Multi-objective shape optimization of helico-axial multiphase pump impeller based on NSGA-II and ANN

In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influencing the impeller’s performance were chosen as the optimization variables, and the sample spaces were structured according to the orthogonal experimental design method. Then the pressure rise and efficiency in specific working conditions were obtained about all the elements in the sample space by numerical simulation. With the simulated results as the input specimen, a multiphase pump performance prediction model was designed through BP neural network. With the obtained prediction model as the fitness value evaluation method, the pump impeller was optimized using the NSGA-II multi-objective genetic algorithm, which finally offered an improved impeller structure with enhanced pressure rise and efficiency. Furthermore, five stages of optimized compression cells were manufactured and applied in experiment test. The result shows compared to the original design, the pressure rise of the optimized pump has increased by ∼10% and the efficiency has increased by ∼3%, which is in keeping with our optimal result and confirms our method is feasible.