Digital Twin Technology Research Articles

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

Fire Station Management System Based on Digital Twin

To streamline travel, alleviate traffic congestion, and enhance overall traffic safety, the Internet of Vehicles (IoV) technology has emerged. This not only simplifies the lives of modern individuals but also integrates with other technologies, fostering advancements in various fields. Notably, the integration of digital twin technology with IoV is widespread, enabling the visualization of road conditions for informed route decisions and optimization. This paper focuses on applying this capability to a fire truck management system, empowering swift route selection, optimal vehicle deployment, and efficient execution of rescue activities in the shortest time and at the lowest cost.

Using Social Media and Artificial Intelligence to Find Your Purpose.

These days, the distractions of the digital world often obscure the journey to discover one's purpose. Yet these distractions hold clues to that very purpose. This article explores social media and artificial intelligence (AI) as tools to assist individuals in uncovering and expressing their purpose. As a test subject, my social media data were analyzed by AI to identify my purpose, using accounts from LinkedIn, Facebook, Instagram, and WhatsApp. This led to my creating an AI bot named Purpose GPT, which is customized to facilitate the exploration of purpose across personal growth, professional development, thought leadership, and legacy-building dimensions. Purpose GPT employs ChatGPT 4 and the Educated Change Digital Twin technology to provide a nuanced approach to understanding and discovering one's multi-dimensional purpose in this world.

Digital Twin Model and Its Establishment Method for Steel Structure Construction Processes

At present, the informatization level in the construction process of steel structures is relatively low. Meanwhile, digital twin technology, with better interactive features, provides a new development direction for the intelligent construction of steel structures. Therefore, this paper introduces the concept of a digital twin into the steel structure construction process, analyzes the connotation and characteristics of the digital twin model, and proposes the digital twin model architecture for steel structure construction processes. Furthermore, a method for establishing a digital twin model for steel structure construction processes is presented, which includes three stages: the acquisition and transmission of physical space data, the construction of a digital twin virtual model, and information exchange in the digital twin model. Based on these concepts, this paper describes a digital twin system architecture for the steel structure construction process from the perspective of data flow in the digital twin model. Finally, with the application of information technology in the steel structure construction process of the university park library project in Xiongan New Area and the reconstruction and expansion project of the Nanchong Gaoping airport, the digital twin model and its establishment method methods are analyzed practically and demonstrated effectively in this study.

Heat dissipation capacity prediction and cooling adjustment using digital twin technologies for a typical space station cabin

Heat dissipation capacity prediction and cooling adjustment using digital twin technologies for a typical space station cabin

Digital Twins for Supporting Ageing Well: Approaches in Current Research and Innovation in Europe and Japan

One of the central social challenges of the 21st century is society’s aging. AI provides numerous possibilities for meeting this challenge. In this context, the concept of digital twins, based on Cyber-Physical Systems, offers an exciting prospect. The e-VITA project, in which a virtual coaching system for elderly people is being created, allows the same to be assessed as a model for development. This white paper collects and presents relevant findings from research areas around digital twin technologies. Furthermore, we address ethical issues. This paper shows that the concept of digital twins can be usefully applied to older adults. However, it also shows that the required technologies must be further developed and that ethical issues must be discussed in an appropriate framework. Finally, the paper explains how the e-VITA project could pave the way towards developing a Digital Twin for Ageing.

Digital Twin Technology of Human–Machine Integration in Cross-Belt Sorting System

The Chinese express delivery industry processes nearly 110 billion items in 2022, averaging an annual growth rate of 200%. Among the various types of sorting systems used for handling express items, cross-belt sorting systems stand out as the most crucial. However, despite their high degree of automation, the workload for operators has intensified owing to the surging volume of express items. In the era of Industry 5.0, it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems. Striking a balance between efficiency in handling express items and operator well-being is challenging. Digital twin technology offers a promising solution in this respect. A realization method of a human–machine integrated digital twin is proposed in this study, enabling the interaction of biological human bodies, virtual human bodies, virtual equipment, and logistics equipment in a closed loop, thus setting an operating framework. Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources, construction of the relationship between operator fatigue and operation efficiency based on physiological measurements, virtual model construction, and an online optimization module based on real-time simulation. The feasibility of the proposed method was verified in an express distribution center.

