{"title":"利用基于深度学习的接合预测分析双速自动变速器中的离心离合器","authors":"Bo-Yi Lin, Kai Chun Lin","doi":"arxiv-2409.09755","DOIUrl":null,"url":null,"abstract":"This paper presents a comprehensive numerical analysis of centrifugal clutch\nsystems integrated with a two-speed automatic transmission, a key component in\nautomotive torque transfer. Centrifugal clutches enable torque transmission\nbased on rotational speed without external controls. The study systematically\nexamines various clutch configurations effects on transmission dynamics,\nfocusing on torque transfer, upshifting, and downshifting behaviors under\ndifferent conditions. A Deep Neural Network (DNN) model predicts clutch\nengagement using parameters such as spring preload and shoe mass, offering an\nefficient alternative to complex simulations. The integration of deep learning\nand numerical modeling provides critical insights for optimizing clutch\ndesigns, enhancing transmission performance and efficiency.","PeriodicalId":501162,"journal":{"name":"arXiv - MATH - Numerical Analysis","volume":"157 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction\",\"authors\":\"Bo-Yi Lin, Kai Chun Lin\",\"doi\":\"arxiv-2409.09755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a comprehensive numerical analysis of centrifugal clutch\\nsystems integrated with a two-speed automatic transmission, a key component in\\nautomotive torque transfer. Centrifugal clutches enable torque transmission\\nbased on rotational speed without external controls. The study systematically\\nexamines various clutch configurations effects on transmission dynamics,\\nfocusing on torque transfer, upshifting, and downshifting behaviors under\\ndifferent conditions. A Deep Neural Network (DNN) model predicts clutch\\nengagement using parameters such as spring preload and shoe mass, offering an\\nefficient alternative to complex simulations. The integration of deep learning\\nand numerical modeling provides critical insights for optimizing clutch\\ndesigns, enhancing transmission performance and efficiency.\",\"PeriodicalId\":501162,\"journal\":{\"name\":\"arXiv - MATH - Numerical Analysis\",\"volume\":\"157 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - MATH - Numerical Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.09755\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - MATH - Numerical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.09755","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction
This paper presents a comprehensive numerical analysis of centrifugal clutch
systems integrated with a two-speed automatic transmission, a key component in
automotive torque transfer. Centrifugal clutches enable torque transmission
based on rotational speed without external controls. The study systematically
examines various clutch configurations effects on transmission dynamics,
focusing on torque transfer, upshifting, and downshifting behaviors under
different conditions. A Deep Neural Network (DNN) model predicts clutch
engagement using parameters such as spring preload and shoe mass, offering an
efficient alternative to complex simulations. The integration of deep learning
and numerical modeling provides critical insights for optimizing clutch
designs, enhancing transmission performance and efficiency.