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This study examines the connection between digital twin mechanisms and digital twin uses. We examined digital twins through three dimensions: navigation; interaction; and discovery. The effects were examined by considering two different uses: enabling use and controlling use. This study found a direct and positive relationship between the interaction mechanism and the enabling and controlling uses of digital twins. The results also indicated the nonexistence of any relationship between the navigation and discovery mechanisms, and digital twins' enabling and controlling uses. The results indicate that digital twin realism exerts a negative moderating effect on the relationship between digital twins' discovery mechanism and controlling use. Managers can leverage research findings as they adopt new digital tools to support their service businesses and management needs. For example, managers can leverage research findings by using digital technologies to inspire and motivate employees to fulfil organisational objectives. [ABSTRACT FROM AUTHOR]

This work focuses on the opportunity to use the Digital Twin of a complex system, as a Decision Support System. In studying the phenomenon of human Decision Making, the concept of Situation Awareness appears to be of primary importance when dealing with these complex systems. Given the complexity of the system to be represented in the DT, its own complexity, and the need to integrate the user's abilities to allow the acquisition of SA, the concept of reality anchor is proposed to identify the elements of the studied situation necessary for users to perceive, understand and project the situation they face. A methodology, called the Reality Anchor Methodology, has been defined to ensure the elicitation and implementation of these elements in a DT. This methodology is composed of three steps that aim (1) to elicit the reality anchors through a study of the operators' tasks and activities, (2) to design a prototype to carry out human-in-the-loop tests and (3) to validate the definition of Reality Anchors by analysing the SA, experience feedback and the activities performed during the tests. This method was applied to a case study in the oil-and-gas industry and showed the importance of the defined reality anchors. [ABSTRACT FROM AUTHOR]

As the adoption of digital twins increases steadily, it is necessary to determine how to operate them most effectively and efficiently. In this article, the digital twin synchronization problem is introduced and defined formally. Frequent synchronizations would increase cost and data traffic congestion, whereas infrequent synchronizations would increase the bias of the predictions and yield wrong decisions. This work defines the synchronization problem variants in different contexts. To discuss the problem and its solution, the problem of determining when to synchronize an unreliable production system with its digital twin to minimize the average synchronization and bias costs is formulated and analyzed analytically. The state-independent, state-dependent, and full-information solutions have been determined by using a stochastic model of the system. Solving the synchronization problem using simulation is discussed, and an approximate policy is proposed. Our results show that the performance of the state-dependent policy is close to the optimal solution that can be obtained with full information and significantly better than the performance of the state-independent policy. Furthermore, the approximate periodic state-dependent policy yields near-optimal results. To operate digital twins more effectively, the digital twin synchronization problem must be considered and solved to determine the optimal synchronization policy. [ABSTRACT FROM AUTHOR]

This paper considers what we know about the potential for disinformation in hydrological data when used for the evaluation of hydrological models. This will generally arise from epistemic uncertainties associated with hydrological observations, particularly from nonstationary or extrapolated rating curves for discharges, and poor rainfall and snowmelt information when interpolated over basin areas. Approaches based on information theory are not well suited to consideration of such epistemic uncertainties in model evaluation and an alternative approach based on setting limits of acceptability independent of any model runs is suggested. This allows for both the rejection of all models tried, and for acceptability of models across different model structures and parameter sets. The paper concludes with some suggestions for future research on defining disinformative data for both point and spatial observables, studying model failures, and defining new observations with a view to having the greatest impact on reducing model uncertainties. [ABSTRACT FROM AUTHOR]

The research introduces an innovative method for creating a digital twin (DT) of heat energy storage and retrieval system (HESRS) for real-time monitoring and performance analysis. The HESRS, type of HVAC system, is evaluated based on parameters like stored heat energy, heat extraction via heat transfer fluid (HTF), and Phase Change Material (PCM) temperatures. Data from temperature sensors is sent to the cloud in real-time. A reduced-order model (ROM) analyses it on the cloud and sends results to an Android app. The DT is then simulated in augmented reality through our app, Twin-X, marking distinctive approach to digital twin development. [ABSTRACT FROM AUTHOR]

In April 2021 Stavå bridge, a main bridge on E6 in Norway, was abruptly closed for traffic. A structural defect had seriously compromised the bridge's structural integrity. The Norwegian Public Roads Administration (NPRA) closed it, made a temporary solution and reopened with severe traffic restrictions. The incident was alerted through what constitutes the bridge's Digital Twin processing data from Internet of Things sensors. The solution was crucial in online and offline diagnostics, the case demonstrating the value of technologies to tackle emerging dangerous situations as well as acting preventively. A critical and rapidly developing damage was detected in time to stop the development, but not in time to avoid the incident altogether. The paper puts risk in a broader perspective for an organization responsible for highway infrastructure. It positions online monitoring and Digital Twins in the context of Risk- and Condition-Based Maintenance. The situation that arose at Stavå bridge, and how it was detected, analysed, and diagnosed during virtual inspection, is described. The case demonstrates how combining physics-based methods with Machine Learning can facilitate damage detection and diagnostics. A summary of lessons learnt, both from technical and organisational perspectives, as well as plans of future work, is presented. [ABSTRACT FROM AUTHOR]

