Optimization of airfoil geometry using the NSGA-II method to improve the performance and efficiency of a very low head turbine

Abstract Research on the development of very low head turbines is becoming increasingly important for harnessing the potential of hydropower in flat-bottomed rivers. However, studies on very low head turbine runner optimization are generally limited to single-objective approaches or have not specifically integrated airfoil parameterization and aerodynamic evaluation within a multi-objective framework. This study proposed the optimization of the very low head propeller turbine runner airfoil geometry using the non-dominated sorting genetic algorithm II multi-objective framework, which combines class shape transformation, genetic algorithm, and XFOIL. The turbine was designed for a net head of 2.07 m, a flow rate of 0.04 m3/s, and a rotational speed of 1700 rpm (high-speed micro-propeller turbine). Optimization was performed on five runner segments to improve the airfoil’s hydrodynamic characteristics, which were subsequently validated using three-dimensional computational fluid dynamic simulations. The results showed that optimization increased the lift-to-drag ratio to 27.67% over the low angle of attack range relevant to operating conditions. At the design point, the optimized runner achieved an efficiency of 86.8% and a power output of approximately 573 W, which is 1.48% higher than the initial design. These results demonstrate that the non-dominated sorting genetic algorithm II-based class shape transformation-genetic algorithm-XFOIL framework is effective for improving the performance of very low head turbine runners.