Efficiency optimization of a hybrid Savonius-Darrieus hydrokinetic turbine via regression modeling and CFD-based design of experiments

This study presents the evaluation and optimization of a hybrid Savonius-Darrieus hydrokinetic turbine through the numerical analysis of a design of experiments. The objective was to improve the turbine's efficiency by analyzing the impact of coupling angle ( θ ), radius ratio (RR), and tip-speed ratio (TSR) on its performance. A total of 36 experiments were designed, with factors set at more than three levels to account for quadratic behaviors. Computational fluid dynamics (CFD) analyses were conducted in ANSYS 2024 R2 to calculate the torque generated in each case. An analysis of variance (ANOVA) was performed for the results of each experiment, identifying the radius ratio as the factor exerting the greatest effect on torque generation. Five regression models were developed in the R environment, and variable coefficients were calculated through ANOVA. The best regression model was identified using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The model with the best fit for efficiency prediction was optimized using a response surface, where three axes represented the analyzed variables and the response variable was visualized as a color gradient. By iterating the regression model within the studied factor levels, the optimal parametric configuration for the hybrid turbine was determined to be 0.2 for the radius ratio, 50° for the coupling angle, and 2.2 for the TSR, resulting in an efficiency of 55.19%. These findings offer a data-driven design framework for improving hybrid hydrokinetic turbine performance in remote and rural energy systems. • Parametric Optimization: The study identified an optimal configuration (RR = 0.2, θ = 50°, TSR = 2.2) that achieved a turbine efficiency of 55.19%, surpassing all evaluated experimental cases. • Key Influencing Factor: Radius ratio (RR) was determined to be the most significant factor affecting turbine efficiency, contributing to 39.1% of the total influence according to ANOVA. • Model Accuracy: Among five developed regression models, model mt5 was the best fit based on AIC and BIC criteria, enabling accurate efficiency prediction and guiding optimal turbine design.