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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 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 .

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

Smart manufacturing becomes a major trend of manufacturing industry in the context of Industry 4.0. The integration of physical manufacturing machines and digitized virtual counterparts is promoted by emerging concepts and technologies of Digital Twin. Aiming to seamlessly integrate the cyberspace and physical world by constructing a bi-directional mapping system, Digital Twin can highly improve the user experience and production efficiency in smart manufacturing. So far little attention has been paid to the mapping from the cyberspace to the physical world in Digital Twin. Without this mapping, operations on the digitized virtual machines are incapable of working on the physical ones, which actually limits the applicability of Digital Twin to more manufacturing processes. In addition, the traditional 2D interactive interface in the cyberspace is limited in visualizing the large amount of digital data and providing concise information to improve the operation efficiency. To optimize the conventional Digital Twin mapping system, this paper proposes a modular-based Digital Twin system for smart manufacturing, where the bi-directional real-time mapping of the cyber–physical space is established through socket communication. Moreover, the proposed Digital Twin system aggregates the functions of remote control and virtual machining using virtual reality and augmented reality. These two essential functions are designed to provide an immersive and friendly operating environment as well as a vivid preview of machining outcomes to improve production efficiency, minimize machining cost, and avoid potential risks. The feasibility and effectiveness of the proposed Digital Twin system are demonstrated by implementing the system on a CNC milling machine where the control latency and virtual machining accuracy are verified. The proposed Digital Twin system can be utilized as an essential part of smart manufacturing, having high potential to be applied to various industrial machines and smart systems.

Hydropower systems face significant challenges in load control and fault detection due to their complex operational dynamics. This study presents an innovative framework combining Digital Twin technology with Deep Learning to enhance fault detection, optimize operations, and improve system resilience. We developed a hybrid approach integrating a Digital Twin model of the hydropower system with advanced Deep Learning algorithms for real-time monitoring and predictive analysis. The proposed framework was evaluated through extensive simulations in a MATLAB environment, where it demonstrated remarkable improvements in system performance. The integration of Digital Twins allowed for precise real-time modeling of system behavior, while Deep Learning algorithms effectively identified and predicted faults. Our results show that the proposed method achieved a 12.14% reduction in fault detection time compared to traditional methods. Furthermore, the optimization of operational parameters led to a 8.97% increase in overall system efficiency and a 5.49% decrease in maintenance costs. In terms of fault detection accuracy, the Deep Learning-enhanced Digital Twin system achieved an 72% accuracy rate, significantly higher than the 65% accuracy observed with conventional techniques. The improved model not only enhanced fault detection but also contributed to a 8.03% reduction in energy loss and a 14.07% increase in power generation reliability. Overall, this research demonstrates that the integration of Digital Twins and Deep Learning provides a powerful, data-driven approach to optimizing hydropower systems. The proposed method offers substantial benefits in terms of operational efficiency, fault detection accuracy, and cost savings, positioning it as a significant advancement in the field.

The increasing complexity of hydropower systems necessitates advanced control strategies to optimize performance and enhance system reliability. This paper presents a novel approach that integrates Type-2 Fuzzy Logic Controllers (T2FLC), Digital Twin technology, and Neural Networks for comprehensive management of hydropower systems. The proposed hybrid system aims to improve load management, fault detection, and operational efficiency. Our method employs a Type-2 Fuzzy Logic Controller to handle uncertainty and imprecision in system control. The Digital Twin creates a dynamic simulation model of the hydropower system, allowing real-time monitoring and predictive analysis. Neural Networks further enhance this system by providing predictive insights based on historical and real-time data. Specifically, the hybrid system achieved a 10.96% increase in load management efficiency and a 12.64% reduction in fault detection time compared to traditional methods. The Digital Twin model contributed to a 18.21% improvement in predictive accuracy, while the Neural Networks enhanced control decisions, resulting in a 8.05% reduction in operational deviations. Furthermore, the proposed approach showed a 11.48% improvement in overall system reliability and a 13.04% reduction in maintenance costs, illustrating the practical benefits of integrating advanced control and predictive technologies. These results underscore the effectiveness of combining T2FLC, Digital Twin, and Neural Networks, offering a substantial advancement in hydropower system management. The proposed method not only enhances system performance but also provides a robust framework for future advancements in intelligent control strategies.

