Radial basis function (RBF) neural network-based boiling heat exchanging prediction method

Abstract

The invention provides a radial basis function (RBF) neural network-based boiling heat exchanging prediction method, in particular a radial basis neural network-based boiling heat exchanging prediction method of mixed medium flowing inside a horizontal plain tube, and the method comprises the following steps that: collecting data, determining input and output vectors of a network, preprocessing data, training and testing an RBF neural network, utilizing the neural network after being trained for prediction to obtain the predicted flowing boiling heat exchanging coefficient, and realizing the prediction of boiling heat exchange of the mixed medium flowing inside the horizontal plain tube. Due to the adoption of the method, the complicated internal mechanism for analyzing a mixed medium flowing boiling heat exchanging process can be avoided, the experimental times can be reduced, the flowing boiling heat exchanging of the mixed medium can be correctly and rapidly predicted through the simulation test of a computer, the precision is remarkably improved compared to a traditional correlation way, and a good instruction significance on predicting the performance and optimizing the structure of a tube-type heat exchanger in a mixed medium refrigerating system can be realized.

Description

technical field [0001] The invention relates to a radial basis (Radial Basis Function, RBF) neural network-based method for predicting the flow and boiling heat transfer of a mixed working medium in a horizontal light tube, which belongs to the technical field of computer artificial intelligence in refrigeration and thermal energy engineering. Background technique [0002] At present, the global energy crisis and environmental problems are intensifying, and the refrigeration and air-conditioning industry is facing the severe test of developing environmentally friendly refrigerants, improving system efficiency and reducing equipment costs. The mixed working fluid has been paid more and more attention due to its unique properties, so accurately grasping the flow boiling heat transfer performance of the mixed working fluid has become the key to the design of heat exchangers using mixed working fluid refrigeration systems. In recent years, scholars at home and abroad have conduc...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the existing deficiencies, the present invention proposes an artificial neural network-based method for predicting the flow boiling heat transfer of the mixed working fluid in the horizontal light tube. This method can avoid analyzing the complicated internal mechanism of the mixed working fluid flow boiling heat transfer process, thereby effectively Solve the problem that the calculation error of traditional correlation is generally large, and improve the accuracy of prediction

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