Changming Li, Bingchen Liang, Peng Yuan, Qin Zhang, Yongkai Liu, Bin Liu, Ming Zhao
{"title":"基于深度学习的螺旋桨动态尾流快速预测","authors":"Changming Li, Bingchen Liang, Peng Yuan, Qin Zhang, Yongkai Liu, Bin Liu, Ming Zhao","doi":"10.1063/5.0220551","DOIUrl":null,"url":null,"abstract":"Efficiently predicting the wake of propellers is of great importance for achieving propeller design optimization. In this work, the deep learning (DL) method called propeller wake convolutional neural networks (PWCNN) is proposed, which combines the transformer encoder and dilated convolutional block to capture the multi-scale characteristics of wakes. Computational fluid dynamics (CFD) simulations are conducted using the delayed detached eddy simulation model for the wake to generate extensive high-fidelity wake data of the propeller operating under different operating conditions required for DL. PWCNN takes the wake predicted at the previous time step to update input and iteratively predicts the wake at future time steps to achieve dynamic wake prediction. The good agreement between DL prediction and CFD simulation results, with the mean relative error of the velocity components less than 2.36% for 15 future time steps, proves that PWCNN can efficiently capture the spatiotemporal evolution characteristic of dynamic wakes. Furthermore, PWCNN can predict the wake dynamic changes with reasonable accuracy under unseen operating conditions, further confirming the generality of the proposed model in forecasting the spatiotemporal evolution of propeller wake.","PeriodicalId":20066,"journal":{"name":"Physics of Fluids","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast prediction of propeller dynamic wake based on deep learning\",\"authors\":\"Changming Li, Bingchen Liang, Peng Yuan, Qin Zhang, Yongkai Liu, Bin Liu, Ming Zhao\",\"doi\":\"10.1063/5.0220551\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Efficiently predicting the wake of propellers is of great importance for achieving propeller design optimization. In this work, the deep learning (DL) method called propeller wake convolutional neural networks (PWCNN) is proposed, which combines the transformer encoder and dilated convolutional block to capture the multi-scale characteristics of wakes. Computational fluid dynamics (CFD) simulations are conducted using the delayed detached eddy simulation model for the wake to generate extensive high-fidelity wake data of the propeller operating under different operating conditions required for DL. PWCNN takes the wake predicted at the previous time step to update input and iteratively predicts the wake at future time steps to achieve dynamic wake prediction. The good agreement between DL prediction and CFD simulation results, with the mean relative error of the velocity components less than 2.36% for 15 future time steps, proves that PWCNN can efficiently capture the spatiotemporal evolution characteristic of dynamic wakes. Furthermore, PWCNN can predict the wake dynamic changes with reasonable accuracy under unseen operating conditions, further confirming the generality of the proposed model in forecasting the spatiotemporal evolution of propeller wake.\",\"PeriodicalId\":20066,\"journal\":{\"name\":\"Physics of Fluids\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physics of Fluids\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0220551\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics of Fluids","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1063/5.0220551","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MECHANICS","Score":null,"Total":0}
Fast prediction of propeller dynamic wake based on deep learning
Efficiently predicting the wake of propellers is of great importance for achieving propeller design optimization. In this work, the deep learning (DL) method called propeller wake convolutional neural networks (PWCNN) is proposed, which combines the transformer encoder and dilated convolutional block to capture the multi-scale characteristics of wakes. Computational fluid dynamics (CFD) simulations are conducted using the delayed detached eddy simulation model for the wake to generate extensive high-fidelity wake data of the propeller operating under different operating conditions required for DL. PWCNN takes the wake predicted at the previous time step to update input and iteratively predicts the wake at future time steps to achieve dynamic wake prediction. The good agreement between DL prediction and CFD simulation results, with the mean relative error of the velocity components less than 2.36% for 15 future time steps, proves that PWCNN can efficiently capture the spatiotemporal evolution characteristic of dynamic wakes. Furthermore, PWCNN can predict the wake dynamic changes with reasonable accuracy under unseen operating conditions, further confirming the generality of the proposed model in forecasting the spatiotemporal evolution of propeller wake.
期刊介绍:
Physics of Fluids (PoF) is a preeminent journal devoted to publishing original theoretical, computational, and experimental contributions to the understanding of the dynamics of gases, liquids, and complex or multiphase fluids. Topics published in PoF are diverse and reflect the most important subjects in fluid dynamics, including, but not limited to:
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