Study on Static Characteristics Prediction of Porous Hydrostatic Bearing
In the design of porous hydrostatic bearings,bearing's design parameter is one of the key factors affecting its static and dynamic characteristics.Normally,several repetitions of modeling and simulation are required to obtain suitable bearing design parameters,and due to the complex bearing structure makes modeling difficult and simulation time long,which seriously affects the efficiency of bearing design.In this paper,a bearing static characteristic prediction model based on genetic algorithm(GA)optimized BP(Back Propagation)neural network is constructed,Latin hypercube sampling method is used to sample the bearing parameter design space,and perform Fluent fluid simulation.The data is used for the training and testing of the GA-BP neural network model to realize the prediction of static characteristics of porous hydrostatic bearing under any design parameters in the design space.The research results show that the trained GA-BP neural network model can accurately predict the static characteristics of porous hydrostatic bearings with a prediction accuracy of over 95%,which is great significance for the rapid design and parameter optimization of porous hydrostatic bearings.
国家科技期刊平台
NSTL
万方数据
