Multi-Objective Optimization of Jet Pump Based on RBF Neural Network Model

Kai Xu,Xi Chen,Wenhao Sun,Liquan Wang,Feihong Yun,Luyao Zhang,Gang Wang,Xiangyu Wang

doi:10.3390/jmse9020236

Abstract

In this study, an annular jet pump optimization method is proposed based on an RBF (Radial Basis Function) neural network model and NSGA-II (Non-Dominated Sorting Genetic Algorithm) optimization algorithm to improve the hydraulic performance of the annular jet pump applied in submarine trenching and dredging. Suction angle, diffusion angle, area ratio and flow ratio were selected as design variables. The computational fluid dynamics (CFD) model was used for numerical simulation to obtain the corresponding performance, and an accurate RBF neural network approximate model was established. Finally, the NSGA-II algorithm was selected to carry out multi-objective optimization and obtain the optimal design variable combination. The results show that the determination coefficient R2 of the two objective functions (jet pump efficiency and head ratio) of the approximate model of the RBF neural network were greater than 0.97. Compared with the original model, the optimized model’s suction angle increased, and the diffusion angle, flow ratio and area ratio decreased. In terms of performance, the head ratio increased by 30.46% after the optimization of the jet pump, and efficiency increased slightly. The proposed jet pump performance optimization method provides a reference for improving the performance of other pumps.

Highlights

The annular jet pump utilizes a high-speed working fluid to entrain the low-speed fluid and realize the pressurization process
optimum space filling (OSF) is an improvement of the Latin hypercube sampling algorithm (LHS) that develops an efficient global optimal search algorithm, the enhanced stochastic evolutionary algorithm (ESE)
The design variables of 80 sample points generated by the OSF and the correspondCFD simulation results are used as inputs and outputs to establish an RBF neural network ing computational fluid dynamics (CFD) simulation results are used as inputs and outputs to establish an RBF neural netapproximate model

Summary

Introduction

The annular jet pump utilizes a high-speed working fluid to entrain the low-speed fluid and realize the pressurization process. This has the advantage of no moving parts in the suction channel and a simple, reliable, and accessible structure that is especially applicable for pumping fluids containing a great quantity of solid particles (mineral, live fish, gravel, etc.). The jet pump is the key technology of underwater trencher design [1,2,3]. With the development of engineering, higher requirements are being put forward for the performance of jet pumps in various aspects. Research into the internal mechanisms, optimization methods and performance improvements of jet pumps are important

Methods

Results

Discussion

Conclusion