Optimization of a radial-inflow turbine and energy-loss analysis based on the NSGA-II algorithm

As a key energy‐conversion component in the organic Rankine cycle (ORC), the performance of the radial-inflow turbine (RIT) plays a critical role in the recovery and reutilization of industrial waste heat. To improve the energy conversion performance of the radial-inflow turbine, this study performs a coupled optimization of the rotor and stator. A multi-objective hybrid optimization framework is developed by integrating an Efficient Global Optimization (EGO) surrogate model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II), enabling simultaneous optimization of the geometric parameters of both the stator and rotor. Moreover, entropy-generation theory is employed to assess the energy-loss mechanisms before and after optimization. The results show that, under the design operating condition, the optimized RIT achieves a 2.9% increase in efficiency. Under varying pressure ratios, the high-efficiency operating region expands to 77.4% of its original range. After optimization, the rotor and stator are better matched, and the losses near the stator throat and trailing edge are significantly reduced. The incidence at the rotor inlet is weakened, and the vortex structures around the blade leading and trailing edges are suppressed, effectively mitigating flow separation and thereby improving the overall thermodynamic performance of the turbine.