Europe's transportation infrastructure including bridges requires optimised maintenance plans and adequate monitoring throughout its life cycle to ensure safety and serviceability. The deployment of new technologies can help this goal, providing numerous advantages compared to existing and established inspection and monitoring methods. This deployment can be facilitated by adequate policies and focused research and innovation, taking also into account the European ambitions for safe, secure, and future-proof transportation infrastructure. Considering the above, the objective of this study is threefold. First, it provides an overview of recent policy initiatives in Europe that facilitate the adoption of new and emerging technologies in bridge inspection and monitoring, as well as maintenance in general. Second, it identifies and reviews R&I projects relevant to bridges, including the budgetary expenditure, from the Horizon 2020 European Research Framework Programme which was recently concluded. Finally, it systematically reviews technologies and solutions developed by the identified projects, assessing also their technology readiness. What emerges is substantial research, which includes solutions and technologies (e.g., use of air or ground drones, artificial intelligence, digital twins) that have the potential to reshape the bridge inspection and monitoring state of play in Europe and beyond. [ABSTRACT FROM AUTHOR]

This article presents a case study with various developments of digital twinning of a sample of load tests performed on several railways bridges. The case study is located in Extremadura, South Western Spain and its aim is the generation of a validated, multi-layered information construct in the form of a digital twin as the result of a load test. This result is conceived, not only to verify the assumptions of the design of the bridge but also, to optimize future maintenance plans of the network. This particular case study is framed within a vaster European effort on digitization of the construction sector. Research and Innovation Actions within this demo case are aimed at integrating routine requirements and procedures of load tests with cutting edge digital technologies for the generation of validated virtual replica of these physical bridges. The generated twins during these load tests behaviourally match the obtained response during loading and as such, represent an ideal model for future simulations and behavioural predictions. Different data-gathering techniques and numerical models are integrated within a Common Data Environment (CDE). All efforts related to measurement, simulation, 3D modelling, assessment and validation can be wrapped up systematically for further use during regular operation of the asset. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) are transforming business operations across industries through accurate replication of physical entities using the Internet of Things and big data analytics. Despite booming progress in the manufacturing, aerospace and buildings sectors, the adoption of DTs in the minerals industry has been slow, and integration with efficient visualisation and user interactions has not been fully optimised to achieve maximum fidelity and usability. One promising avenue for enhancing DT capabilities is the utilisation of extended reality (XR) technologies, which also hold great potential for realising an industrial metaverse where real-world business activities can be conducted in a virtual space. This article proposes a cost-effective and scalable approach to developing a DT with real-time monitoring and control capabilities for a ball mill operation, a widely used processing equipment in the minerals industry. The case study showcases two approaches with different levels of system integration by leveraging serious game development platforms, toolkits and workflows. [ABSTRACT FROM AUTHOR]

With the spreading of Industry 4.0, cyber-physical systems, and tools for augmented and virtual reality, Digital Twin (DT) is gaining momentum in several areas of Computer Science and Engineering. This paper presents a systematic literature review that investigates the way DTs are described in Human-Computer Interaction (HCI) scientific literature. The study includes 23 papers selected through a systematic search on the 21 most ranked journals and conferences in the HCI area. As a result of this work, it appears clear that the way humans interact with DTs is a topic still far from being widely studied. A set of hints for future research about the relationship between HCI and DT constitute the main outcome of this paper. [ABSTRACT FROM AUTHOR]

This article systematically summarises the digital twin technology in the existing scientific field using the bibliometric method, puts forward the critical areas and research directions at different stages of the life cycle of the digital twin research topic. Using R-Studio 'Biblioshiny' to measure scientifically, we visually analyze 514 articles related to digital twin in the Web of Science's core set from 2014 to 2021 (including SCI-Expanded and SSCI database). Firstly, we analyzed the core journals, articles, authors, institutions, relevant countries to determine the vital influence of digital twin-related literature. Secondly, we use theme maps to draw the research topics of digital twin-related literature as icons on the conceptual structure and offer four cluster parts (Motor themes, highly developed and isolated themes, Emerging themes, and Basic themes). Finally, we concluded by summarising the limitations of our study and subsequently highlighted future research directions. The study shows that monitoring, maintenance, and computer vision are research topics with good development momentum. Disturbance observer, reinforcement learning, and friction are relatively small research topics with a good development foundation, and scheduling, process modelling, and deep reinforcement learning are emerging research directions. [ABSTRACT FROM AUTHOR]

Rail surface damage occurs due to repeated overstressing of the surface by wheel-rail contact cycles, which is also known as rolling contact fatigue, and causes regular costly maintenance expenses to railways. The preventive strategies to reduce rail damage can be conservative if it is not based on measured data. A validated digital model of the physical system can provide data for maintenance decision. Interaction between a physical system and a digital model has evolved at the beginning of this century into a new process commonly referred to by the term 'Digital Twin'. There is a lack of a Digital Twin framework in decision-making on railway maintenance. The term Digital Twin is often confused with advanced simulation methods where the communication of data is only one way, i.e. sensor or design data of the physical system input to a model. In this paper, a Digital Twin has been developed to predict rolling contact fatigue rail surface damage, corresponding to traffic passing along a railway network. Differences between the Digital Twin and advanced simulation techniques have been presented. Finally, a predictive assessment tool has been proposed to provide prediction from the Digital Twin to the physical twin, and results are discussed. Abbreviations: AS: Advanced Simulations; DS: Digital Shadow; DT: Digital Twin; HPC: High Performance Computing; LTS: Longitudinal Train Simulation; MBS: Multibody Simulation; MGT: Million Gross Tonnes; NASA: National Aeronautics and Space Administration; RCF: Rolling Contact Fatigue [ABSTRACT FROM AUTHOR]

