{"title":"基于分布式传感序列数据同化的海底线结构行为估计","authors":"S. Kojima, Ryota Wada, H. Murayama","doi":"10.1115/1.4056846","DOIUrl":null,"url":null,"abstract":"\n In this paper, a method that estimates the real-time behavior of subsea line structures based on sequential data assimilation with distributed strain sensors is proposed. A finite element method is used to represent the behavior of subsea line structures and generates ensemble forecasts regarding unknown parameters. A merging particle filter technique is applied to integrate the observation data with the numerical models to calculate the posterior probability density function. The effectiveness of the proposed method is examined through twin experiments. The presented results validate the proposed method's capability to estimate the current state as well as unknown parameters of subsea line structures. The results suggest the advantage of distributed sensors against pointwise sensing when applied to line structures.","PeriodicalId":50106,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of Subsea Line Structure Behavior Based on Sequential Data Assimilation with Distributed Sensing\",\"authors\":\"S. Kojima, Ryota Wada, H. Murayama\",\"doi\":\"10.1115/1.4056846\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n In this paper, a method that estimates the real-time behavior of subsea line structures based on sequential data assimilation with distributed strain sensors is proposed. A finite element method is used to represent the behavior of subsea line structures and generates ensemble forecasts regarding unknown parameters. A merging particle filter technique is applied to integrate the observation data with the numerical models to calculate the posterior probability density function. The effectiveness of the proposed method is examined through twin experiments. The presented results validate the proposed method's capability to estimate the current state as well as unknown parameters of subsea line structures. The results suggest the advantage of distributed sensors against pointwise sensing when applied to line structures.\",\"PeriodicalId\":50106,\"journal\":{\"name\":\"Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-02-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4056846\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4056846","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Estimation of Subsea Line Structure Behavior Based on Sequential Data Assimilation with Distributed Sensing
In this paper, a method that estimates the real-time behavior of subsea line structures based on sequential data assimilation with distributed strain sensors is proposed. A finite element method is used to represent the behavior of subsea line structures and generates ensemble forecasts regarding unknown parameters. A merging particle filter technique is applied to integrate the observation data with the numerical models to calculate the posterior probability density function. The effectiveness of the proposed method is examined through twin experiments. The presented results validate the proposed method's capability to estimate the current state as well as unknown parameters of subsea line structures. The results suggest the advantage of distributed sensors against pointwise sensing when applied to line structures.
期刊介绍:
The Journal of Offshore Mechanics and Arctic Engineering is an international resource for original peer-reviewed research that advances the state of knowledge on all aspects of analysis, design, and technology development in ocean, offshore, arctic, and related fields. Its main goals are to provide a forum for timely and in-depth exchanges of scientific and technical information among researchers and engineers. It emphasizes fundamental research and development studies as well as review articles that offer either retrospective perspectives on well-established topics or exposures to innovative or novel developments. Case histories are not encouraged. The journal also documents significant developments in related fields and major accomplishments of renowned scientists by programming themed issues to record such events.
Scope: Offshore Mechanics, Drilling Technology, Fixed and Floating Production Systems; Ocean Engineering, Hydrodynamics, and Ship Motions; Ocean Climate Statistics, Storms, Extremes, and Hurricanes; Structural Mechanics; Safety, Reliability, Risk Assessment, and Uncertainty Quantification; Riser Mechanics, Cable and Mooring Dynamics, Pipeline and Subsea Technology; Materials Engineering, Fatigue, Fracture, Welding Technology, Non-destructive Testing, Inspection Technologies, Corrosion Protection and Control; Fluid-structure Interaction, Computational Fluid Dynamics, Flow and Vortex-Induced Vibrations; Marine and Offshore Geotechnics, Soil Mechanics, Soil-pipeline Interaction; Ocean Renewable Energy; Ocean Space Utilization and Aquaculture Engineering; Petroleum Technology; Polar and Arctic Science and Technology, Ice Mechanics, Arctic Drilling and Exploration, Arctic Structures, Ice-structure and Ship Interaction, Permafrost Engineering, Arctic and Thermal Design.