Open Access
ARTICLE
Airfoil Shape Optimisation Using a Multi-Fidelity Surrogate-Assisted Metaheuristic with a New Multi-Objective Infill Sampling Technique
1
Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering,
Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand
2
Department of Mechanical Engineering, Faculty of Engineering, Mahasarakham University, Mahasarakham, 44150, Thailand
3
Australian Maritime College, College of Science and Engineering, University of Tasmania, Launceston, 7248, Australia
4
Department of Mechanical Engineering, School of Technology, GSFC University, Vadodara, Gujarat, 391750, India
5
Department of Mechanical Engineering, Bursa Uludag University, Bursa, 16059, Türkiye
* Corresponding Author: Nantiwat Pholdee. Email:
Computer Modeling in Engineering & Sciences 2023, 137(3), 2111-2128. https://doi.org/10.32604/cmes.2023.028632
Received 29 December 2022; Accepted 17 March 2023; Issue published 03 August 2023
Abstract
This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization. The optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry constraints. Computational Fluid Dynamic (CFD) and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value. A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model constructed. To validate and further assess the proposed methods, a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied with multi-fidelity simulation, while their numerical performance is investigated. The results obtained show that the proposed technique is the best performer for the demonstrated airfoil shape optimization. According to this study, applying multi-fidelity with multi-objective infill sampling criteria for surrogate-assisted optimization is a powerful design tool.Graphic Abstract
Keywords
Cite This Article
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.