Digital Twin Technology (DTT) has gained significant attention as a vital technology for the efficient management of smart cities. However, its successful implementation in developing countries is often hindered by several barriers. Despite limited research available on smart city development in Malaysia, there is a need to investigate the possible challenges that could affect the effective implementation of DTT in the country. This study employs a mixed methodology research design, comprising an interview, a pilot survey, and the main survey. Firstly, we identified barriers reported in the literature and excluded insignificant factors through interviews. Next, we conducted an Exploratory Factor Analysis (EFA) on the pilot survey results to further refine the factors. Finally, we performed a Structural Equation Modeling (SEM) analysis on the main survey data to develop a model that identifies barriers to DTT implementation in smart city development in Malaysia. Our findings suggest the presence of 13 highly significant barriers, which are divided into four formative constructs. We found that personalization barriers are highly crucial, while operational barriers were less important for DTT implementation in smart city development in Malaysia.

Aging bridges require regular inspection due to performance deterioration. For this purpose, numerous researchers have considered the use of unmanned aerial vehicle (UAV) systems for structural health monitoring and inspection. However, present UAV-based inspection methods only represent the type and extent of external damage, but does not assess the seismic performance. In this study, a seismic fragility analysis of deteriorated bridges employing a UAV inspection-based updated digital twin is proposed. The proposed method consists of two phases: (1) bridge condition assessment using UAV inspection for updating the digital twin and (2) seismic fragility analysis based on the updated digital twin. To update the digital twin, the bridge damage grade is assigned based on the UAV inspection, and subsequently, the corresponding damage index is calculated. The damage index is utilized as a percentage reduction in the stiffness of finite element (FE) model, based on a previously proposed research. Using the updated digital twin, the seismic fragility analysis is conducted with different earthquake motions and magnitudes. To demonstrate the proposed method, an inservice pre-stressed concrete box bridge is examined. In particular, the seismic fragility curves of deteriorated bridges are compared with those of intact bridges. The numerical results show that the maximum failure probability of the deteriorated bridges is 3.6% higher than that of intact bridges. Therefore, the proposed method has the potential to updated the digital twin effectively using UAV inspection, allowing for seismic fragility analysis of deteriorated bridges to be conducted.

In the era of Industry 5.0, characterized by the seamless collaboration between humans and machines, the integration of digital twin technology (DTT) and blockchain technology (BCT) is poised to revolutionize supply chain management. This research explores the impact of DTT and BCT on achieving sustainable and efficient supply chain operations. Digital twins, virtual replicas of physical systems, enable real-time analysis and simulations, enhancing decision-making and operational efficiency. Simultaneously, blockchain technology ensures transparency and security in supply chains by maintaining unchangeable transaction records. This paper delves into the advantages and challenges presented by these technologies, examining real-world case studies across various industries. The study reveals that the combination of DTT and BCT creates a symbiotic relationship, driving continuous monitoring and validation of supply chain processes. This integration aligns with global sustainability goals, emphasizing resource optimization and waste reduction. However, data privacy, scalability, and interoperability remain significant barriers. A comprehensive approach is advocated to overcome these challenges, emphasizing ethical and environmental norms. Furthermore, this research offers insights into the mediating role of sustainable supply chain management (SSCM) practices and dynamic capabilities in the relationship between Industry 5.0 technologies and operational resource utilization (ORU) performance. It highlights the need for a sophisticated strategy in implementing technology adoption initiatives. This study contributes to theoretical advancements in Industry 5.0 by elucidating the complex interactions between DTT, BCT, and SSCM, paving the way for future research. Additionally, it provides valuable policy implications, guiding policymakers to prioritize innovation, transparency, and sustainability in the industrial sector. Integrating DTT and BCT can reshape supply chain dynamics, fostering a future marked by efficiency, innovation, and environmental responsibility.

In the absence of a high-fidelity physics-based prognostics model, data-driven prognostics methods are widely adopted. In practice, however, data-driven approaches often suffer from insufficient training data, which causes large training uncertainty that hinders the Digital twin (DT)-based decision-making. In such a case, the integration of low-fidelity physics with a data-driven method is highly demanded. This paper introduces physics-informed neural network (PINN)-based prognostics that can utilize low-fidelity physics information, such as monotonicity or the sign of curvature. Low-fidelity physics information is included as a constraint during the optimization process to reduce the training uncertainty in the neural network model by preventing unrealistic predictions. The proposed method is applied to two case studies to demonstrate the effect of reducing the prediction uncertainty and the robustness to the variability in test data. The two case studies show that PINN-based prognostics can successfully reduce the prediction uncertainty and yield more robust prognostics performance than the ordinary neural network.