A Prediction Method of Mass Transfer Relaxation Factor for Subcooled Flow Boiling Heat Exchange

Abstract

The invention provides a method for predicting the mass transfer relaxation factor of subcooled flow boiling heat transfer based on radial basis neural network, which includes trying out the mass transfer relaxation factor under different working conditions through CFD software; collecting flow parameters and Mass transfer relaxation factor; determine the input and output variables of the RBF neural network; train and test the RBF neural network; use the trained RBF neural network to predict the mass transfer relaxation factor under different working conditions, and realize the mass transfer of subcooled flow boiling heat transfer Prediction of relaxation factors. The invention considers the influence of different flow parameters on the mass transfer relaxation factor, tries to gather the best mass transfer relaxation factor under each working condition, and establishes the RBF neural network prediction model based on the flow parameters and the mass transfer relaxation factor. By directly inputting the flow parameters under different working conditions into the RBF neural network prediction model, the mass transfer relaxation factor of the boiling heat transfer in the subcooled flow of the cooling channel can be predicted quickly and accurately.

Description

technical field The invention relates to a method for predicting a mass transfer relaxation factor, in particular to a method for predicting a mass transfer relaxation factor for subcooled flow boiling heat transfer based on a radial basis (Radial Basis Function, RBF) neural network. Background technique At present, with the continuous improvement of heat exchange requirements, common heat exchange methods such as convective heat exchange can no longer meet the heat exchange needs of some industries. The subcooled flow boiling heat transfer has an important application prospect in many industries (such as internal combustion engine industry, refrigeration industry, nuclear industry) because of its high heat transfer capacity and can achieve the effect of high temperature cooling. In recent years, scholars at home and abroad have conducted a lot of research on the boiling heat transfer of the subcooled flow in the cooling channel. In terms of simulation, the heat flux density...

AI Technical Summary

Problems solved by technology

In view of the existing deficiencies, the present invention proposes a calculation method for the mass transfer relaxation factor of subcooled flow boiling heat transfer based on radial basis (RBF) neural network, which takes into account the significant effect of different flow parameters on the mass transfer relaxation factor influence, based on the flow parameters and mass transfer relaxation factor to establish a prediction model, thereby effectively solving the problem of large calculation errors in the numerical calculation of subcooled flow boiling heat transfer using a fixed value of mass transfer relaxation factor in the prior art, and improving the calculation of heat flux density precision

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