Akay, Hasan Umur

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https://hdl.handle.net/20.500.14411/6751
Name Variants
Akay, Hasan
Hasan, Akay
Akay,Hasan
Hasan Akay
A.,Hasan
H., Akay
A., Hasan
Akay,H.
Akay H.
H.,Akay
Akay, Hasan U.
Akay, H. U.
Akay, Hasan U. U.
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Profesör Doktor
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hasan.akay@atilim.edu.tr
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Automotive Engineering
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https://atilim.edu.tr/en/ae/page/2279/academic-staff
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0000-0003-2574-9942
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24532717000
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DLU2bk4AAAAJ
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ABI-3992-2020

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8th International Conference on Education and New Learning Technologies (EDULEARN) -- JUL 04-06, 2016 -- Barcelona, SPAIN2
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering2
International Journal of Computational Fluid Dynamics2
ARPN Journal of Engineering and Applied Sciences1
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Start date: 2020Subject: Aerodynamic Shape Optimization

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    Article
    A Gradient Enhanced Efficient Global Optimization-Driven Aerodynamic Shape Optimization Framework
    (MDPI, 2025) Senol, Niyazi; Akay, Hasan U.; Yigit, Sahin
    The aerodynamic optimization of airfoil shapes remains a critical research area for enhancing aircraft performance under various flight conditions. In this study, the RAE 2822 airfoil was selected as a benchmark case to investigate and compare the effectiveness of surrogate-based methods under an Efficient Global Optimization (EGO) framework and an adjoint-based approach in both single-point and multi-point optimization settings. Prior to optimization, the computational fluid dynamics (CFD) model was validated against experimental data to ensure accuracy. For the surrogate-based methods, Kriging (KRG), Kriging with Partial Least Squares (KPLS), Gradient-Enhanced Kriging (GEK), and Gradient-Enhanced Kriging with Partial Least Squares (GEKPLS) were employed. In the single-point optimization, the GEK method achieved the highest drag reduction, outperforming other approaches, while in the multi-point case, GEKPLS provided the best overall improvement. Detailed comparisons were made against existing literature results, with the proposed methods showing competitive and superior performance, particularly in viscous, transonic conditions. The results underline the importance of incorporating gradient information into surrogate models for achieving high-fidelity aerodynamic optimizations. The study demonstrates that surrogate-based methods, especially those enriched with gradient information, can effectively match or exceed the performance of gradient-based adjoint methods within reasonable computational costs.