Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Abstract

A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

“…Some researchers studied predictive twins as data analytics format of digital twins to estimate the system's behavior. Soni et al [17] and Rasheed et al [113] categorized the implementation of digital twins into three classes: industrial, virtual, and predictive. Industrial twins consist of the use of Industrial IoT infrastructure [17].…”

“…Industrial twins consist of the use of Industrial IoT infrastructure [17]. Virtual twins comprise the digital representation of physical assets, where predictive twins are the data-driven models operating on the virtual twins to predict the behavior of the products or services [113]. Focusing on predictive capability, Xu and Duan [109] conducted a survey to highlight the employment of Cyber-Physical systems for big data analytics in Industry 4.0.…”

Quo Vadis Industry 4.0? Position, Trends, and Challenges

“…Moreover, the specification [23], development [49], and utilization of the AAS, as a concrete implementation of the Digital Twins within an Industry 4.0-compliant solution, is one essential challenge to be approached in the next couple of years [50]. The main challenges behind the digital twin technology include the interplay with Big Data [51], simulation [52], standardization [53], and also the security and privacy of the networks created for networking digitized data, virtual data, and information models [54].…”

“…The main problem with a mixed approach is the difficulty in the integration of the simulation models with AI techniques. Improving the modeling and simulation in digital twins is critical [54], and a possible way to do this is to integrate MAS applications to enhance the capabilities of generating different scenarios based on the interaction of multiple intelligent entities [56].…”

Breaking the Kolmogorov Barrier in Model Reduction of Fluid Flows

“…Reduced order modeling (ROM) has been a hot topic in response to the increasing complexity of engineering systems and the requirement of near real-time and multi-query responses along with the limited advancement in computational resources [1][2][3][4][5]. This is vital for informed decision-making in the context of digital twins [6][7][8][9][10][11][12][13][14], model-based control [15][16][17][18][19][20][21][22], data assimilation [23][24][25][26][27][28][29][30][31][32][33], parameter estimation [34][35][36][37], and uncertainty quantification [38][39][40][41][42][43][44]. In fluid systems, this has been feasible thanks to the existence of a few underlying structures (e.g., vortices) that dominate the flow dynamics as well as the tremendous availability of data, which enables us to identify those structures (also called modes or basis functions) in a data-driven fashion…”