Huaichao Wu, Zhao Peng, Junqi Mu, Limei Zhao, Lv Yang
{"title":"重型自动变速器锁紧阀流力分析与优化","authors":"Huaichao Wu, Zhao Peng, Junqi Mu, Limei Zhao, Lv Yang","doi":"10.1139/tcsme-2021-0143","DOIUrl":null,"url":null,"abstract":"One of the main factors determining the stability of lock valve during opening is the flow force on spool. The size of the flow force profoundly affects the dynamic characteristics of the spool. In this paper, the flow force on a heavy-duty automatic transmission lock valve during the opening process is analyzed and optimized, and aiming to improve the opening smoothness of the lock valve. First, numerical simulation of the opening process of the main oil chamber flow path in the lock valve is carried out using dynamic mesh technology. The influence of internal flow field on the flow force under different parameters is studied. Second, the structural parameters and peaks of flow force obtained from the random sampling method are used as samples for training and prediction using the BP neural network. The prediction results pass the accuracy test. Last, the prediction results of the BP neural network are optimized using the genetic algorithm. Subsequent results show that this optimization method significantly reduces the flow force of spool and improves stability of the lock valve during opening by only changing the structural parameters. It also provides a new systematic direction for the optimization of other nonlinear mapping relationships.","PeriodicalId":23285,"journal":{"name":"Transactions of The Canadian Society for Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Flow force analysis and optimization of lock valve for heavy-duty automatic transmission\",\"authors\":\"Huaichao Wu, Zhao Peng, Junqi Mu, Limei Zhao, Lv Yang\",\"doi\":\"10.1139/tcsme-2021-0143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the main factors determining the stability of lock valve during opening is the flow force on spool. The size of the flow force profoundly affects the dynamic characteristics of the spool. In this paper, the flow force on a heavy-duty automatic transmission lock valve during the opening process is analyzed and optimized, and aiming to improve the opening smoothness of the lock valve. First, numerical simulation of the opening process of the main oil chamber flow path in the lock valve is carried out using dynamic mesh technology. The influence of internal flow field on the flow force under different parameters is studied. Second, the structural parameters and peaks of flow force obtained from the random sampling method are used as samples for training and prediction using the BP neural network. The prediction results pass the accuracy test. Last, the prediction results of the BP neural network are optimized using the genetic algorithm. Subsequent results show that this optimization method significantly reduces the flow force of spool and improves stability of the lock valve during opening by only changing the structural parameters. It also provides a new systematic direction for the optimization of other nonlinear mapping relationships.\",\"PeriodicalId\":23285,\"journal\":{\"name\":\"Transactions of The Canadian Society for Mechanical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2022-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions of The Canadian Society for Mechanical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1139/tcsme-2021-0143\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of The Canadian Society for Mechanical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1139/tcsme-2021-0143","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Flow force analysis and optimization of lock valve for heavy-duty automatic transmission
One of the main factors determining the stability of lock valve during opening is the flow force on spool. The size of the flow force profoundly affects the dynamic characteristics of the spool. In this paper, the flow force on a heavy-duty automatic transmission lock valve during the opening process is analyzed and optimized, and aiming to improve the opening smoothness of the lock valve. First, numerical simulation of the opening process of the main oil chamber flow path in the lock valve is carried out using dynamic mesh technology. The influence of internal flow field on the flow force under different parameters is studied. Second, the structural parameters and peaks of flow force obtained from the random sampling method are used as samples for training and prediction using the BP neural network. The prediction results pass the accuracy test. Last, the prediction results of the BP neural network are optimized using the genetic algorithm. Subsequent results show that this optimization method significantly reduces the flow force of spool and improves stability of the lock valve during opening by only changing the structural parameters. It also provides a new systematic direction for the optimization of other nonlinear mapping relationships.
期刊介绍:
Published since 1972, Transactions of the Canadian Society for Mechanical Engineering is a quarterly journal that publishes comprehensive research articles and notes in the broad field of mechanical engineering. New advances in energy systems, biomechanics, engineering analysis and design, environmental engineering, materials technology, advanced manufacturing, mechatronics, MEMS, nanotechnology, thermo-fluids engineering, and transportation systems are featured.