Supercritical fluid heat transfer correlation type proxy model construction method based on machine learning

Abstract

The invention discloses a supercritical fluid heat transfer correlation type proxy model construction method based on machine learning, and aims to solve the problems that a traditional or developed heat transfer experience correlation type is difficult to predict and poor in precision due to nonlinear physical property change of supercritical fluid. The method comprises the following steps: firstly, widely collecting experimental data, and evaluating and selecting thermal boundary, geometric and physical property dimensionless parameter factors which potentially influence a heat transfer grade; then, on the basis of a singular value decomposition technology, carrying out data order reduction processing, and achieving main flowing heat exchange feature recognition and extraction of samples; establishing a mathematical expression of the supercritical heat transfer model and a nonlinear RBF-MLP neural network structure, and training, verifying and optimally selecting the number of neurons of an input layer, a hidden layer and an output layer; and finally, enabling a prediction result to show that the heat transfer correlation type agent model has the characteristics of high prediction precision and small network error. The scheme is simple and reliable, and the purposes of accurately predicting the wall surface temperature and the heat transfer coefficient and reducing the test cost can be quickly achieved.

Description

technical field [0001] The invention belongs to the technical field of heat energy, and in particular relates to a method for constructing a fluid heat transfer correlation proxy model. Background technique [0002] Supercritical fluid has broad application prospects in nuclear energy, chemical industry, power, refrigeration, food, heat pump, aerospace and other fields. The fluid in the supercritical state is mainly characterized by drastic changes under different pressure and temperature conditions, especially near the critical point, which is also the difference between supercritical fluid and traditional conventional fluid. Root cause of thermal signature. In particular, when the temperature difference near the critical point is 1K, the specific heat capacity will change hundreds of times, and the values ​​of density, dynamic viscosity, thermal conductivity and thermal diffusivity will also undergo drastic changes. The thermal diffusivity of the fluid near the critical ...

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Problems solved by technology

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a method for constructing a supercritical fluid heat transfer correlative surrogate model based on machine learning, in order to solve the traditional or developed empirical correlative prediction of heat transfer caused by the nonlinear physical property changes of supercritical fluid Difficult and poor precision problems

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