Digital Twin: Reference Model, Realizations, and Recommendations. Issue 1 (12th April 2022)

• Record Type: Journal Article
• Authors: Pinon Fischer, Olivia J.; Matlik, John F.; Schindel, William D.; French, Mathew O.; Kabir, Mohammed H.; Ganguli, Jayendra S.; Hardwick, Martin; Arnold, Steven M.; Byar, Alan D.; Lewe, Jung‐Ho; Mahadevan, Sankara; Duncan, Scott; Dong, John J.; Kinard, Don A.; Maiaru, Marianna
• Is Part Of: Insight. Volume 25:Issue 1(2022)
• Page Start: 50
• Page End: 55
• Publication Date: 2022-04-12
• Subjects: digital twin -- digital engineering -- ASELCM -- generic reference model; Systems engineering -- Periodicals; Systems engineering; Periodicals; 620.0011
• DOI: 10.1002/inst.12373
• Languages: English
• ISSNs: 2156-485X

Abstract

ABSTRACT: Organizations continuously develop Digital Twins across a wide number of applications and industries. While this represents a testimony to the benefits and opportunities Digital Twins provide (AIAA 2022), their development, maintenance and evolution still face major challenges (Bordeleau et al. 2020). Most past and current efforts focusing on the development of Digital Twins have relied on ad‐hoc approaches, where most of the efforts start with building models without properly framing the problem (Martin 2019, Lu et al. 2020). More importantly it has led to the development of models that provide a solution to the wrong problem and consequently fail to address the core questions and needs of the stakeholders (Martin 2019)]. The development and implementation of Digital Twins also lack standardization (Shao 2021), instead relying on bespoke methods and technologies (Niederer et al. 2021), which in turns leads to a lack of consistency in their description and implementation, as well as limited interoperability across applications, tools and disciplines (Niederer et al. 2021, Piroumian 2021). The development of Digital Twins has been plagued by a lack of scalable approaches, leading to implementations that are highly specialized and require considerable resources in terms of subject matter expertise (Niederer et al. 2021). The development of standardized methodologies has been identified as a means to unleash the full potential of Digital Twins (Shao 2021, Piroumian 2021) and increase their adoption across a wider range of disciplines and applications (Niederer et al. 2021). To that end, this paper presents a domain agnostic and descriptive reference model, which builds on recognized industry practices and guidelines, to support the planning, description, and analysis of Digital Twins. It also introduces real‐world examples of aerospace Digital Twin use cases and discusses how the generic reference model supports the various use case applications. Finally, this paper briefly discusses recommendations and next steps on how to realize value from Digital Twins more broadly.