Implementation Of Digital Twin Research Articles

Examining grid-forming inverters for power restoration using power-hardware in-the-loop and Digital Twins approaches with Real-time Digital Simulation

The current pursuit of ambitious decarbonization targets is driving a swift transformation of the power grid, marked by a surge in the production of renewable energy. The expansion on application of renewable energy hinges significantly on Distributed Energy Resources but system operators are grappling with challenges due to the opaque nature of DER operations. This opacity introduces considerable risks to grid stability, as the burgeoning volume of DERs may surpass the existing power network’s capacity. In response, the advent of Digital Twins (DT) technology offers a viable remedy by creating virtual counterparts of the physical grid infrastructure that necessitate transmitting minimal data. Digital Twins technology circumvents the hindrances associated with real-time data flows and bolsters the transparency of the system. To foster widespread implementation of DT within the sector, it is imperative to cultivate and validate its application through practical trials. To this end, Power Hardware-in-the-Loop (PHIL) experiments are employed to juxtapose the efficacy of actual power components against that of the DT models. The experiments involve connecting Grid-forming Inverter to a Real-time Digital Simulator (RTDS) for PHIL and DT testing, allowing for an in-depth analysis of the behaviour of photovoltaic inverter. This paper elucidates a platform engineered for immediate simulation tailored to DT and PHIL approaches. This platform is designed to prototype, exhibit, and evaluate grid-forming inverters under different scenarios that are critical for power restoration. With the help of simulation exchange, Perez Model is recommended to add in the DT model to increase the accuracy comparing with the PHIL model. The entire restoration process can therefore be comprehensively represented and analysed.

Opportunities and Threats of Adopting Digital Twin in Construction Projects: A Review

Digital twin (DT) is recognized as a pillar in the transition from traditional to digital construction, yet the risks (opportunities and threats) associated with its implementation have not been thoroughly determined in the literature. In addition, there is a scarcity of research relating the risks of DT implementation to DT maturity levels, which has hindered the optimum consideration of such risks when DT is adopted at different maturity levels. To address these gaps, this study conducted a literature review of 1889 documents from Scopus and Web of Science databases. After rigorous filtration, 72 documents were selected and comprehensively reviewed. A total of 47 risk factors (RFs) were identified and categorized into opportunities (economic, technical, environmental and sustainability, monitoring and safety, and management) and threats (economic, technical, and policy and management). Subsequently, these RFs were mapped onto the five-level DT maturity model, providing users with insights into opportunities and threats on each level. The exhaustive list of RFs and proposed integration of a DT maturity model with corresponding RFs enables stakeholders to identify the risks in their specific use cases and facilitate the decision-making and success in transition across various levels of DT in real-life construction projects.

Digital twin-based modeling of natural gas leakage and dispersion in urban utility tunnels

Background Unexpected leakage accidents of the natural gas pipeline inside urban utility tunnels can pose great threats to public safety, property, and the environment. It highlights the modeling of natural gas leakage and dispersion dynamics, especially from a digital twin implementation perspective facilitating effective emergency response in a data-driven way. Methods In this study, a digital twin-based emergency response framework for gas leakage accidents in urban utility tunnels is proposed. Within this framework, the data-calibrated gas concentration prediction (DC-GCP) model is developed by integrating the Lattice Boltzmann Method (LBM) with data assimilation (DA) techniques. This combination enables accurate spatiotemporal predictions of gas concentrations, even with a prior or inaccurate gas leakage source term. Specifically, we develop a high-performance LBM-based gas concentration prediction model using the parallel programming language Taichi Lang. Based on this model, real-time integration of gas sensor data from utility tunnels is achieved through the DA algorithm. Therefore, the predicted results can be calibrated by the continuous data in the absence of complete source term information. Furthermore, a widely used twin experiment and statistical performance measures (SPMs) are used to evaluate and validate the effectiveness of the proposed approach. Results The results show that all SPMs progressively converge towards their ideal values as calibration progresses. And both the gas concentration predictions and the source term estimations can be calibrated effectively by the proposed approach, achieving a relative error of less than 5%. Conclusions This study helps for dynamic risk assessment and emergency response of natural gas leakage accidents, as well as facilitating the implementation of predictive digital twin in utility tunnels.