The information that automated train control (ATO) systems use to improve safety and reduce power usage is limited by on-board and wayside monitoring applications and computing power. This paper presents an augmented digital twin for railway applications that enables real-time consideration of derailment risk in train operations. The augmented digital twin implements a surrogate model with the results of a massive multibody dynamics numerical program and machine learning models to predict the instantaneous wagon derailment risk. A case study for a heavy haul iron ore wagon with three-piece bogies was conducted to test the augmented digital twin. A multibody simulation numerical program comprising 2100 simulation cases was completed. The surrogate model was developed using linear, polynomial, decision tree and ensemble forest regression models on the results of the numerical program. A longitudinal train simulator was used to calculate the speed and lateral coupler force throughout a train trip. The surrogate model effectively predicted the derailment index for empty and loaded conditions accounting for lateral coupler forces, vehicle speeds and curve radius. The proposed augmented digital twin can be further developed to accomplish other train operational benefits such as the reduction of rail damage. [ABSTRACT FROM AUTHOR]

The paper shows the feasibility of building closed-form and fast-to-evaluate surrogate models via supervised kernel-based machine learning regressions behaving as digital twins for computationally expensive multibody simulations. The aforementioned surrogate models are adopted to predict the railway vehicle dynamics safety indexes defined in the international standards, depending on the wheel-rail forces, directly from the results of longitudinal train dynamics simulations. The digital twin models are trained with the outputs of Simpack multibody simulations of a reference freight wagon, to which the in-train forces calculated by an in-house MATLAB longitudinal train dynamics simulator are applied. Two machine learning techniques are considered: the support vector machine and the least-squares support vector machine regressions. Both techniques ensure a good accuracy even with a limited number of training samples. The derivation of the surrogate models can strongly enhance the modelling capabilities of common longitudinal train dynamics simulators, that cannot evaluate the wheel-rail contact forces. At the same time, the method shown in the paper allows to strongly reduce the total computational times, as the evaluation of the closed-form surrogate models allows to effectively estimate the safety indexes with no need for computationally demanding multibody simulations. [ABSTRACT FROM AUTHOR]

A successful predictive maintenance strategy for wheels and rails depends on an accurate and robust modelling of damage evolution, mainly uniform wear and Rolling Contact Fatigue (RCF). In this work a life prediction framework for wheels and rails is presented. The prediction model accounts for wear, RCF, and their interaction based on the output from MBS simulations to calculate the remaining life of the asset, given in mileage for wheels and MGTs for rails. Once the model is calibrated, the proposed methodology can predict the sensitivity of the maintenance intervals against changes in operational conditions, such as changes in contact lubrication, track gauge, operating speeds, etc. The prediction framework is then used in two operational cases on the Swedish Iron-Ore line. The studied cases are, the analysis of wheel life for the locomotives, and the analysis of rail life for gauge widening scenarios. The results demonstrate the capabilities of the MBS-based damage modelling for predictive maintenance purposes and showcase how these techniques can set the path towards Digital Twins of railway assets. [ABSTRACT FROM AUTHOR]

The rapid adoption of Cloud, Fog, and Edge computing paradigms, along with Digital Twin (DT) technology, is being observed across numerous domains. Smart Agriculture is one such domain, where diverse requirements exist for applications that range from Augmented Reality, Satellite, and auto harvesters to a multitude of agricultural devices and sensors. While the integration of Cloud, Fog, and Edge computing can address various challenges, it can also pose some difficulties, including resource allocation, resource scheduling, and task scheduling. To address these challenges and enhance the productivity and sustainability of Smart Agriculture, this paper presents a novel architecture based on Cloud, Fog, Edge computing, and Multi-Agent Systems (MAS), incorporating the concept of Digital Twin. This proposed architecture is implemented in Smart Agricultural farms and fields, opening up new contemporary applications in the Agriculture domain. [ABSTRACT FROM AUTHOR]

One typical approach to implement model predictive control in relatively fast sample rate applications utilises linearised plant models to simplify the online optimisation problem in the presence of limited computation budget. A number of linearisation points may be used to account for the variation in the non-linear plant over the entire operating domain, leading to potential switched linear time-invariant (LTI) or linear time-variant (LTV) implementations. The choice of these linearisation points, and the regions over which they are considered valid, can have a significant impact on the closed loop performance of the overall controller. In this work, in the presence of a high-fidelity digital twin of a non-linear plant, a numerical algorithm for the selection of linearisation points is proposed. The approach makes use of the concept of disturbance sets to determine both the linearisation points and the domain to which each linearisation point applies. The use of disturbance sets provides an implicit connection to closed-loop controller performance if robust model predictive control algorithms are implemented. The proposed approach is implemented for the challenging problem of diesel air-path control, with simulation results on a high-fidelity mean value engine model used to demonstrate the efficacy of the approach. [ABSTRACT FROM AUTHOR]

The similarities between additive manufacturing (AM) and fusion welding have made Science and Technology of Welding and Joining an appropriate venue to discuss the recent progress made in AM. This special issue highlights significant recent progress in AM towards inventing new processes, developing novel materials, improving microstructure, achieving unique mechanical properties, and reducing defects. Furthermore, it identifies the modern tools of the digital age such as mechanistic modelling, machine learning, and digital twin as powerful drivers of this progress. The articles in this special issue are also believed to identify research needs and opportunities for the AM community. [ABSTRACT FROM AUTHOR]

Digital Twins (DT) have become popular in various industries to solve complex problems. A true DT allows for bidirectional real-time data flows between the physical and virtual worlds. The term DT was often overused, leading to confusion about its capabilities therefore this paper provides a review of the current state of DT and its applications in the minerals industry. Existing literature shows a slow adoption of DTs in the minerals industry, which is largely due to challenges associated with geological unpredictability, legacy system integration, cost, skill gaps, cybersecurity, and lack of standards. Combining immersive visualisation with real-time spatial computer graphics could improve the usability and acceptance of DTs in the minerals industry which has been used successfully in training while underexplored for operational scenarios. The study concludes by highlighting the need for further research to understand the effectiveness of such an approach in mineral operations. [ABSTRACT FROM AUTHOR]

