基于物理混合神經(jīng)網(wǎng)絡的渦流管性能研究

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天鍵詞：渦流管；預測模型；混合神經(jīng)網(wǎng)絡；溫度性能中圖分類號：TP183；TP399 文獻標識碼：A文章編號：2096-4706（2025）08-0194-05

Abstract： Inthis paper，a hybrid neural network model is constructed by adding the physical constraint conditions of theBernoulliequationandtheNicolas formula，exploringthetemperaturechangelawof thecoldendofthevortex tubeand making corrsponding predictions.The network adoptsa multi-layer fedforward model andthe Levenberg-Marquardt learning algorithm，andtehypebolictangentfunctionisselecedasthetransferfuncion.Inadditio，thecofcientofdeteation and the Root Mean Square Eror （RMSE）areused to determine the statistical validityof the developed model，and he model's uncertainty and robustness are analyzed.The hybrid model has an index of 0.9936and anRMSEof0.3392，and also has agood performance in tersofuncertaintyand robustnessThesedata indicate thatthe modelconstructed 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

0 引言

渦旋管又稱為Ranque-Hilsch管（RHVT），其是一種簡單的裝置，由一根簡單的圓形管、一個或多個切向噴嘴、冷端孔和一個熱端控制閥組成（如圖1所示）。（剩余7218字）