Critical heat flux density prediction method based on deep learning support vector machine

Abstract

The invention discloses a critical heat flux density prediction method based on a deep learning support vector machine. The method is specifically implemented according to the following steps: step 1,dividing collected critical heat flux density original data into two parts, wherein 70% of the original data serves as a training set and is represented by X = {(x1, y1), (x2, y2),... (xi, yi)... (xn, yn)}, normalization processing is conducted on xi in the obtained training data set through linear transformation, and a data point x'i obtained after normalization processing is obtained; the other30% of the original data is taken as a test set for testing the prediction accuracy of the prediction model obtained by training; step 2, selecting experimental data from the normalized data points x'i obtained in the step 1 by using subtraction clustering added with information potential, wherein i is equal to 1, 2,..., n; and step 3, optimizing parameters of a support vector machine by performing cross validation on the experimental data obtained in the step 2 through a one-step method, and performing training by adopting a restricted Boltzmann machine in deep learning to obtain an optimalprediction model and optimal parameters. The prediction method can predict the critical heat flux density more accurately.

Description

technical field [0001] The invention belongs to the technical field of reactor core safety analysis methods, and in particular relates to a critical heat flux prediction method based on deep learning support vector machines. Background technique [0002] The nuclear reactor is one of the key parts of the nuclear power plant, and it is also a high-intensity heat exchange equipment. However, since the heat flux in the core is limited by the critical heat flux, the power level of the reactor is also limited by the critical heat flux. Therefore, in the nuclear reactor system, the critical heat flux is an important parameter in the thermal-hydraulic design of the reactor core, and has an extremely important influence on the safe operation of the nuclear reactor. [0003] The critical heat flux is also known as the maximum heat flux, which means that under the condition of constant temperature heating, when the superheat of the wall surface rises to a certain value, the steam on ...

AI Technical Summary

Problems solved by technology

The traditional three critical heat flux prediction methods have developed the critical heat flux prediction method to a certain extent, but they can only be used within a specific parameter range

Since the critical heat flux is affected by many uncertain factors, until now there is no definite theory to accurately predict the critical heat flux

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