{"title":"基于深度强化学习的多扇风洞瞬态风场主动模拟","authors":"Shaopeng Li, Reda Snaiki, Teng Wu","doi":"10.1061/(ASCE)EM.1943-7889.0001967","DOIUrl":null,"url":null,"abstract":"AbstractThe transient wind field during a nonsynoptic wind event (e.g., thunderstorm downburst) presents time-varying mean and nonstationary fluctuating components, and hence is not easy to be repr...","PeriodicalId":299892,"journal":{"name":"Journal of Engineering Mechanics-asce","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Active Simulation of Transient Wind Field in a Multiple-Fan Wind Tunnel via Deep Reinforcement Learning\",\"authors\":\"Shaopeng Li, Reda Snaiki, Teng Wu\",\"doi\":\"10.1061/(ASCE)EM.1943-7889.0001967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractThe transient wind field during a nonsynoptic wind event (e.g., thunderstorm downburst) presents time-varying mean and nonstationary fluctuating components, and hence is not easy to be repr...\",\"PeriodicalId\":299892,\"journal\":{\"name\":\"Journal of Engineering Mechanics-asce\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Engineering Mechanics-asce\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1061/(ASCE)EM.1943-7889.0001967\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Mechanics-asce","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1061/(ASCE)EM.1943-7889.0001967","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Active Simulation of Transient Wind Field in a Multiple-Fan Wind Tunnel via Deep Reinforcement Learning
AbstractThe transient wind field during a nonsynoptic wind event (e.g., thunderstorm downburst) presents time-varying mean and nonstationary fluctuating components, and hence is not easy to be repr...
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