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

• Published in: Engineering computations Vol. 37; no. 2; pp. 430 - 457
• Main Authors: Amrit, Anand, Leifsson, Leifur
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 09.08.2019; Emerald Group Publishing Limited
• Subjects: Accuracy; Adaptation; Aerodynamics; Aircraft; Design optimization; Evolutionary algorithms; Genetic algorithms; Multiple objective analysis; Optimization algorithms; Pareto optimization; Simulation; Subsonic aircraft; Tradeoffs; Transonic airfoil design; Subsonic wing design; Multi-objective optimization; Multi-fidelity methods; Pareto front exploration; Simulation-based aerodynamic design
• ISSN: 0264-4401; 1758-7077
• DOI: 10.1108/EC-12-2018-0553

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.