The wall shear stress of ultra-high-pressure water jet impinging on the ship hull can be regarded as an important indicator to reflect the working efficiency of rust removal operation,and it also acts as a crucial criterion to assess the reasonability of the internal structure parameters related of the nozzles.Several parameters should be considered when the hydrodynamic performance of nozzles is optimized.If only relying on CFD simulations,the computational consumption is too high.In this paper,the straight-cone nozzle is taken as the research object,a prediction model for water jet performance is established,by using CFD simulation and neural network modelling simultaneously.By means of adopting the orthogonal test rule to set simulation cases and regarding the wall shear stress of water jet on target surface as a core indicator,the primary and secondary effect sequence of the internal structure parameters of nozzle on the hydrodynamic performance of water jetting is obtained.It provides a reference for the shape lines design of nozzle,as well as for the optimization of the structure parameters.The results show that the length of the exit section has the greatest effect on the water-jet performance among the four internal structure parameters,and the optimal combination of those four parameters corresponding to the larger wall shear stress is also provided.The proposed KGWO-BP model can provide reliable technical support for the optimization of the straight-cone nozzle,because of its high prediction accuracy and high generalization performance.
关键词
超高压水射流/直锥型喷嘴/结构参数/灰狼算法/BP神经网络
Key words
Ultra-high-pressure water jet/Straight-cone nozzle/Structure parameter/Grey wolf algorithm/BP neural network
维普
万方数据