Prediction of Lift and Drag for Hydro Turbine Design Using Machine Learning Algorithms

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 time-consuming. 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.