A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of inte...

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Uložené v:
Podrobná bibliografia
Vydané v:Structural and multidisciplinary optimization Ročník 65; číslo 12
Hlavní autori: Thelen, Adam, Zhang, Xiaoge, Fink, Olga, Lu, Yan, Ghosh, Sayan, Youn, Byeng D., Todd, Michael D., Mahadevan, Sankaran, Hu, Chao, Hu, Zhen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022
Springer Nature B.V
Predmet:
Advanced Optimization Enabling Digital Twin Technology
Computational Mathematics and Numerical Analysis
Design optimization
Digital twins
Engineering
Engineering Design
Literature reviews
Modelling
New technology
Quality control
Review Paper
Theoretical and Applied Mechanics
Uncertainty
Enabling technology
Perspective
Digital twin
Machine learning
Industry 4.0
Review
Optimization
ISSN:1615-147X, 1615-1488
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Abstract As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on Github .
AbstractList As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on Github.
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on Github .
ArticleNumber 354
Author Thelen, Adam
Hu, Chao
Youn, Byeng D.
Lu, Yan
Zhang, Xiaoge
Hu, Zhen
Todd, Michael D.
Fink, Olga
Ghosh, Sayan
Mahadevan, Sankaran
Author_xml – sequence: 1
  givenname: Adam
  surname: Thelen
  fullname: Thelen, Adam
  organization: Department of Mechanical Engineering, Iowa State University
– sequence: 2
  givenname: Xiaoge
  surname: Zhang
  fullname: Zhang, Xiaoge
  organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University
– sequence: 3
  givenname: Olga
  surname: Fink
  fullname: Fink, Olga
  organization: Intelligent Maintenance and Operations Systems, EPFL
– sequence: 4
  givenname: Yan
  surname: Lu
  fullname: Lu, Yan
  organization: Information Modeling and Testing Group, National Institute of Standards and Technology
– sequence: 5
  givenname: Sayan
  surname: Ghosh
  fullname: Ghosh, Sayan
  organization: Probabilistic Design and Optimization Group, GE Research
– sequence: 6
  givenname: Byeng D.
  surname: Youn
  fullname: Youn, Byeng D.
  organization: Department of Mechanical Engineering, Seoul National University
– sequence: 7
  givenname: Michael D.
  surname: Todd
  fullname: Todd, Michael D.
  organization: Department of Structural Engineering, University of California, San Diego
– sequence: 8
  givenname: Sankaran
  surname: Mahadevan
  fullname: Mahadevan, Sankaran
  organization: Department of Civil and Environmental Engineering, Vanderbilt University
– sequence: 9
  givenname: Chao
  orcidid: 0000-0001-9228-7675
  surname: Hu
  fullname: Hu, Chao
  email: chao.hu@uconn.edu, huchaostu@gmail.com
  organization: Department of Mechanical Engineering, Iowa State University, Department of Mechanical Engineering, University of Connecticut
– sequence: 10
  givenname: Zhen
  surname: Hu
  fullname: Hu, Zhen
  organization: Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn
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Optimization
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Snippet As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process...
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SubjectTerms Advanced Optimization Enabling Digital Twin Technology
Computational Mathematics and Numerical Analysis
Design optimization
Digital twins
Engineering
Engineering Design
Literature reviews
Modelling
New technology
Quality control
Review Paper
Theoretical and Applied Mechanics
Uncertainty
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Title A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
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