Multi-Objective Aerodynamic Optimization of Impulse Turbine Using Surrogate-Based Modeling and Genetic Algorithms

Abstract An axial-flow impulse turbine functions as a crucial secondary energy conversion component in oscillating water column (OWC) wave energy systems, and its performance directly impacts the overall power generation efficiency. To enhance turbine performance, this study introduces a multi-objective optimization method that focuses on key geometric parameters, including rotor blade thickness, rotor-guide spacing, tip clearance, number of rotor blades, guide vane installation angle, and number of guide vanes. A numerical model based on the RANS equations is established to evaluate turbine aerodynamic performance. Subsequently, a response surface model is constructed using the design of experiments (DOE), and the nondominated sorting genetic algorithm II (NSGA-II) is employed to derive Pareto front solutions, from which the optimal solution is selected. Finally, the effectiveness of the optimized model is verified through both numerical simulations and physical experiments. The simulation results indicate that, at the design operating point, the optimized model achieves approximately 7.22% higher efficiency compared to the initial model. Over the flow coefficient range of 0–2.5, the optimized model reaches a peak efficiency of 0.43, representing a 7.50% improvement over the initial model. Additionally, under sinusoidal reciprocating airflow conditions, the optimized model demonstrates around 7.00% increases in both peak output torque and power. Experimental results confirm that the numerical simulation model established in this study is reliable, with the optimized model exhibiting an approximately 10.50% increase in peak efficiency compared to the initial model during experimental testing.