Computational fluid dynamics reveals optimal blade entry and water admission angles to enhance turbine efficiency.
Cross-Flow Turbines (CFTs) are widely recognized for their adaptability and cost-effectiveness in micro-hydropower (MHP) systems. However, their hydraulic efficiency remains highly sensitive to geometric configurations, particularly the Blade Entry Angle (BEA) and Water Admission Angle (WAA). This study presents a high-fidelity computational fluid dynamics (CFDs) investigation of CFT performance across a wide range of BEA (5–40°) and WAA (45–105°) combinations at runner speeds from 150 to 1200 rpm, under constant head and flow conditions. The simulations were performed using a steady-state Reynolds-Averaged Navier–Stokes (RANS) model coupled with the volume of fluid (VOF) method and the SST k–ω turbulence closure. Benchmarking against the widely used industrial standard configuration (BEA = 30°, WAA = 90°), which achieved 79.1% efficiency at 900 rpm, this study identifies an optimized setup at BEA = 15° and WAA = 60° delivering a peak efficiency of 84.91% and shaft power output of 225.5 W—representing an efficiency gain of approximately 5.8%. The standard configuration was found to suffer from flow misalignment, jet dispersion, and increased internal energy loss, particularly at off-design speeds. In contrast, optimized geometries ensured stable pressure gradients, coherent jet–blade interaction, and enhanced momentum transfer. The results provide a validated performance map and establish a robust design reference for enhancing CFT efficiency and reliability in decentralized renewable energy systems.
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Assefa et al. (2025) studied this question.
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