摘要
Computational fluid dynamics(CFD)was used in conjunction with BP neural network to study theflow resistance characteristic of the combination-channel inside hydraulic manifold block(HMB).The in-put parameters of the combination-channel model were confirmed to have effect on the pressure-drop bythe numerical method,and a BP neural network model was accordingly constructed to predict the channelpressure-drops.The flow resistance characteristic curves of various channels were achieved,and a perfor-mance parameter was given to evaluate the through-flow characteristic of the channel according to thecurves.The predictions are' in agreement with the numerical computation,indicating that the method canbe utilized to accurately determine the flow characteristic of the combination channel with high efficiency.
Computational fluid dynamics （CFD） was used in conjunction with BP neural network to study the flow resistance characteristic of the combination-channel inside hydraulic manifold block （HMB）. The input parameters of the combination-channel model were confirmed to have effect on the pressure-drop by the numerical method, and a BP neural network model was accordingly constructed to predict the channel pressure-drops. The flow resistance characteristic curves of various channels were achieved, and a performance parameter was given to evaluate the through-flow characteristic of the channel according to the curves. The predictions are in agreement with the numerical computation, indicating that the method can be utilized to accurately determine the flow characteristic of the combination channel with high efficiency.
基金
the National Natural Science Foundation of China(No.50375023)