Embedded Highway Health Maintenance System Based on Digital Twin Superposition Model

INTRODUCTION: The highway monitoring data acquisition technology develops quickly. Based on the traditional form of continuous monitoring, intelligent management system focuses on digital and wireless transmission. In the operation of highway maintenance system, each system is independent of each other, lacking of effective connection. Moreover, the level of continuous monitoring is obviously backward, which restricts the development of highway health monitoring. It is necessary to further study the level of integration to achieve the real-time tracking and the monitoring of highway’s healthy development.
 OBJECTIVES: This paper presents a highway health maintenance system based on digital twin technology, which intends to provide a solution for efficient, stable and automatic data transmission of the highway operation and maintenance management.
 METHODS: The output of the algorithm after the noise reduction effect is compared with the data containing the generated noise. The average number of nodes is set before running the algorithm to determine the actual length of the vertical position of the embedded sensor (calculating the position of two sensor nodes). The vertical length can be referred to the combined noise level formed and the combined test to determine the position. With the help of the overall data, it can be seen that the Kalman low-pass filtering algorithm can well describe the trend of the received signal and retain the key information in the received signal.
 RESULTS: It proves that the algorithm in this paper has fast calculation speed and high efficiency, and the basic working principle is simple. Thus, it is a good data denoising solution.
 CONCLUSION: The output in the paper ensures the data exchange and the update of the whole life cycle of highway, defines the digital twin entity model, and provides a reference for the establishment of information and data network.

A multiparameter simulation-driven analysis of ship response when turning concerning a required number of irregular wave realizations

The growing implementation of Decision Support Systems on modern ships, digital-twin technology, and the introduction of autonomous vessels cause the marine industry to seek accurate modeling of vessel response. Despite the contemporary 6DOF models can be used to predict ship motions in irregular waves, the impact of their stochastic realization is usually neglected and remains under-investigated. Especially in the case of turning, differences arising from the stochastic representation of the waves may result in excessive ship motions or even stability failure during maneuver execution. Therefore, in this study, statistical distributions of maximum amplitudes of roll, pitch, and lateral acceleration calculated in two representative locations on board a passenger vessel were analyzed concerning stochastic wave realization and existing extremes. The research utilized 6DOF simulation data and numerous realizations of the irregular wave with random phases of its components. Furthermore, the required number of wave realizations allowing for capturing the actual ranges of ship response at an assumed confidence level has been determined and analyzed. Ultimately, the results were compared in the safety-critical cases concerning various wave and operational conditions. The outcome of this study may be found useful by all parties involved in developing maritime autonomous systems and modeling ship motions.

Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS’s ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.

The residual life prediction of power grid transformers based on GA-ELM computational model and digital twin data

As one of the core equipment of the power grid, the operation status of transformers directly affects the stability and reliability of the power system. To accurately evaluate the remaining life of power grid transformers, a genetic algorithm is applied to optimize the Extreme Learning Machine based on digital twin technology. Then, considering changes in load rate, a residual life prediction model for power grid transformers is constructed. From the results, the error of the research method was within 2℃, with a maximum error of only 1.76℃. The research model converged with a fitness value of 0.04 at 150 iterations. It showed good predictive performance for hot spot temperatures under different load rates, with an average accuracy of 99.97%. Compared with backpropagation models and extreme learning machine models, the research method improved accuracy by 2.85% and 1.01%, respectively, with small and stable prediction errors. It verified the superiority of the research model, indicating that the research method can improve the accuracy of predicting the remaining life for power grid transformers. By monitoring the operation status of transformers in real-time, potential faults can be detected in a timely manner. The maintenance and replacement can be carried out in advance to avoid power outages caused by equipment damage. In addition, the research can provide reference for the planning and design of power systems, and support the stability and reliability of power systems.

Conceptual Design of a 3D Visualization Platform for Ships Based on Digital Twin Technology

Addressing the challenges in the current ship’s lifecycle design process, such as high difficulty in information sharing and complex installation of design environments, this study proposes a concept design of a ship 3D visualization platform based on digital twin technology. It constructs a web-based ship 3D visualization platform with an interactive interface of realism. The aim is to avoid cumbersome client-side programs and provide a method to showcase and interact with ship 3D models on web pages. This method not only reduces the threshold for ordinary users but also significantly enhances the work efficiency of professional designers. Following the model construction theory of digital twin platforms, it utilizes WebGL technology and the Three.js library to build ship 3D scenes, achieving ship 3D visualization. Functional interaction among modules is implemented using JavaScript, establishing the digital twin of the ship. Test results indicate that the proposed concept design of the ship 3D visualization platform can preliminarily fulfill the basic requirements of achieving ship 3D display, rendering, and interaction on web platforms. The proposed method can support the development of ship online collaborative design systems based on front-end internet technologies.

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

WITHDRAWN: Digital twin-driven machining process for multi-process irregular-shaped parts manufacturing

With the advancement of high-end manufacturing industries, certain critical parts exhibit peculiar shapes and characteristics of multi-process manufacturing, which intensify milling difficulties and give rise to error propagation effects during the machining process. This necessitates machining systems to be adaptive to different working conditions and capable of real-time error analysis and optimization. Digital twin technology can create virtual replicas of physical entities to observe, analyze, and optimize the machining process. Therefore, this paper proposes a framework for digital twin-driven machining of multi-process irregular-shaped parts, consisting of preparation, processing, and optimization stages. In the preparation stage, a mapping relationship between the workpiece and machine tool motion coordinates is established to create a twin simulation environment consistent with actual operational parameters. In the processing stage, the digital thread dynamically updates the digital twin workpiece and machine tool status in real-time, analyzing the machining errors for each process to assess the finished quality. In the optimization stage, a cloud-based machining knowledge graph is constructed to enhance the precision of machining optimization decisions under different error sources and causes in the digital twin system. Finally, a case study of a typical multi-process irregular-shaped part machining task validates the reliability and superiority of the digital twin-driven method. The case study demonstrates that in a total of 260 machining processes, the digital twin-driven method improves machining accuracy by 40%, saves 34.9% of machining time, and alleviates 70% of machining downtime compared to existing machining methods.