A special case study of a rail transport consist comprised of 11 wagons on three-piece bogies was carried out using advanced multi-body vehicle simulations to evaluate if the safety and stability of the wagons would be compromised by loading 39 continuous welded rails on the wagon decks. Each rail was 139 m long and laterally fastened to the wagon decks. The wagon model components, including a car body, two bolsters, 4 sideframes and 4 wheelsets, were connected by non-linear spring and damping elements. A non-linear coupler was used to connect the adjacent wagon models. A twist simulation for a single wagon model was conducted and the results were compared with experimental data for validation. The lateral stability analysis found that the lowest critical hunting speed was 90 km/h for the empty wagon model, which satisfies the current operational speed range from 0 to 70 km/h. Safety simulations were conducted on the curved track geometry modelled from the actual track alignment and included FRA Class 4 track irregularities. The safety simulation results indicated that the 11-wagon consist performed within the maximum safety criteria limits as stipulated in the AS7509:2017 standard. [ABSTRACT FROM AUTHOR]

This paper presents a Digital Twin-driven framework for fatigue lifecycle management of steel bridges. A probabilistic multi-scale fatigue deterioration model is proposed to predict the entire fatigue process of steel bridges. Bayesian inference of the deterioration parameters realizes the real-time updating of the predicted lifecycle fatigue evolution process, which provides a good basis for lifecycle optimization. To avoid an empirically predefined repair crack size for maintenance, an optimization process for maintenance strategies is included. The relationship of the extended lifetime and the design repair crack size is constructed by numerical experimental design and surrogate modeling. The solution for optimum repair crack size is obtained while maximizing the extended fatigue life and minimizing the maintenance costs. Based on the occurrence time distribution of the optimum repair crack size, the inspection/monitoring planning is determined from a probabilistic optimization process based on the minimization of the expected damage detection delay and the lifecycle costs. The uncertainties associated with the damage occurrence and detection ability are considered during the formulation of the expected damage detection delay by decision tree analysis. Based on Digital Twin concept, the predicted deterioration process, derived maintenance, and inspection/monitoring planning are timely updated until a defined stopping rule is met. [ABSTRACT FROM AUTHOR]

Abstract The use of physics-based models for the development and optimization of energy systems is popular due to their versatility. However, their inherent complexity often makes these simulations computationally prohibitive, despite the current computational power available nowadays. This problem is aggravated when considering renewable energy systems, which are subjected to intermittent weather conditions and must be operated in connection with a grid of variable demand. In that sense, the use of metamodels has shown immense potential in the simulation, analysis, and optimization of energy systems. Therefore, this review presents a comprehensive analysis of the extensive application of metamodeling techniques in energy systems. After conducting a careful search using the Scopus database while targeting a well-defined group of keywords, 474 articles published from 2013 to 2023 were obtained in the present literature review. With a rigorous filtering and screening process, the first pool of articles was reduced to 126. The resulting articles were then separated in two main groups, reviews and research articles. Each of these groups was further organized in terms of the energy system studied, as well as the applicability, machine learning technique and error indicator. The results indicate a steady and significant growth in the number of publications associated with energy systems and different types of metamodeling techniques. The reasons are arguably twofold, reduction of computational time provided by the metamodels and their good accuracy. Nevertheless, and as reported by other studies, the review also identified the need to develop a standardized method for evaluating the error analysis of these models and a comparison between their actual efficiency, which includes accuracy and time. Furthermore, no clear relation was found between specific types of metamodels and specific energy systems. [ABSTRACT FROM AUTHOR]

An uncertain mathematical model is developed to reproduce the human-body responses to a simulated flight-induced vibration. The objective is to create a predictive model based on the anthropometric characteristics of a seated human body that can turn useful for human-centered design considerations and for the integration of liveware into digital twin models. To this end, the human body is seen as an advanced vibrating structure represented as a dynamic stochastic 4DOF model with random anthropometric values. Experimental and numerical tests are given and results are evaluated in the time and frequency domain. [ABSTRACT FROM AUTHOR]

This review article provides a critical assessment of the progress made in computational modelling of metal-based additive manufacturing (AM) with emphasis on its ability to predict physical phenomena, concepts of microstructural evolution, residual stresses, role of multiple thermal cycles, and formation of multi-dimensional defects along with the achieved degree of experimental validation. The uniqueness of this article stems from the inclusion of comprehensive information on computational progress in the field of fusion-based, sintering-based, and mechanical deformation-based AM. A computational model's role in determining the process framework for the desired outcome of the set properties of the AM components is recognised while presenting the process-microstructure maps, thereby appraising computational ability towards the qualification of products. The inclusion of a detailed discussion on the bi-directional coupling of machine learning and physics-based computational models provides a futuristic roadmap for the digital twin of metal-based AM. [ABSTRACT FROM AUTHOR]

