Continuous calibration of a digital twin: Comparison of particle filter and Bayesian calibration approaches

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2020-11-19 DOI:10.1017/dce.2021.12
R. Ward, R. Choudhary, A. Gregory, M. Jans-Singh, M. Girolami
{"title":"Continuous calibration of a digital twin: Comparison of particle filter and Bayesian calibration approaches","authors":"R. Ward, R. Choudhary, A. Gregory, M. Jans-Singh, M. Girolami","doi":"10.1017/dce.2021.12","DOIUrl":null,"url":null,"abstract":"Abstract Assimilation of continuously streamed monitored data is an essential component of a digital twin; the assimilated data are used to ensure the digital twin represents the monitored system as accurately as possible. One way this is achieved is by calibration of simulation models, whether data-derived or physics-based, or a combination of both. Traditional manual calibration is not possible in this context; hence, new methods are required for continuous calibration. In this paper, a particle filter methodology for continuous calibration of the physics-based model element of a digital twin is presented and applied to an example of an underground farm. The methodology is applied to a synthetic problem with known calibration parameter values prior to being used in conjunction with monitored data. The proposed methodology is compared against static and sequential Bayesian calibration approaches and compares favourably in terms of determination of the distribution of parameter values and analysis run times, both essential requirements. The methodology is shown to be potentially useful as a means to ensure continuing model fidelity.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DataCentric Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dce.2021.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 9

Abstract

Abstract Assimilation of continuously streamed monitored data is an essential component of a digital twin; the assimilated data are used to ensure the digital twin represents the monitored system as accurately as possible. One way this is achieved is by calibration of simulation models, whether data-derived or physics-based, or a combination of both. Traditional manual calibration is not possible in this context; hence, new methods are required for continuous calibration. In this paper, a particle filter methodology for continuous calibration of the physics-based model element of a digital twin is presented and applied to an example of an underground farm. The methodology is applied to a synthetic problem with known calibration parameter values prior to being used in conjunction with monitored data. The proposed methodology is compared against static and sequential Bayesian calibration approaches and compares favourably in terms of determination of the distribution of parameter values and analysis run times, both essential requirements. The methodology is shown to be potentially useful as a means to ensure continuing model fidelity.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
数字孪生的连续校准:粒子滤波器和贝叶斯校准方法的比较
摘要连续流监测数据的同化是数字孪生的重要组成部分;同化的数据用于确保数字孪生尽可能准确地表示被监控的系统。实现这一点的一种方法是校准模拟模型,无论是基于数据还是基于物理,或者两者结合。在这种情况下,传统的手动校准是不可能的;因此,需要新的方法来进行连续校准。本文提出了一种粒子滤波方法,用于连续校准基于物理的数字孪生模型元素,并将其应用于地下农场的一个例子。在与监测数据一起使用之前,该方法适用于具有已知校准参数值的合成问题。将所提出的方法与静态和顺序贝叶斯校准方法进行了比较,并在参数值分布的确定和分析运行时间方面进行了比较。该方法被证明是一种潜在的有用手段,以确保持续的模型保真度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
Semantic 3D city interfaces—Intelligent interactions on dynamic geospatial knowledge graphs Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes Finite element model updating with quantified uncertainties using point cloud data Evaluating probabilistic forecasts for maritime engineering operations Bottom-up forecasting: Applications and limitations in load forecasting using smart-meter data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1