The Strategy of Digital twin Implementation at the Cyber-Physical Enterprises

The strategy of digital twin (DT) implementation at the cyber-physical enterprises is proposed, which is due to the need for high-quality and effective reconstruction of Ukraine. The TISM and MICMAC methods are used to study the factors that influence the strategy of DT implementation to support the guaranteed functioning of a cyber-physical system. 13 main factors, that determine strategy success, were identified, and a model of strategy realization was built in the form of a six-level interaction diagram (digraph). The factors are classified by influence and dependence. The substantiated conclusion is that the state support of DT implementation strategy, the availability of design standards, data protection methods, legislative regulation and implementation experience by other enterprises are key factors to the success of strategy for DT implementation at the enterprise.

Матрица бизнес-выгод как инструмент исследования потенциала цифровизации на примере мини-завода для производства невзрывчатых компонентов эмульсионных взрывчатых веществ

Modern business solutions set a trend towards integrating digital technologies into all areas of corporate organization. Digitalization plays a key role in modern business, enabling companies to increase the process efficiency, reduce costs, and improve product quality and maintenance efficiency. It also helps to be competitive in the marketplace and adapt to changing customer demands. In order to maximize the effects of implementing digitalization, the transformation needs to encompass not only the local business processes and business models, but also the corporate culture. The study analyses the process of exploring the digitalization potential for a modular mobile plant (mini-plant) that produces non-explosive components of emulsion explosives developed by AZOTTECH. The paper identifies the key points necessary for successful implementation of the digitalization process at the mini-plant, which can serve as a basis for designing a digital twin implementation strategy. It also describes application of a modern approach in performance evaluation using the matrix of business benefits.

Digital Twin applied to Predictive Maintenance for Industry 4.0

Abstract The major concept of the future Industrial 4.0 framework is the integration of Artificial Intelligence (AI) and the implementation of Digital Twin (DT), which avoids serious economic losses caused by unexpected equipment failures and significantly improves system reliability. DT is an emerging technology in the context of digital transformation that enables the monitoring, diagnosis, energy efficiency, and optimization of different systems. Numerous initiatives have shown how artificial intelligence (AI) can enhance the performance of DT for industrial applications. This paper describes a data based DT architecture for the Monitoring, and Predictive Maintenance (PdM) in manufacturing. This new concept is based on deep learning, specifically the Autoencoder model. The system was tested on a real industry example, by developing the data collection, data system analysis and applying the deep learning approach. The data was collected from a Profinet communication network installed on an automated system. This approach enables better quality results and more efficient management of the weaver's workshop. Lastly, to prove the efficiency and the accuracy of the newly developed approach, an example is shown.

Implementation of digital twins as a tool for increasing the efficiency of business operations

ABSTRACT This research aims to evaluate the impact of implementing digital twins on companies’ operational activities in terms of enhancing their efficiency. Research methods include data analytics and visualization to explore the intricacies of creating and utilizing digital twins among high-tech leaders, as well as the correlation between expenses on innovative digital twin solutions and company revenues, followed by clustering to assess the impact of costs associated with the development and implementation of digital twins on enhancing the efficiency of business operational activities. In this context, the research presents a novel approach to measuring the influence of digital twin implementation on the efficiency of operational activities of high-tech companies based on correlation and cluster analysis. The findings indicate that all selected high-tech companies analyzed successfully applied digital twins to enhance their operational efficiency, as evidenced by the high correlation coefficient between expenses on digital twins and company revenues. Cluster analysis, distinguishing between two clusters of leaders and followers in the use of digital twins, allowed for identifying some distinctive characteristics of a leader in digital twin utilization and presenting a vision for transitioning to management based on the digitization of all company business processes.

The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss.

