Experimental and Computational Fluid Dynamics-Based Response Surface Methodology Optimization of Rotor–Stator Blade Configuration in Micro-Kaplan Turbines

Abstract Kaplan turbines are widely recognized for their efficiency and adaptability in low-head hydropower systems, making them critical components in renewable energy infrastructure. This study combines experimental testing, high-fidelity computational fluid dynamics (CFD), and response surface methodology (RSM)-based design of experiments (DOE) to evaluate the influence of rotor blade count, stator blade count, and inlet velocity on turbine performance. A three-level factorial DOE with four center points was used to efficiently capture nonlinearities and interactions among the design factors. The CFD model was validated by comparing its power predictions with experimental data from a closed-loop hydraulic test rig under controlled flow conditions. The model agreed with experimental results, with deviations of less than ±5% across the tested range of velocities, confirming its accuracy. This validated CFD model was then used to construct second-order regression surfaces for key performance metrics: power, efficiency, and pressure drop. Results indicated that while an increase in blade count improves power output, it also leads to higher pressure losses and reduced efficiency beyond an optimal configuration, reflecting the inherent trade-offs in Kaplan turbine design. Significant interactions between blade geometry and flow conditions highlighted the need for coordinated rotor and stator tuning. This integrated approach offers a predictive, resource-efficient framework for optimizing micro-Kaplan turbines, aiding the development of compact, high-performance hydropower systems that contribute to global sustainability goals.