AI-assisted CFD optimisation of multi-element wing angle of attack for enhanced formula SAE aerodynamic performance
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Issued Date
2025-11-01
Resource Type
eISSN
26662027
Scopus ID
2-s2.0-105018174815
Journal Title
International Journal of Thermofluids
Volume
30
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Thermofluids Vol.30 (2025)
Suggested Citation
Suvanjumrat C., Namchanthra S., Phengpom T., Priyadumkol J., Chookaew W., Watechagit S., Cheung S.C.P., Promtong M. AI-assisted CFD optimisation of multi-element wing angle of attack for enhanced formula SAE aerodynamic performance. International Journal of Thermofluids Vol.30 (2025). doi:10.1016/j.ijft.2025.101440 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112607
Title
AI-assisted CFD optimisation of multi-element wing angle of attack for enhanced formula SAE aerodynamic performance
Author's Affiliation
Corresponding Author(s)
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Abstract
This study presents a multi-level aerodynamic optimisation framework that integrates Computational Fluid Dynamics (CFD) with Artificial Intelligence (AI) techniques to enhance the performance of Formula SAE vehicles. Unlike conventional CFD or empirical approaches, the proposed framework employs a hybrid Genetic Algorithm–Artificial Neural Network (GA–ANN) model to optimise design parameters with greater accuracy and efficiency. Initial investigations refined the angles of attack (AoA) and airfoil geometries to maximise downforce while reducing drag and energy consumption. Two-dimensional CFD simulations established the baseline aerodynamic characteristics, which were subsequently extended to full three-dimensional analyses. Among turbulence models, the Menter k–ω SST demonstrated superior predictive capability compared to the standard k–ε, with discrepancies in drag and downforce below 15 %. The MSHD and Selig 1223 airfoils emerged as the most aerodynamically efficient candidates. Systematic AoA adjustments from 4.5°, 28.0°, and 56.0° to 5.5°, 33.0°, and 59.5° for the front wing, and from 9.5° and 40.0° to 12.2° and 41.9° for the rear wing produced CFD predictions validated within a 5 % error margin. The GA–ANN optimisation further enhanced aerodynamic performance, yielding a 14.8 % increase in front-wing downforce and a 28.4 % increase in rear-wing downforce, alongside reduced drag and improved energy efficiency. These findings demonstrate the potential of AI-assisted CFD optimisation to bridge high-fidelity simulations with tangible performance gains, offering a promising pathway for hybrid airfoil design and active control strategies in high-performance automotive applications.