Wagg, D.J. orcid.org/0000-0002-7266-2105 (2024) Modelling, reductionism and the implications for digital twins. In: Touzé, C. and Frangi, A., (eds.) Model Order Reduction for Design, Analysis and Control of Nonlinear Vibratory Systems. CISM International Centre for Mechanical Sciences, CISM 614 . Springer Cham , pp. 1-57. ISBN 9783031674983
Abstract
In this Chapter we will discuss modelling and reductionism in science and engineering, and how this relates to the new idea of digital twins. In particular, we focus on the historical context of modelling and reductionism for dynamics and control of engineering systems. Both active and passive control methods will be discussed, including the novel ideas associated with the inerter. Based on a selected review of the philosophy of modelling, we consider the role of knowledge and complexity in model making. The related topics of systems engineering, uncertainty analysis and artificial intelligence are also briefly discussed in the context of digital twins. We will argue that utility, trust and insight are the three key properties of models that will ideally be extended to digital twins. We then consider how digital twins will require the dynamic assembly of digital objects in order to recreate emergent behaviours. In order to implement a digital twin, an operational platform is required. We briefly present an aircraft example of a digital twin operational platform. Lastly we consider digital twin knowledge models and ontologies, and how this topic might help shape digital twins in the future.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author-produced version of a book chapter subsequently published in Model Order Reduction for Design, Analysis and Control of Nonlinear Vibratory Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Information and Computing Sciences; Engineering; Artificial Intelligence; Generic health relevance |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Mechanical, Aerospace and Civil Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Jan 2025 16:04 |
Last Modified: | 22 Jan 2025 16:04 |
Status: | Published |
Publisher: | Springer Cham |
Series Name: | CISM International Centre for Mechanical Sciences |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-67499-0_1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221783 |
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