Genetic Algorithm-Optimized Wind Tunnel for Experimental Validation of High-Efficiency Savonius Wind Turbines

This paper presents the design, optimization, and experimental validation of a closed-loop wind tunnel tailored for testing small-scale Savonius-type wind turbine (SWT) prototypes. Building on the genetic algorithm-based optimization framework, the tunnel integrates a contraction nozzle, test section, and diffuser, fabricated via 3D printing to ensure geometric precision. Computational Fluid Dynamics (CFD) simulations using ANSYS CFX guided the optimization process, targeting uniform velocity profiles and minimized pressure losses. Key innovations include a redesigned honeycomb flow straightener and screens to reduce turbulence intensity, achieving a root-mean-square (RMS) velocity deviation of 2% in the test section—critical for reliable SWT performance evaluation. Experimental validation confirmed a maximum velocity of 10.6 m/s with improved flow uniformity, while pressure drop reductions of 35% were attained through diffuser and guide vane optimizations. The study underscores the tunnel’s efficacy in bridging CFD predictions with physical testing, addressing challenges such as Reynolds number scalability and blockage effects (maintaining a sub10% frontal area ratio). The optimized tunnel will be used to validate novel SWT rotor designs, including CFD-optimized geometries that have demonstrated promising power coefficients of c<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> = 0.4 in simulations—a significant improvement over conventional Savonius rotors c<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> =0.25. This work bridges high-fidelity CFD predictions with experimental testing, providing a robust platform for advancing renewable energy technologies by verifying aerodynamic enhancements in SWT configurations under controlled flow conditions.