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 multiobjective framework. This study proposed the optimization of the very low head propeller turbine runner airfoil geometry using the nondominated sorting genetic algorithm II multiobjective 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 dynamics 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 ∼573 W

which is 1.48% higher than the initial design. These results demonstrate that the nondominated sorting genetic algorithm II-based class shape transformation–genetic algorithm–XFOIL framework is effective for improving the performance of very low head turbine runners.