two conflicting criteria, several nonlinear constraints and over ten design variables.

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Section: Methodsmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The multi-fidelity multi-objective aerodynamic design exploration using point-by-point Pareto set identification (Amrit et al , 2017b), and…”

Section: Introductionmentioning

confidence: 99%

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Applications of surrogate-assisted and multi-fidelity multi-objective optimization algorithms to simulation-based aerodynamic design

Amrit

Leifsson

2019

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Low‐cost multi‐criteria design optimization of compact microwave passives using constrained surrogates and dimensionality reduction

Koziel

Pietrenko‐Dabrowska

Al‐Hasan

2020

Int J Numerical Modelling

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Design of contemporary microwave circuits is a challenging task. Typically, it has to take into account several performance requirements and constraints.The design objectives are often conflicting and their simultaneous improvement may not be possible; instead, compromise solutions are to be sought. Representative examples are miniaturized microwave passives where reduction of the circuit size has a detrimental effect on its electrical characteristics. Acquiring information about the best possible design trade-offs is invaluable for the designer, yet it entails computationally expensive multi-objective optimization (MO). MO is typically conducted using population-based metaheuristic algorithms, the cost of which might be extremely high. If the circuit performance is evaluated using full-wave electromagnetic (EM) analysis, this cost is often prohibitive. A workaround is the employment of fast surrogate models, and a number of surrogate-assisted frameworks have been proposed in the literature. Unfortunately, a construction of reliable surrogates is hindered in higher dimensional parameter spaces. The recently proposed constrained modeling mitigates this issue to a certain extent by restricting the modeling process to the region containing the Pareto front to be found. This work proposes a novel surrogate-based MO technique that involves constrained modeling and explicit reduction of the surrogate domain dimensionality. The latter is achieved through the spectral analysis of the extreme Pareto-optimal design set obtained by local search routines. Our methodology is validated using a 15-parameter impedance-matching transformer with the Pareto set identified at the cost of a few hundred EM analyses of the circuit. The numerical experiments also demonstrate a significant reduction of the optimization cost as compared to the state-of-the-art surrogate-assisted MO methods.

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Numerical Analysis for Roof Rack Crossbar Wind Noise Prediction and Minimization

Amrit

Sodhi

Ranga

2022

Lecture Notes in Mechanical Engineering

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Multi-fidelity aerodynamic design trade-off exploration using point-by-point Pareto set identification

Cited by 20 publications

References 67 publications

Applications of surrogate-assisted and multi-fidelity multi-objective optimization algorithms to simulation-based aerodynamic design

Applications of surrogate-assisted and multi-fidelity multi-objective optimization algorithms to simulation-based aerodynamic design

Low‐cost multi‐criteria design optimization of compact microwave passives using constrained surrogates and dimensionality reduction

Numerical Analysis for Roof Rack Crossbar Wind Noise Prediction and Minimization

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