Airfoil Shape Optimisation Using a Multi-Fidelity Surrogate-Assisted Metaheuristic with a New Multi-Objective Infill Sampling Technique

Aye C. M., Wansaseub K., Kumar S., Tejani G. G., Bureerat S., YILDIZ A. R., ...Daha Fazla

CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, cilt.137, ss.2111-2128, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 137
  • Basım Tarihi: 2023
  • Doi Numarası: 10.32604/cmes.2023.028632
  • Dergi Adı: CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2111-2128
  • Anahtar Kelimeler: Multi-fidelity modelling, differential evolution, kriging, infill sampling criteria, metaheuristics, COMPUTATIONAL FLUID-DYNAMICS, DIFFERENTIAL EVOLUTION, DESIGN, SIMULATION, ALGORITHM, FRAMEWORK, STRATEGY
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

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.