Adjoint-based robust optimization design of laminar flow airfoil under flight condition uncertainties

Chen, Yifu; Rao, Hanyue; Xiong, Neng; Fan, Jiaxuan; Shi, Yayun; Yang, Tihao

doi:10.1016/j.ast.2023.108465

Cited by 7 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tanabi et al [122] proposed an adjoint-based optimization framework that can robustly optimize the shape of the airfoil based on variable parameters of the airfoil and flight conditions. Chen et al [123] developed an adjoint-based robust optimization design framework of laminar airfoil subject to uncertainties in flight conditions for subsonic and transonic conditions. The results indicated that the adjoint-based optimization can improve the ability of the laminar airfoil under uncertain disturbance of flight conditions.…”

Section: Optimization Patternmentioning

confidence: 99%

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

Wang,

Zhang,

Wang

et al. 2024

Mathematics

View full text Add to dashboard Cite

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

show abstract

Section: Optimization Patternmentioning

confidence: 99%