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Fast prediction model based on high flow check valve experimental system
Published:2024-04-24 author:WANG Jiangkun, ZHAO Jing, ZHA Sasa, et al. Browse: 989 Check PDF documents
Fast prediction model based on high flow check valve experimental system


WANG Jiangkun1, ZHAO Jing1,3, ZHA Sasa1, WANG Jian2, QU Mingjian1,3, ZHANG Junfei2


(1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Shenyang Shengshi Wuhuan 

Technology Co., Ltd., Fushun 110172, China; 3.Liaoning Wuhuan Special Materials and Intelligent Equipment Industrial Technology 

Research Institute, Shenyang 118015, China)


Abstract:  Aiming at the limitations of sensors for data acquisition and the inability to collect comprehensive data for the experimental system, the fast prediction modeling technology of the high flow check valve experimental system was studied. The rapid prediction model of the experimental system was established, and the results of the rapid prediction model were analyzed.First of all, the solid model was constructed and based on the structure of the experimental system of high flow check valve. At the same time, the working principle of the experimental system was combined to simplify it and carry out finite element analysis. Then, the key technology of fast prediction model was used to construct the experimental system database and realize the sample collection of the experimental system. By comparing the prediction accuracy of different machine learning algorithms, the random forest (RF)algorithm was selected to establish the relationship between the internal pressure and stress-strain of the experimental system. Finally, the results of the fast prediction model were analyzed, and the overall prediction experiment of the experimental system and the individual prediction experiment of the experimental system components were done. The results show that the fast prediction model established by the random forest algorithm has a goodness of fit (R2) of 0.99, which is 68.97% and 51.47% higher compared to the deep neural network (DNN) algorithm and gradient boosted tree (GBDT) algorithm. Comparison tests between the overall prediction of the experimental system and the individual prediction of the experimental system components show that the overall prediction model has a faster prediction speed and an accuracy of 97.43%.

Key words: large flow swing check valve; non-return valve; random forest(RF) algorithm; response time; deep neural network(DNN); gradient boosting tree(GBDT); coefficient of determination(R2)
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