A newly-developed online laboratory experiment in immersive virtual reality (VR) is used for process engineering students. A digital twin (DT) of a jet pump was realised using the game engine Unreal Engine 4. The chance was taken for an instructional redesign deviating from the conventional cookbook-experiment to better integrate the changing competence demands of the working world 4.0 (W4.0). A real-world scenario (RWS) is used to address the sometimes-vague problem-solving assignments of W4.0: the students found themselves in an ambiguous situation which must be cleared and resolved constructively and autonomously by them. The RWS was evaluated by triangulating qualitative and quantitative methods. Firstly, its Constructive Alignment was evaluated, i.e. whether the intended learning outcomes to be achieved through the novel teaching and learning activities were reflected in the assessment tasks, too. Secondly, students were surveyed regarding their attitudes towards such new forms of laboratory learning. The results show that even if the students initially seem overwhelmed by the unfamiliar type of assignment, they are nevertheless able to solve the work-related engineering problem – at least with varying degrees of assistance from a fictional immediate superior. The students appreciate the opportunity to think creatively and independently develop solutions to the assignment. [ABSTRACT FROM AUTHOR]

Abstract This article presents a case study with various developments of digital twinning of a sample of load tests performed on several railways bridges. The case study is located in Extremadura, South Western Spain and its aim is the generation of a validated, multi-layered information construct in the form of a digital twin as the result of a load test. This result is conceived, not only to verify the assumptions of the design of the bridge but also, to optimize future maintenance plans of the network. This particular case study is framed within a vaster European effort on digitization of the construction sector. Research and Innovation Actions within this demo case are aimed at integrating routine requirements and procedures of load tests with cutting edge digital technologies for the generation of validated virtual replica of these physical bridges. The generated twins during these load tests behaviourally match the obtained response during loading and as such, represent an ideal model for future simulations and behavioural predictions. Different data-gathering techniques and numerical models are integrated within a Common Data Environment (CDE). All efforts related to measurement, simulation, 3D modelling, assessment and validation can be wrapped up systematically for further use during regular operation of the asset. [ABSTRACT FROM AUTHOR]

Abstract The Industry 5.0 initiative seeks the sustainability and resilience of production systems through digital technologies. Derived from such an initiative, the Operator 5.0 concept surged to place the operator as the main value-creation contributor of sustainable and resilient human-machine systems. Smart service systems are data-driven services that provide intelligent capabilities to support decision-making in business processes. Despite previous research on smart services driven by multiple technologies in different contexts, further studies approaching Digital Twins as enablers of smart services in machine use phases are yet to be explored. Digital Twin, an Industry 5.0 enabler, is a technology that virtually represents physical assets that collect and analyze data from actual operations to make predictions for decision-making. Simulation-based Digital Twins generate simulation models that are continuously upgraded with asset data in real time, improving the accuracy of predictions. This paper aims to investigate how the Simulation-based Digital Twins (SDT) can enable the development of smart services in the Operator 5.0 context. We build on a multiple case study of 13 interviews from nine heavy mobile machinery manufacturers. We capture our results in categorizing SDT-enabled smart services for the use phases of the machine lifecycle. The categorization identifies the capabilities of each smart service for decision-making support and value co-creation. [ABSTRACT FROM AUTHOR]

Futuristic views of mine operations allude to the use of immersive virtual worlds that transport people into what is now called a Metaverse, a network of 3D virtual worlds that can be experienced by an unlimited number of people simultaneously. The concept and benefit of combining real and virtual worlds have been around for many decades and have evolved considerably in recent years. When these are presented as a digital twin with bidirectional data and collaboration capability, potential exists for a powerful communications tool for mining operations. Hence, it is time to discuss the Metaverse from a mining perspective and this paper asks, 'What is the Metaverse and how does it relate to mining?' This paper presents some case studies, concepts, a model and considers the benefits of a Mining Metaverse as a communications tool that enhances decision making via remote collaboration. [ABSTRACT FROM AUTHOR]

Lime is an important flux used in the iron and steel-making processes. Tata Steel Jamshedpur uses a twin-shaft Parallel Flow Regenerative type kiln to calcine limestone. Previously, the kiln was operated based on the temperature measurement at only three locations. This led to suboptimal kiln performance and difficulty in maintaining lime quality. A thermal digital twin was developed using first principles to address these issues. A set of unsteady-state partial-differential equations representing the thermophysical phenomena is solved numerically using the finite volume method. The model was validated with the measured flue gas temperature and lime temperatures. The model is implemented online with the real-time operating parameters to estimate the temperature profile of solid and gas and the degree of conversion of limestone. This real-time visibility enables the operators to optimize the process and achieve lime conversion up to 94%, resulting in energy savings of 31.8 GJ/day and stable lime quality. [ABSTRACT FROM AUTHOR]

Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, "data first" approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data. [ABSTRACT FROM AUTHOR]

The electric power system is undergoing a significant transformation driven by advances in digital technologies. This article provides a comprehensive and detailed analysis of recent advances and the future outlook of electric power systems, with a particular emphasis on the impacts of digital transformation. By conducting a thorough examination of over 150 up-to-date research papers, the study highlights the key developments shaping the future of the field, such as the integration of renewable energy, distributed generation, microgrids, and smart grids. The analysis includes comparison tables and SWOT analysis for each technology, offering insights into their strengths, weaknesses, opportunities, and threats. The study also provides a historical and conceptual overview of digital transformation in electric power systems, exploring recent digital technologies such as cloud computing, the Internet of Things, blockchain, digital twins, and their impact on the electric power system. The article concludes by addressing the barriers and challenges in this transformation. Overall, this research offers a comprehensive and up-to-date analysis of the current state and future outlook of electric power systems, providing valuable insights for researchers, practitioners, and policymakers. [ABSTRACT FROM AUTHOR]