Volumetric Muscle Loss (VML) injuries are characterized by significant loss of muscle mass, usually due to trauma or surgical resection, often with a residual open wound in clinical settings and subsequent loss of limb function due to the replacement of the lost muscle mass with non-functional scar. Being able to regrow functional muscle in VML injuries is a complex control problem that needs to override robust, evolutionarily conserved healing processes aimed at rapidly closing the defect in lieu of restoration of function. We propose that discovering and implementing this complex control can be accomplished by the development of a Medical Digital Twin of VML. Digital Twins (DTs) are the subject of a recent report from the National Academies of Science, Engineering and Medicine (NASEM), which provides guidance as to the definition, capabilities and research challenges associated with the development and implementation of DTs. Specifically, DTs are defined as dynamic computational models that can be personalized to an individual real world "twin" and are connected to that twin via an ongoing data link. DTs can be used to provide control on the real-world twin that is, by the ongoing data connection, adaptive. We have developed an anatomic scale cell-level agent-based model of VML termed the Wound Environment Agent Based Model (WEABM) that can serve as the computational specification for a DT of VML. Simulations of the WEABM provided fundamental insights into the biology of VML, and we used the WEABM in our previously developed pipeline for simulation-based Deep Reinforcement Learning (DRL) to train an artificial intelligence (AI) to implement a robust generalizable control policy aimed at increasing the healing of VML with functional muscle. The insights into VML obtained include: 1) a competition between fibrosis and myogenesis due to spatial constraints on available edges of intact myofibrils to initiate the myoblast differentiation process, 2) the need to biologically "close" the wound from atmospheric/environmental exposure, which represents an ongoing inflammatory stimulus that promotes fibrosis and 3) that selective, multimodal and adaptive local mediator-level control can shift the trajectory of healing away from a highly evolutionarily beneficial imperative to close the wound via fibrosis. Control discovery with the WEABM identified the following design principles: 1) multimodal adaptive tissue-level mediator control to mitigate pro-inflammation as well as the pro-fibrotic aspects of compensatory anti-inflammation, 2) tissue-level mediator manipulation to promote myogenesis, 3) the use of an engineered extracellular matrix (ECM) to functionally close the wound and 4) the administration of an anti-fibrotic agent focused on the collagen-producing function of fibroblasts and myofibroblasts. The WEABM-trained DRL AI integrates these control modalities and provides design specifications for a potential device that can implement the required wound sensing and intervention delivery capabilities needed. The proposed cyber-physical system integrates the control AI with a physical sense-and-actuate device that meets the tenets of DTs put forth in the NASEM report and can serve as an example schema for the future development of Medical DTs.

Digital Twin Implementation in Additive Manufacturing: A Comprehensive Review

The additive manufacturing (AM) field is rapidly expanding, attracting significant scientific attention. This family of processes will be widely used in the evolution of Industry 4.0, particularly in the production of customized components. However, as the complexity and variability of additive manufacturing processes increase, there is an increasing need for advanced techniques to ensure quality control, optimize performance, and reduce production costs. Multiple tests are required to optimize processing variables for specific equipment and processes, to achieve optimum processing conditions. The application of digital twins (DTs) has significantly enhanced the field of additive manufacturing. A digital twin, abbreviated as DT, refers to a computer-generated model that accurately depicts a real-world object, system, or process. A DT comprises the complete additive manufacturing process, from the initial conception phase to the final manufacturing phase. It enables the manufacturing process to be continuously monitored, studied, and optimized in real time. DT has emerged as an important tool in the additive manufacturing industry. They allow manufacturers to enhance the process, improve product quality, decrease costs, and accelerate innovation. However, the development of DT in AM is an iterative and continuous process. It requires collaboration between domain experts, data scientists, engineers, and manufacturing teams to guarantee an accurate representation of the process by the digital twin. This paper aims to provide a comprehensive analysis of the current state of DT for additive manufacturing, examining their applications, benefits, challenges, and future directions.

