Article Details

Title · Authors · Abstract · Keywords · DOI · References

Research on the Performance of Vortex Tube Based on Physical Hybrid Neural Network

LI Shenshen1, HAN Zhihong2, LIU Shuyang2, HUANG Zhiyuan1, GAN Dejun1

(1.School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, China; 2.New Engineering Industry College, Putian University, Putian 351100, China)

Section：Intelligent Manufacturing · Vol. 2025, Issue 8 · Pages：194-198 · 2096-4706（2025）08-0194-05 · CLC number：TP183；TP399 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.08.036

Abstract

In this paper, a hybrid neural network model is constructed by adding the physical constraint conditions of the Bernoulli equation and the Nicolas formula, exploring the temperature change law of the cold end of the vortex tube and making corresponding predictions. The network adopts a multi-layer feedforward model and the Levenberg-Marquardt learning algorithm, and the hyperbolic tangent function is selected as the transfer function. In addition, the coefficient of determination (R2) and the Root Mean Square Error (RMSE) are used to determine the statistical validity of the developed model, and the model's uncertainty and robustness are analyzed. The hybrid model has an index R2 of 0.993 6 and an RMSE of 0.339 2, and also has a good performance in terms of uncertainty and robustness. These data indicate that the model constructed in this paper successfully predicts the changes in the temperature of the cold end of the vortex tube and has good accuracy.

Keywords

vortex tube; predictive model; hybrid neural network; temperature performance

References

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Funding

National Natural Science Foundation of China (52066006); Jingdezhen Science and Technology Bureau Project (2019GYZD008-13)

About the authors

Li Shenshen (1997-), male, Han ethnicity, from Zhoukou, Henan Province, is currently pursuing a master's degree in intelligent detection and control.