Genetic Algorithm-Driven Analysis of Gravitational Water Vortex Power Plant Runner Performance: A Comparative Study Integrating Experimental and Computational Fluid Dynamics

Abstract

The gravitational water vortex power plant (GWVPP) has emerged as a promising renewable energy technology, characterized by low investment costs, a simple design, and minimal maintenance requirements. However, its performance has been constrained by suboptimal parameters, particularly in critical components such as the runner. This paper presents a comprehensive exploration of the optimization process for GWVPP runners, using a comparative analysis driven by a genetic algorithm (GA) prediction that integrates experimental data and computational fluid dynamics (CFD) simulations. The investigation focuses on three key performance parameters: power, torque, and efficiency, assessed over a rotational speed range of 1.91–3.26 rad/s. The findings show a consistent trend across all techniques, with performance improving with rotational speed up to an optimal range of around 2.6–2.7 rad/s, after which it declines. There is strong agreement between GA predictions and CFD simulations, demonstrating the GA's ability to capture and optimize system behavior. Experimental results show a similar pattern, but with lower efficiency, particularly at higher rotational speeds, due to practical losses, flow disruptions, and measurement uncertainties that are not fully captured in the numerical models. Sensitivity analysis emphasizes the impact of rotational speed, hub-blade angle, and blade number on system performance. Overall, the findings demonstrate that GA is a reliable and effective approach for improving GWVPP performance, with results consistent with CFD simulations and supported by experimental data. The study emphasizes the viability of GWVPP systems as a long-term energy option for low-head applications.