Determining the stationary digital twins implementation barriers for sustainable construction projects

PurposePrevious research has demonstrated that Digital Twins (DT) are extensively employed to improve sustainable construction methods. Nonetheless, their uptake in numerous nations is still constrained. This study seeks to identify and examine the digital twin’s implementation barriers in construction building projects to augment operational performance and sustainability.Design/methodology/approachAn iterative two-stage approach was adopted to explore the phenomena under investigation. General DT Implementation Barriers were first identified from extant literature and subsequently explored using primary questionnaire survey data from Hong Kong building industry professionals.FindingsSurvey results illustrated that Lack of methodologies and tools, Difficulty in ensuring a high level of performance in real-time communication, Impossibility of directly measuring all data relevant to the DT, need to share the DT among multiple application systems involving multiple stakeholders and Uncertainties in the quality and reliability of data are the main barriers for adopting digital twins' technology. Moreover, Ginni’s mean difference measure of dispersion showed that the stationary digital twin’s barriers adoption is needed to share the DT among multiple application systems involving multiple stakeholders.Practical implicationsThe study’s findings offer valuable guidance to the construction industry. They help stakeholders adopt digital twins' technology, which, in turn, improves cost efficiency and sustainability. This adoption reduces project expenses and enhances environmental responsibility, providing companies a competitive edge in the industry.Originality/valueThis research rigorously explores barriers to Digital Twin (DT) implementation in the Hong Kong construction industry, employing a systematic approach that includes a comprehensive literature review, Ranking Analysis (RII) and Ginni’s coefficient of mean difference (GM). With a tailored focus on Hong Kong, the study aims to identify, analyze and provide novel insights into DT implementation challenges. Emphasizing practical relevance, the research bridges the gap between academic understanding and real-world application, offering actionable solutions for industry professionals, policymakers and researchers. This multifaceted contribution enhances the feasibility and success of DT implementation in construction projects within the Architecture, Engineering and Construction (AEC) sector.

A unified framework for digital twin development in manufacturing

The concept of digital twin (DT) is undergoing rapid transformation and attracting increased attention across industries. It is recognised as an innovative technology offering real-time monitoring, simulation, optimisation, accurate forecasting and bi-directional feedback between physical and digital objects. Despite extensive academic and industrial research, DT has not yet been properly understood and implemented by many industries, due to challenges identified during its development. Existing literature shows that there is a lack of a unified framework to build DT, a lack of standardisation in the development, and challenges related to coherent goals of DT in a multi-disciplinary team engaged in the design, development and implementation of DT to a larger scale system. To address these challenges, this study introduces a unified framework for DT development, emphasising reusability and scalability. The framework harmonises existing DT frameworks by unifying concepts and process development. It facilitates the integration of heterogeneous data types and ensures a continuous flow of information among data sources, simulation models and visualisation platforms. Scalability is achieved through ontology implementation, while employing an agent-based approach, it monitors physical asset performance, automatically detects faults, checks repair status and offers operators feedback on asset demand, availability and health conditions. The effectiveness of the proposed DT framework is validated through its application to a real-world case study involving five interconnected air compressors located at the Connected Facility at Devonport Royal Dockyard, UK. The DT automatically and remotely monitors the performance and health status of compressors, providing guidance to humans on fault repair. This guidance dynamically adapts based on feedback from the DT. Analyses of the results demonstrate that the proposed DT increases the facility’s operation availability and enhances decision-making by promptly and accurately detecting faults.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

DRIVE-THRU REVOLUTION: ENHANCING CUSTOMER SATISFACTION THROUGH DIGITAL TWINS

In business, understanding consumers is paramount. Digital Twins technology provides virtual representations of consumers and real-time insights. This study evaluates the implementation of Digital Twins in drive-thru services, focusing on efficiency and user experience. A survey was conducted with 2000 drive-thru consumers in the US and analyzed using SEM-PLS. The findings of this study indicate the significance of Digital Twins in purchase accuracy, interactivity, and satisfaction. The importance of strategic implementation is highlighted to maximize benefits and offer a framework for secure implementation. Limitations of this research include geographical constraints of the respondents, data collection methods, and limitations in relevant literature due to the new nature of Digital Twins technology in the context of drive-thru services. Future research needs to explore cross-cultural comparative research, longitudinal studies, and mixed-methods approaches to further understand the impact of this technology.