The air-drying of resin impregnated paper sheets in industrial lines, formed by a serial array of furnaces, presents a high number of different controllable operational parameters whose adjustment, usually done by the maintenance staff, leads to non-efficient configurations. A model-based numerical tool, which predicts accurately in a few seconds the evolution of the paper temperature and paper grammage along the line for a given combination of the input operational parameters (direct design), was used coupled to an optimization tool to select appropriate operational parameters (inverse design) that ensure a drying process quality (i.e., fulfills an objective grammage profile) with a minimum of energy consumption. The numerical tool was capable of selecting suitable configurations with an energy reduction of up to 50% for several tested industrial cases, making the model an essential tool in the framework of the increasingly relevant role of digital twins in industry. [ABSTRACT FROM AUTHOR]

A digital twin is aimed to be a virtual model designed to accurately replicate a physical system in the digital world. This is why digital twins have become quite popular in recent years in both rail and road domain applications, and some researchers and engineers have started considering the term as a buzz word that attracts increased interest in their research. In some cases, this leads to misunderstandings regarding the digital twin concept, especially if the discussion relates to digital twins built on physics-based models. This paper's detailed review has been performed to address this issue considering the application of vehicle system dynamics theories as a basis for development of digital twins and the implementation process in digital twin studies. The results show that most works are focusing on digital twin developments at the 'top level of the pyramid' and more comprehensive research and investigation studies are needed in terms of the application of vehicle system dynamics in design and construction of digital twins. This paper adopts and summarises the possible directions for the development of a future framework in terms of disciplines relevant to vehicle system dynamics and presents a forecasting roadmap on future development strategies of digital twins. [ABSTRACT FROM AUTHOR]

Comprehensive evaluation of dams in a dynamic and proactive way is accepted as an effective strategy to improve dam safety. However, there is a lack of efficient and standardised approaches for managing monitoring-related information that can help to provide dynamic and reliable information for continuous dam evaluation. With the importance of digital twins (DTs) being proven in better data integration and interoperability, a DT-based approach for comprehensive dam evaluation and its data integration method based on extended industry foundation classes (IFC) are provided in this study. This paper presents a new data structure and corresponding data classification strategy, using which information required for continuous evaluation can be effectively organised. Considering that existing evaluation methods cannot fully characterise uncertainties in the evaluation process, a cloud model based comprehensive evaluation framework is proposed. In this approach, a multi-rule cloud reasoning model is utilised to evaluate monitoring points. Also, a multi-dimensional cloud model and an improved Criteria Importance Though Intercriteria Correlation (CRITIC) method are leveraged to assess evaluation indices affected by multiple factors. Finally, the results of a case study verify that the proposed DT-based solution realises a continuous dam safety evaluation, which contributes to automated and efficient dam condition monitoring. [ABSTRACT FROM AUTHOR]

Abstract Based on the differential idea, this article establishes two segmented algorithms which are the linear equations method and iterative method for solving the one-dimensional digital twin of the temperature field and pressure field of plate heat exchanger (PHE). The segmented algorithms are to consider the change of the physical properties of the fluid along the flow direction, which makes the solution of the temperature field and pressure field more accurate than that without segmentation. It can still get other important parameters besides temperature and is easy to converge with a few iterative steps. When the boundary conditions are known, it can accurately solve the heat transfer, wall temperature, convective heat transfer coefficient, temperature field and pressure field of fluid. Under six groups of test conditions, the heat exchange, temperature and pressure calculated by the two algorithms are compared with the commercial software Flownex, when the number of segments is 100, the average maximum error of each segment is not more than −0.39%, −0.97%, and 0.18%, respectively. To balance accuracy and calculation speed, this article also explores the impact of the number of segments on accuracy. It is found that when the number of segments increases to 10, the impact on the calculation accuracy can be ignored. Compared with Flownex, these two algorithms have smaller errors, and the solution speed of solving linear equations method is faster than that of iteration. It provides application value for emerging digital twin technology, data center chilled water system and other projects and industries that need to accurately control the outlet temperature and wall temperature of PHE. [ABSTRACT FROM AUTHOR]

Abstract The need for measurable data from physical assets to actively feed a living Digital Twin (DT) is paramount. The requirements and needs that the gathered data should fulfill in order to be practically implemented in the stream data pipeline are heterogeneous, some of them general and other case-specific. This article summarizes a set of identified challenges and requirements for a seamless infusion of well-established Structural Health Monitoring (SHM) systems within DT platforms, without the objective of solve all of them. This identification is performed based on a review of traditional SHM systems with a vast array of information sources as well as on the review of techniques for the systematic digitalization of existing assets. On the other hand, ten real demo cases belonging to Ashvin, an H2020 Research and Innovation project, are providing real world testing beds for an active development of SHM infused in DT systems. Multiple information sources are studied in those sites, which also enriches with more realism, the identification of requirements and challenges presented herein. These assets provide a perspective to researchers about practical implications of these needs. [ABSTRACT FROM AUTHOR]

In this work, the authors present a detailed train-track interaction model of a long freight train operation to predict long-term rail surface damage. In addition to vehicles and track, intermediate maintenance actions in the form of cyclic grinding passes have also been modelled according to European standards to realistically represent the evolving wheel-rail interface. The influence of longitudinal train dynamics in the form of inter-vehicle interactions, traction, braking, gradients, etc is also included in this method to reflect their effect on damage evolution. The authors demonstrate that the novel 'Train-track interaction' formulation is more complete and therefore better suited to study long-term rail surface damage as opposed to existing 'vehicle-track' formulations since the former brings the system dynamics at play, significantly altering the wheel-rail interaction. A key highlight of this work is that the rail surface damage is expressed in the form of evolving rail profiles over a large tonnage passing and by depicting RCF-affected zones. This framework can be tuned into a digital twin to guide infrastructure managers regarding the condition of rail surface as a function of tonnage passage. This can in turn facilitate predictive maintenance of track depending on traffic and operation. [ABSTRACT FROM AUTHOR]

