{"title":"死腿稳态热性能参数化模型","authors":"A. Sanchis, S. Andersson, A. Jensen","doi":"10.1115/OMAE2018-78407","DOIUrl":null,"url":null,"abstract":"During thermal design of Subsea Production Systems (SPS), Computational Fluid Dynamics (CFD) is used to calculate production fluid temperatures in dead legs of the system. One purpose of such simulations could be to calculate the amount of insulation needed to avoid low temperatures in the piping system. A novel approach to this type of analysis is presented here to build a parametric model able to map the dead leg performance against any set of input parameters. The workflow relies on a response surface analysis performed from the results of a limited set of CFD simulations run on a sparse simulation matrix that covers the design space. Once generated, the parametric model provides real-time results and may be used for screening, optimization or condition monitoring purposes.","PeriodicalId":155568,"journal":{"name":"Volume 5: Pipelines, Risers, and Subsea Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parametric Model of Dead Leg Steady-State Thermal Performance\",\"authors\":\"A. Sanchis, S. Andersson, A. Jensen\",\"doi\":\"10.1115/OMAE2018-78407\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"During thermal design of Subsea Production Systems (SPS), Computational Fluid Dynamics (CFD) is used to calculate production fluid temperatures in dead legs of the system. One purpose of such simulations could be to calculate the amount of insulation needed to avoid low temperatures in the piping system. A novel approach to this type of analysis is presented here to build a parametric model able to map the dead leg performance against any set of input parameters. The workflow relies on a response surface analysis performed from the results of a limited set of CFD simulations run on a sparse simulation matrix that covers the design space. Once generated, the parametric model provides real-time results and may be used for screening, optimization or condition monitoring purposes.\",\"PeriodicalId\":155568,\"journal\":{\"name\":\"Volume 5: Pipelines, Risers, and Subsea Systems\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 5: Pipelines, Risers, and Subsea Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/OMAE2018-78407\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 5: Pipelines, Risers, and Subsea Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/OMAE2018-78407","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parametric Model of Dead Leg Steady-State Thermal Performance
During thermal design of Subsea Production Systems (SPS), Computational Fluid Dynamics (CFD) is used to calculate production fluid temperatures in dead legs of the system. One purpose of such simulations could be to calculate the amount of insulation needed to avoid low temperatures in the piping system. A novel approach to this type of analysis is presented here to build a parametric model able to map the dead leg performance against any set of input parameters. The workflow relies on a response surface analysis performed from the results of a limited set of CFD simulations run on a sparse simulation matrix that covers the design space. Once generated, the parametric model provides real-time results and may be used for screening, optimization or condition monitoring purposes.