Energy 4.0: Challenges and Enablers of Digital Twin Application in Power Plant

Digital twins have recently garnered attention as digital solutions in "Energy 4.0" that will reshape the future of the power generation industry toward the digitalization era. It is supported by the rapid advancement of data connectivity and computational power that intensifies the potential of the digital twin approach in addressing the energy trilemma. Despite its popularity, the preliminary analysis revealed a lack of publications discussing the implementation of digital twins in the power generation industry. The researcher may find the difficulties and face the issues that prevent them from exploring this study area. Therefore, this article will perform a literature review of the selected publications to analyze the challenges and enablers of the digital twin implementation in power plants. The selection of articles from multiple databases is refined based on keywords search, publication time frame, and inclusion criteria. The study found that the challenges can be divided into nine categories, and eight enablers have been identified to address the issues that arise in digital twin applications. The findings contribute to the body of knowledge on digital twin applications by proposing the operational ecosystem framework to illustrate the interaction between enablers and challenges.

A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry

Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have enabled integrated digital technologies (e.g., digital twins) as a promising trend to achieve emission reduction and net-zero. While digital twins in the construction sector have shown rapid growth in recent years, most applications focus on the improvement of productivity, safety and management. There is a lack of critical review and discussion of state-of-the-art digital twins to improve sustainability in this sector, particularly in reducing carbon emissions. This paper reviews the existing research where digital twins have been directly used to enhance sustainability throughout the entire life cycle of a building (including design, construction, operation and maintenance, renovation, and demolition). Additionally, we introduce a conceptual framework for this industry, which involves the elements of the entire digital twin implementation process, and discuss the challenges faced during deployment, along with potential research opportunities. A proof-of-concept example is also presented to demonstrate the validity of the proposed conceptual framework and potential of digital twins for enhanced sustainability. This study aims to inspire more forward-thinking research and innovation to fully exploit digital twin technologies and transform the traditional construction industry into a more sustainable sector.

An Exploratory view of Application of Digital Twin Technology in Education

Digital twins have gained significant attention and become a buzzword in recent years. The advancement of technologies such as the Internet of Things (IoT), artificial intelligence (AI), data analytics, and cloud computing has made it easier and more cost-effective to collect, analyze, and process vast amounts of data. This has enabled the creation and implementation of digital twins on a larger scale. Digital twins provide valuable insights and data- driven information to support decision-making. They enable organizations to simulate scenarios, test different strategies, and assess the impact of changes or interventions before implementing them in the physical world and thus help minimize risks and uncertainties associated with decision- making processes. Applications of Digital twins are not limited to specific industries or sectors. They have versatile applications across domains such as manufacturing, healthcare, transportation, smart cities, energy management etc. This broad applicability has contributed to the widespread interest and buzz around digital twins. The academic world has also not remained untouched with the advancement in this technology and efforts are on in the direction of designing Digital Twins for various aspects of education including supporting teaching, research, and administrative processes. As the technology continues to evolve, new and innovative applications are likely to emerge, transforming various aspects of teaching, learning, research, and administration in the academic sphere. In this study an effort is made to bring together the work carried out in the field of application of digital twins technology for teaching. Existing studies have been deliberated upon to analyze the progress and discover the prospects of bringing the digital teachers closer to human teachers. It can be inferred that as the technology continues to evolve, new and innovative applications are likely to emerge, transforming various aspects of teaching and learning in the academic sphere. It is also important to note that while digital twins of teachers hold potential benefits, their implementation and adoption in real-world educational settings are still developing. Ethical considerations such as data privacy, algorithmic bias, and the need for human involvement in teaching roles needs to be carefully addressed to ensure their effective and equitable use in education

