Citation

Ab. Aziz, Nor Azlina and Abdul Aziz, Nor Hidayati and Ghazali, Anith Khairunisa and Mohamad, Norhidayah and Tobing, Sheila and Auzani, Ahmad Syihan and Hutagaol, Delfando and Lumban Siantar, Gabriella Averina (2025) Prediction of Lift and Drag for Hydro Turbine Design Using Machine Learning Algorithms. Algorithms, 19 (1). p. 8. ISSN 1999-4893

Text Prediction of Lift and Drag for Hydro Turbine Design Using Machine Learning Algorithms.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Predicting lift and drag in hydro turbine design is important to optimize its performance. However, it poses significant challenges due to the complexity of fluid dynamics, which is traditionally addressed by Reynolds-Averaged Navier–Stokes equations, which is timeconsuming. Moreover, these methods are computationally demanding, making them a costly approach and less efficient for complex turbine designs. Recent advancements in machine learning (ML) offer a promising alternative with reduced computational costs while maintaining accuracy. This paper explores the use of a data-driven ML model for predicting aerodynamic performance, specifically lift and drag, in hydro turbine design. The models were developed from experimental hydro turbine data gathered from various blade designs and flow conditions. CatBoost yielded the highest predictive accuracy among all the models tested. The findings indicate that CatBoost achieved the best predicted accuracy, followed by LGBM, demonstrating the efficacy of machine learning methodologies in modeling hydrodynamic forces in turbine design.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, regressor
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	11 Feb 2026 01:08
Last Modified:	11 Feb 2026 01:08
URII:	http://shdl.mmu.edu.my/id/eprint/15333

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