This review paper was written on the occasion of the 40th anniversary of the Drying Technology Journal. Interestingly, most of the progress made in the domain of drying modeling and simulation has occurred over the last 40 years. Indeed, 40 years ago, the comprehensive formulation of coupled heat and mass transfer in porous media was available; however, its potential use for computational simulation was only in its nascent stage. This is therefore an ideal time span in which to take a full historical context, to present the state-of-the-art and to give insights into its future trajectory. All aspects are deliberately presented in a pedagogical manner with many illustrations to make the text accessible to young scientists. However, for the benefit of experts, detailed formulations are provided in the annexes. The main points addressed in this work include: The description of the comprehensive macroscopic formulation, An historical perspective of the progress made in computational simulation, The contribution of modeling in parameter characterization, The mechanical aspects of drying: strain and stress induced by drying, including the case of large deformations, The impact of non-local equilibrium, The promising domain of the digital twin in parameter tuning and control/command. [ABSTRACT FROM AUTHOR]

3D city models are omnipresent in urban management and simulations. However, instruments for their evaluation have been limited. Furthermore, current instances are scattered worldwide and developed independently, hampering their comparison and understanding practices. While there are developed assessment frameworks in open data, such efforts are generic and not applied to geospatial data. We establish a holistic and comprehensive four-category framework '3D City Index', encompassing 47 criteria to identify key properties of 3D city models, enabling their assessment and benchmarking, and suggesting usability. We evaluate 40 authoritative 3D city models and derive quantitative and qualitative insights. The framework implementation enables a comprehensive and structured understanding of the landscape of semantic 3D geospatial data, as well as doubles as an evaluated collection of open 3D city models. For example, datasets differ substantially in their characteristics, having heterogeneous properties influenced by their different purposes. There are further applications of this first endeavour to standardise the characterisation of 3D data: monitoring developments and trends in 3D city modelling, and enabling researchers and practitioners to find the most appropriate datasets for their needs. The work is designed to measure datasets continuously and can also be applied to other instances in spatial data infrastructures. [ABSTRACT FROM AUTHOR]

The sorting operation of the production line in a heavy industrial scenario has the double complexity of the problem and the data. To improve production efficiency, the operation needs to be optimized. Aimed at this problem, this article designs a data representation method and an evolutionary job scheduling algorithm with an optimized population by deep reinforcement learning (DRL). Moreover, a real industrial dataset is contributed. The representation method represents the job data by referring to the bag-of-words model. The evolutionary algorithm uses DRL to initialize the genetic algorithm (GA)'s population and further evolves the population through the GA to obtain the final scheduling result. The experimental results indicate that the evolutionary algorithm has achieved the largest decrease in the average times for frame clearing on the real and simulated validation datasets, which are 12.54 % and 11.43 % , respectively. It is of great significance for subsequent scheduling of the full-scenario digital twin. [ABSTRACT FROM AUTHOR]

With increased urbanization and the impacts of climate change, cities around the world are making resilience-building a priority. Simultaneously, advances in technology have enabled the creation of City Digital Twins (CDTs). Informed by a literature review and interviews with resilience and Digital Twin experts, this paper explores how CDTs might support the development of more resilient urban communities. First, the various definitions of urban resilience, smart cities and CDTs are described. Second, the paper explores how characteristics of CDTs make them uniquely equipped to facilitate (1) a better understanding of complex phenomena, (2) the imagination of possible futures and (3) collaboration between stakeholders. Finally, the technical requirements and challenges of CDT implementation are discussed, including (1) identifying priority hazards and users, (2) collecting and managing data, (3) integrating different models and (4) ensuring usability. The paper concludes by emphasizing the important role of stakeholders in shaping CDTs that can be successfully integrated by the communities they serve. [ABSTRACT FROM AUTHOR]

The concept of Digital Twin was invented not only for a static virtual mimic of reality, but also means an evolving digital copy which could stay consistent with its physical counterpart. The life-cycle management of a product or a system could not be completed without dynamic feature, while conventional methods to create graphic assets especially 3D models for Digital Twins are still limited. In this paper, Procedural Content Generation is introduced as a potential technique which could reduce difficulty for the creation and modification of 3D graphic assets of Digital Twins. A trial project and two validation cases are executed to verify the proposed method. [ABSTRACT FROM AUTHOR]

There is significant interest in emerging technologies. Universities, companies and other adopters are researching how and when these technologies should be implemented. However, this research area still lacks ways to evaluate the maturity level of the technologies and rapid experimentation concepts for testing the new technologies. This paper addresses the research gap by providing a new evaluation method and a rapid experimentation concept. This method and concept were successfully used in twenty-five Flash-projects, where five emerging digital Industry 4.0 themes evaluated. [ABSTRACT FROM AUTHOR]

In recent years, our world has experienced significant disruptions due to the COVID-19 pandemic, and Russia's 2022 invasion of Ukraine, impacting human activities and the global environment. This paper explored air quality changes in Ukraine due to COVID-19, and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach. During the COVID-19 pandemic, strict quarantine policies in Ukraine led to a 2% reduction in tropospheric NO2 concentration before the lockdown and 4% during the lockdown period. Cities like Kyiv, Donetsk, and Dnipro exhibited reductions of 5%, 11%, and 16%, respectively. Total SO2 column concentration decreased by 6% before the lockdown and 2.5% during the lockdown period, except in high population density areas. Kyiv showed the highest reduction of 17% in SO2 concentration, while Donetsk and Dnipro exhibited an 11% reduction. However, during the Russian invasion, there was a significant increase in tropospheric NO2 concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction. The total SO2 column was 48% higher before the war but reduced throughout the country after the war, except for in Kyiv and a few central regions. These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios. [ABSTRACT FROM AUTHOR]

Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare. [ABSTRACT FROM AUTHOR]

As a fundamental service in near future, medical digital twin (MDT) is the virtual replica of a person. MDT applies new technologies of IoT, AI and big data to predict the state of health and offer clinical suggestions. It is crucial to secure medical digital twins through deep understanding of the design of digital twins and applying the new vulnerability tolerant approach. In this paper, we present a new medical digital twin, which systematically combines Haptic-AR navigation and deep learning techniques to achieve virtual replica and cyber–human interaction. We report an innovative study of the cyber–human interaction performance in different scenarios. With the focus on cyber resilience, a new solution of vulnerability tolerant is the must in the real-world MDT scenarios. We propose a novel scheme for recognising and fixing MDT vulnerabilities, in which a new CodeBERT-based neural network is applied to better understand risky code and capture cybersecurity semantics. We develop a prototype of the new MDT and collect several real-world datasets. In the empirical study, a number of well-designed experiments are conducted to evaluate the performance of digital twin, cyber–human interaction and vulnerability detection. The results confirm that our new platform works well, can support clinical decision and has great potential in cyber resilience. [ABSTRACT FROM AUTHOR]

A digital twin is often adopted in computer simulations to expedite autonomous vehicle developments by using the simulated 3D environment that reflects a physical environment. In particular, traffic simulations are a crucial part of training the driving logic before the field test of an autonomous vehicle is performed on specific regions to adapt to the region-specific, dynamic traffic conditions. Currently, the traffic conditions are either synthesised by tools (e.g. using mathematical models) or created manually (using domain knowledge), which cannot reflect the realistic, region-specific conditions or will require extensive labour works. In this article, we propose an automatic methodology to model the real-world traffic conditions captured by the sensor data and to reproduce the modeled traffic in the digital twin. We have built the tools based on the methodology and use the KITTI dataset to validate the effectiveness of the tools. To recreate the region-specific traffic, we present the results of capturing, modelling, and recreating the two-wheeler traffic condition on the Southeast Asia road. Our experimental results show that the proposed method facilitates the simulation of real-world, Southeast Asia-specific traffic conditions by removing the needs of the synthesised traffic and the labour hours. [ABSTRACT FROM AUTHOR]

There has been a vision of creating bridge digital twins as virtual simulation models of bridge assets to facilitate remote management. Bridge model updating is one digital twin technology which can enable the continuous updating of the structural model as new monitoring data is collected. This paper examines why there is currently little industry uptake of monitoring, modelling and model updating for the operation and maintenance of bridges despite over two decades of research in these fields. The study analyses the findings from a series of semi-structured industry interviews with expert bridge professionals in the U.K. and from an extensive literature survey of bridge model updating studies to examine the disconnects between research and practice and the practical issues of implementing bridge model updating. In particular, the study found that localised damage resulting in local reduction in structural stiffness, a key assumption made in the majority of research, is subject to question by practitioners as many common types of bridge damage may not induce noticeable change in structural stiffness that existing model updating techniques would identify. Key recommendations for future research are proposed to drive adoption of bridge monitoring, modelling and model updating and thus realise their industrial value. [ABSTRACT FROM AUTHOR]

Transdermal fentanyl patches are an effective alternative to the sustained release of oral morphine for chronic pain management. Due to the narrow therapeutic range of fentanyl, the concentration of fentanyl in the blood needs to be carefully monitored. Only then can effective pain relief be achieved while avoiding adverse effects such as respiratory depression. This study developed a physics-based digital twin of a patient by implementing drug uptake, pharmacokinetics, and pharmacodynamics models. The twin was employed to predict the in-silico effect of conventional fentanyl transdermal in a 20-80-year-old virtual patient. The results show that, with increasing age, the maximum transdermal fentanyl flux and maximum concentration of fentanyl in the blood decreased by 11.4% and 7.0%, respectively. However, the results also show that as the patient's age increases, the pain relief increases by 45.2%. Furthermore, the digital twin was used to propose a tailored therapy based on the patient's age. This predesigned therapy customized the duration of applying the commercialized fentanyl patches. According to this therapy, a 20-year-old patient needs to change the patch 2.1 times more frequently than conventional therapy, which leads to 30% more pain relief and 315% more time without pain. In addition, the digital twin was updated by the patient's pain intensity feedback. Such therapy increased the patient's breathing rate while providing effective pain relief, so a safer treatment. We quantified the added value of a patient's physics-based digital twin and sketched the future roadmap for implementing such twin-assisted treatment into the clinics. [ABSTRACT FROM AUTHOR]

This document discusses the development of digital twin (DT) technologies and their application in the field of vehicle system dynamics. It explains that digital twin technologies are closely related to concepts such as Industry 4.0, IoT, and Big Data, and have become increasingly important in engineering tasks such as design optimization and predictive maintenance. The document emphasizes the need to understand the transition from virtual prototyping to digital twin methodologies, as well as the combination of vehicle dynamics modeling and data-based approaches in vehicle engineering applications. It also highlights that more research is needed in this area to improve the system architecture and developmental framework of digital twin techniques. The document concludes by inviting researchers to contribute further innovations and research papers on digital twins in Vehicle System Dynamics. [Extracted from the article]