Medium-Level Architectures for Digital Twins: Bridging Conceptual Reference Architectures to Practical Implementation in Cloud, Edge and Cloud–Edge Deployments

The integration of Digital Twins (DTs) is becoming increasingly important in various industries. This entails the need for a comprehensive and practical IT infrastructure framework. This paper presents a modifiable medium-level architecture that serves as a link between established reference architectures such as RAMI 4.0 and the pragmatic implementation of Digital Twins. The functionalities of an IT infrastructure are considered, and functional hardware and software components for fulfilling these are described. The proposed architecture is suitable for various deployment scenarios, including local, cloud and hybrid cloud–edge configurations. In order to improve the applicability of the medium-level architecture, a step-by-step procedure is also proposed, which helps to transfer the overarching requirements for a Digital Twin into a suitable IT infrastructure. Finally, the results are demonstrated by an exemplary application to a two-stage industrial gearbox.

Digital Twin Implementation for Three-Dimensional Rotordynamic Response via Physics-Informed LSTM Neural Networks

Abstract The rotating assemblies of critical machinery are complex dynamical systems and rotordynamic model response prediction inaccuracy risks machinery failure leading to high production losses. Jeffcott, Euler beam, and high-fidelity 3D solid finite element models are frequently utilized for rotordynamic analyses. Even though the 3D rotor has the higher accuracy, beam models are still widely used in industrial applications. To improve prediction accuracy of the lower-fidelity Jeffcott and beam models, a rotordynamics physics-informed neural network (R-PINN) is proposed. This models physics-informed long short-term memory (LSTM) neural networks that utilize partial or limited measured data, by incorporating physical laws. This approach enables the creation of a Digital Twin, which can produce additional data and help remove noise and outliers. In the current study, two R-PINNs are introduced to validate the superior capability of the model for both low- and high-fidelity physics. Random noise of 10% is introduced into the measured data produced by the Digital Twin to replicate real-world noisy measurements. The result shows that both low- and high-fidelity physics R-PINNs can achieve high accuracy even with high noise data, thereby increasing the robustness of the model. The results clearly demonstrate the ability of the proposed R-PINN algorithm to enhance an Euler beam model's predicted response to the level of accuracy of a 3D solid element model's predicted response, the latter acting as a surrogate for test measurements in an actual application.

A Digital Twin for Assessing the Remaining Useful Life of Offshore Wind Turbine Structures

This paper delves into the application of digital twin monitoring techniques for enhancing offshore floating wind turbine performance, with a detailed case study that uses open-source digital twin software. We explore the practical implementation of digital twins and their efficacy in optimizing operations and predictive maintenance, focusing on controlling the real-time structural state of composite wind turbine structures and forecasting the remaining useful life by tracking the fatigue state in the structure. Our findings emphasize digital twins’ potential as a valuable tool for renewable energy, driving efficiency and sustainability in offshore floating wind installations. These aspects, along with the aforementioned simulations, whether in real-time or forecasted, reduce costly and unnecessary inspections and scheduled maintenance.

A preliminary investigation on enabling digital twin technology for operations and maintenance of urban underground infrastructure

Underground infrastructure plays a kind of crucial role in modern production and living, especially in big cities where the ground space has been fully utilized. In the context of recent advancements in digital technology, the demand for the application of digital twin technology in underground infrastructure has become increasingly urgent as well. However, the interaction and co-integration between underground engineering entities and virtual models remain relatively limited, primarily due to the unique nature of underground engineering data and the constraints imposed by the development of information technology. This research focuses on underground engineering infrastructure and provides an overview of the application of novel information technologies. Furthermore, a comprehensive framework for digital twin implementation, which encompasses five dimensions and combines emerging technologies, has been proposed. It thereby expands the horizons of the intersection between underground engineering and digital twins. Additionally, a practical project in Wenzhou serves as a case study, where a comprehensive database covering the project’s entire life cycle has been established. The physical model is visualized, endowed with functional implications and data analysis capabilities, and integrated with the visualization platform to enable dynamic operation and maintenance management of the project.