VR-based digital twin for remote monitoring of mining equipment: Architecture and a case study

BackgroundTraditional methods for monitoring mining equipment rely primarily on visual inspections, which are time-consuming, inefficient, and hazardous. This article introduces a novel approach to monitoring mission-critical systems and services in the mining industry by integrating virtual reality (VR) and digital twin (DT) technologies. VR-based DTs enable remote equipment monitoring, advanced analysis of machine health, enhanced visualization, and improved decision making. MethodsThis article presents an architecture for VR-based DT development, including the developmental stages, activities, and stakeholders involved. A case study on the condition monitoring of a conveyor belt using real-time synthetic vibration sensor data was conducted using the proposed methodology. The study demonstrated the application of the methodology in remote monitoring and identified the need for further development for implementation in active mining operations. The article also discusses interdisciplinarity, choice of tools, computational resources, time and cost, human involvement, user acceptance, frequency of inspection, multiuser environment, potential risks, and applications beyond the mining industry. ResultsThe findings of this study provide a foundation for future research in the domain of VR-based DTs for remote equipment monitoring and a novel application area for VR in mining.

Analysis of digital twin and its physical object: Exploring the efficiency and accuracy of datasets for real-world application

The concept of “digital twin” has recently gained popularity due to its ability to create a virtual representation of systems in order to improve the performance of its cyber-physical counterpart. This study compares and analyses datasets of digital twins and their corresponding physical objects to determine the effectiveness and dependability of digital twin technology for practical applications. The present research aims to proffer a framework for ascertaining the level of (dis)similarity between a physical object and its digital twin equivalent. A study of water distribution (WADI) is put into perspective. Findings revealed that the proffered framework presents a method for ascertaining a (dis)similarity level of a physical object (features) and its digital twin using adequate statistical tests.

Digital methods in architectural and environmental renovation of buildings for a public function

This article explores advanced digital methods for comprehensive renovation of industrial buildings into public spaces. Four different approaches are described in detail: 1. Digital mapping and modeling: allows accurate spatial analysis to be performed for optimizing design solutions before physical renovation; combines virtual and augmented reality to create an immersive experience. 2. Integration of augmented and virtual reality: facilitates joint decision-making by visualizing proposed projects and thus increasing public engagement. Historical and cultural narratives are overlapped, strengthening the connection between the past and the present. 3. Smart building systems: include IoT devices and sensors to optimize energy efficiency, comfort, and overall functionality; solve unique challenges related to the conversion of industrial buildings. 4. Digital twin technology: creates a dynamic virtual copy for continuous real-time monitoring; uses machine learning for predictive analysis, increasing operational efficiency and sustainability. The convergence of technologies and architectural innovations presents a transformative force in the context of architectural and environmental renovation of industrial buildings, representing a change of the paradigm in the renovation of industrial facilities for re-use as a public function.

The impact of the war on the development of freight transportation in Ukraine

The paper analyzes the relevance of the chosen research topic, in particular, the importance of the freight transportation sector for the development of the Ukrainian economy. The significant impact of a full-scale war on the dynamics of freight transportation and structural changes in this market, which began in 2022, is substantiated. The dynamics of prices for A95 gasoline and diesel fuel for 2022-2024, UAH, is analyzed and analyzed. The dynamics of prices for freight transportation for 2022-2023 is analyzed and substantiated, UAH. The structure of the Ukrainian freight transportation market by export and import of goods as of 2022 is analyzed and analyzed. It is determined that the war affected the rise in fuel prices, which led to higher transportation prices and fuel shortages. It is determined that the largest share in 2022 belonged to rail transport, namely: 50% were exports and 41% were imports. It is determined that most of the cargo was transported by road and rail during the study period. The dynamics of the volume of freight traffic in Ukraine by types of transport for 2021-2022, million tons, is analyzed and substantiated. The paper analyzes the introduction of electronic queues and electronic registration "e-queue". The main problems faced by freight transportation are identified. A way to optimize the costs of enterprises under martial law is proposed. The expediency of introducing digital technologies, which should primarily lead to time savings and faster delivery, is substantiated. The use of a logistics chain management system based on the Digital Twin technology is proposed, the use of which will positively affect the organization of the process of cargo transportation by road and minimize unnecessary costs. The process of implementing Digital Twin in the logistics processes of a carrier company is depicted schematically. Conclusions are drawn about the impact of the war on the development of freight transportation in Ukraine.

Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations

Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages.

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.