现代信息科技2025，Vol.9Issue(8)：194-198,5.DOI:10.19850/j.cnki.2096-4706.2025.08.036
基于物理混合神经网络的涡流管性能研究
Research on the Performance of Vortex Tube Based on Physical Hybrid Neural Network
摘要
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.关键词
涡流管/预测模型/混合神经网络/温度性能Key words
vortex tube/predictive model/hybrid neural network/temperature performance分类
信息技术与安全科学引用本文复制引用
李申申,韩志宏,刘蜀阳,黄志远,甘德俊..基于物理混合神经网络的涡流管性能研究[J].现代信息科技,2025,9(8):194-198,5.基金项目
国家自然科学基金(52066006) （52066006）
景德镇市科技局项目(2019GYZD008-13) （2019GYZD008-13）
