Progress of CFD-based Aerodynamics Optimization
DOI:
https://doi.org/10.54097/e3w25m97Keywords:
Computational Fluid Dynamics (CFD); Aerodynamic Optimization; Biomimetic Design; Flow Control; Machine Learning.Abstract
Computational Fluid Dynamics (CFD) has become a cornerstone of modern aerodynamic optimization, enabling precise simulation and analysis of fluid flow across complex biomimetic geometries. This paper reviews the latest progress in CFD-based aerodynamic design, focusing on bionic flow control mechanisms and multi-scale optimization strategies. Inspired by natural organisms—such as the drag-reducing riblets of sharks, the stall-delaying tubercles of humpback whales, and the noise-suppressing feathers of owls—researchers have successfully incorporated biological principles into engineering applications in the aerospace and automotive fields. The review highlights three critical aspects: CFD simulation of boundary-layer interactions and vortex dynamics, fluid–structure interaction (FSI) for flexible aerodynamic designs, and AI/ML-assisted CFD acceleration for data-driven optimization. Moreover, multi-objective algorithms and surrogate modeling have significantly improved efficiency in exploring complex design spaces. The integration of CFD with artificial intelligence is paving the way toward real-time, intelligent aerodynamic design and system-level optimization. Future trends include refined multi-scale modeling, stronger coupling of physical and data-driven methods, and broader application of bionic concepts in sustainable engineering.
Downloads
References
[1] Bechert D W, Bruse M, Hage W, Van der Hoeven J G T, Hoppe G. Experiments on drag-reducing surfaces and their optimization with adjustable geometry. Journal of Fluid Mechanics, 1997, 338: 59–87.
[2] Bounds C P, 2024. Enhancing CFD predictions with explainable machine learning for aerodynamic characteristics of idealized ground vehicles. Aerospace Science and Technology, 149: 109986.
[3] Caron C, Roy N, Lecocq G, et al. Machine learning to speed up computational fluid dynamics engineering simulations for built environments: a review. Building and Environment, 2024, 255: 111089.
[4] Carruthers A C, Thomas A L R, Taylor G K. Automatic aeroelastic devices in the wings of a steppe eagle (Aquila nipalensis). Journal of Experimental Biology, 2007, 210(23): 4136–4149.
[5] Choi H, Moin P, Kim J. Direct numerical simulation of turbulent flow over riblets. Journal of Fluid Mechanics, 2006, 177: 133–166.
[6] Dussauge T P. A reinforcement learning approach to airfoil shape optimization. Aerospace Science and Technology, 2023, 142: 108423.
[7] Fish F E, Battle J M. The tubercles on humpback whales’ flippers: application of bio-inspired technology. Integrative and Comparative Biology, 2011, 51(1): 203–213.
[8] Han J, Zhang Y, Chen R, et al. Review on bio-inspired flight systems and bionic aerodynamics. Progress in Aerospace Sciences, 2020, 116: 100617.
[9] Hou D, Zhang Z, Gao Y, et al. Aerodynamic effects of time-varying corrugations on dragonfly wings in flapping flight. Bioinspiration & Biomimetics, 2024, 19(3): 036012.
[10] Jaworski J W, Peake N. Aeroacoustics of silent owl flight. Annual Review of Fluid Mechanics, 2020, 52: 395–420.
[11] Ke W, Wang D, Zhao Q, et al. Influence of leading-edge tubercles on the aerodynamic performance of a horizontal-axis wind turbine: a numerical study. Renewable Energy, 2021, 170: 1031–1042.
[12] Li W, Zhang J, Chen Y, et al. Biomimetic shark skin: design, fabrication and hydrodynamic function. Bioinspiration & Biomimetics, 2014, 9(4): 046018.
[13] Othman A K, Abdelaziz M, Ahmed M R. Aerial and aquatic biological and bioinspired flow control strategies. Progress in Aerospace Sciences, 2023, 146: 100927.
[14] Poloni C, Onesti L, Giurgevich A, et al. Multi-objective aerodynamic optimization by means of robust and efficient genetic algorithms. Structural and Multidisciplinary Optimization, 2004, 27(1–2): 64–74.
[15] Shirvani A, Yaghoubi M, Hashemi M R. A deep learning–genetic algorithm approach for aerodynamic inverse design via optimization of pressure distribution. Aerospace Science and Technology, 2024, 150: 110021.
[16] Spillman J J. The use of wing tip sails to reduce vortex drag. Aeronautical Journal, 2016, 120(1226): 1451–1465.
[17] Sudhakar S, Balakrishnan S. Influence of leading-edge tubercles on aerodynamic characteristics of a high aspect-ratio UAV. Aerospace Science and Technology, 2017, 66: 86–97.
[18] Sudin M N, Abdullah M Z, Ariffin A K, et al. Review of research on vehicle aerodynamic drag reduction methods. Applied Mechanics and Materials, 2014, 663: 153–158.
[19] Tran J, Lee K, Cho Y, et al. Aerodynamics-guided machine learning for design optimization of electric vehicles. Energy Reports, 2024, 10: 1090–1102.
[20] Wang L, Zhang C, Gao F, et al. A relay-based probabilistic prediction model for multi-fidelity scenarios in the total pressure loss of a compressor cascade with micro-textured surfaces. Energy Conversion and Management, 2025, 316: 118758.
[21] Walsh M J, Lindemann A M. Optimization and application of riblets for turbulent drag reduction. AIAA Journal, 1984, 22(4): 485–489.
[22] Zhan Z, Liu J, Zhang X, et al. Review of computational fluid dynamics analysis in biomimetic applications for underwater vehicles. Ocean Engineering, 2024, 298: 117463.
[23] Zhou E, Basri E this paper, Abdullah M F, et al. Computational fluid dynamics analysis in biomimetics applications: a review from aerospace engineering perspective. Aerospace Science and Technology, 2023, 137: 107874.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Academic Journal of Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
