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A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

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A Correction to this article was published on 07 January 2023

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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 second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open-source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on Github.

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Acknowledgements

Adam Thelen and Chao Hu would like to thank the financial support from the U.S. National Science Foundation under Grant No. ECCS-2015710. Xiaoge Zhang is supported by a grant from the Research Committee of The Hong Kong Polytechnic University under project code 1-BE6V. Sankaran Mahadevan acknowledges the support of the National Institute of Science and Technology. Michael D. Todd and Zhen Hu received financial support from the U.S. Army Corps of Engineers through the U.S. Army Engineer Research and Development Center Research Cooperative Agreement W912HZ-17-2-0024.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Iowa State University, Ames, IA, 50011, USA

    Adam Thelen & Chao Hu

  2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Xiaoge Zhang

  3. Intelligent Maintenance and Operations Systems, EPFL, 1015, Lausanne, Switzerland

    Olga Fink

  4. Information Modeling and Testing Group, National Institute of Standards and Technology, Gaithersburg, MD, 20877, USA

    Yan Lu

  5. Probabilistic Design and Optimization group, GE Research, Niskayuna, NY, 12309, USA

    Sayan Ghosh

  6. Department of Mechanical Engineering, Seoul National University, Gwanak-gu, Seoul, 151-742, Republic of Korea

    Byeng D. Youn

  7. Department of Structural Engineering, University of California, San Diego, La Jolla, CA, 92093, USA

    Michael D. Todd

  8. Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Sankaran Mahadevan

  9. Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, MI, 48128, USA

    Zhen Hu

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  1. Adam Thelen
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Contributions

All authors read and approved the final manuscript. CH and ZH devised the original concept of the review paper. ZH, AT, and XZ were responsible for the literature review. AT was responsible for geometric modeling. CH, AT, and ZH were responsible for physics-based modeling. ZH was responsible for data-driven modeling. CH and ZH were responsible for physics-informed ML. XZ was responsible for system modeling. CH and ZH were responsible for probabilistic model updating. XZ was responsible for ML model updating. CH and ZH were responsible for fault diagnostics, failure prognostics, and predictive maintenance. YL was responsible for MPC. OF was responsible for federated learning and domain adaptation. XZ, ZH, and OF were responsible for deep reinforcement learning. CH was responsible for UQ of ML models. ZH was responsible for UQ of dynamic system models, optimization for sensor placement, and optimization for physical system modeling. YL was responsible for the optimization of additive manufacturing processes. XZ and ZH were responsible for real-time mission planning. AT and CH were responsible for the case study and predictive maintenance scheduling. CH was responsible for open-source software and data. SG was responsible for the industry demonstration. CH, MT, and SM were responsible for perspectives. All authors participated in manuscript writing, review, editing, and comment. All correspondence should be addressed to Chao Hu (e-mails: chao.hu@uconn.edu; huchaostu@gmail.com) and Zhen Hu (e-mail: zhennhu@umich.edu).

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Correspondence to Zhen Hu.

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The authors have no relevant financial or non-financial conflicts of interest to disclose.

Replication of results

The Python code and preprocessed dataset used for the battery case study are available for download on Githubhttps://github.com/acthelen/battery_digital_twin.

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Responsible Editor: Taejin Kim

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Topical Collection: Advanced Optimization Enabling Digital Twin Technology.

Guest Editors: C Hu, VA González, T Kim, O San, Z Hu, P Zheng.

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Thelen, A., Zhang, X., Fink, O. et al. A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives. Struct Multidisc Optim 66, 1 (2023). https://doi.org/10.1007/s00158-022-03410-x

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