Digital Twin Applications Research Articles

The digital health care and digital twins as its components: the systematic review

The article presents systematic review of National and foreign publications concerning strategy of digital society development, definitions and concepts of digital twins in health care. The analysis of development of digital twins of patients and application of digital twins of human organs in medicine is presented. The experience of digital twins functioning in health care management system in Russia and abroad is presented.

Experimental study on the mechanical property of carbon fiber reinforced polymer with the combined application of digital twin and reduced order algorithm

AbstractRecognizing the anisotropy of mechanical property transfer between carbon fiber reinforced polymer (CFRP) layers, a unidirectional reduced‐order model (UROM) is proposed to comprehensively express the mechanical properties of each layer. A crucial focus is on effectively analyzing the real‐time mechanical properties of CFRP specimens, particularly in accurately identifying and predicting the interlaminar mechanical states of anisotropic CFRP laminated specimens. Moreover, the multi fidelity surrogate (MFS) is employed to replace the computationally intensive model, amalgamating high‐fidelity sensor data with low‐fidelity data obtained from the UROM. The UROM MFS is used to reduce the amount of data in the same layer of CFRP laminates, while preserving the characteristics between different layers and preserving the interlaminar information of CFRP laminates. Finally, the stress prediction results of the UROM MFS and the unreduced MFS model were compared and analyzed. The real‐time response speed of the u‐ROM MFS digital twin (DT) model was increased by 658%. The UROM MFS DT model effectively captures the mechanical properties of each layer of CFRP laminates, enables quick calculation of model parameters, and provides accurate predictions.Highlights First, the interlayer mechanical property of CFRP is analyzed with the combined application of experimental data, DT model, and UROM MFS. Second, MFS is employed to merge high‐fidelity sensor data of CFPR laminate with low‐fidelity data obtained from UROM. Third, the digital twin framework for CFPR laminate with high response speed and calculation accuracy is proposed.

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

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.

Recent Progress on Digital Twins in Intelligent Connected Vehicles: A Review

As an important enabling technology in the era of Industry 4.0, the intelligent connected vehicle (ICV) facilitates robust data interaction with the outside through sensors and communication technologies, ultimately making scientific decisions based on environmental perception information. However, due to constraints such as limited communication bandwidth and computing resources, the influx of data simultaneously impedes the sustainable optimisation of the vehicle decision making process at the same time. As a novel technology that effectively connects physical and virtual space, the special ability of the digital twin (DT) is to identify characteristics within a certain lifecycle, thereby garnering widespread attention across various industries. The purpose of this paper is to review the contribution of digital twins in the application field of intelligent vehicles and explore its potential for development. First, the key technologies of ICV provide a basis for the embedding of digital twins. Then, by analysing the development process and technical composition of digital twins, readers can better understand the concept of digital twins. Finally, the application of DTs in ICV is reviewed from the perspective of vehicles, traffic facilities, and occupants. Future challenges and opportunities in this direction are described at the same time.

Digital Twins for Discrete Manufacturing Lines: A Review

Along with the development of new-generation information technology, digital twins (DTs) have become the most promising enabling technology for smart manufacturing. This article presents a statistical analysis of the literature related to the applications of DTs for discrete manufacturing lines, researches their development status in the areas of the design and improvement of manufacturing lines, the scheduling and control of manufacturing line, and predicting faults in critical equipment. The deployment frameworks of DTs in different applications are summarized. In addition, this article discusses the three key technologies of high-fidelity modeling, real-time information interaction methods, and iterative optimization algorithms. The current issues, such as fine-grained sculpting of twin models, the adaptivity of the models, delay issues, and the development of efficient modeling tools are raised. This study provides a reference for the design, modification, and optimization of discrete manufacturing lines.

Co-evolutionary digital twins: A multidimensional dynamic approach to digital engineering

Digital Engineering (DE) must be able to maximize efficiency and update dynamically in order to keep up with the wave of global interconnection. The evolution of the Digital Twin (DT) plays a significant role in DE as it enables autonomous optimization and an iterative cycle. However, the intricacy of real-world settings renders conventional evolutionary investigations of a solitary DT inadequate in tackling intricate systems at a comprehensive lifecycle magnitude. To address this, this paper presents the Co-evolutionary DTs (CoEDT), which has several key characteristics. (1) CoEDT study the evolutionary behavior of interconnected multi-DT. (2) CoEDT use a co-evolutionary distributed system architecture and is a DT technology that integrate MBSE. (3) CoEDT offers detailed dynamic models for the complex interactions and co-evolution among multi-DT throughout the lifecycle. It also supports real time measurement of multi-DT behavior to detect system anomalies. The significance of CoEDT lies in providing a more comprehensive insight for future product development by constructing a parallel world that simulates the lifecycle. In the CoEDT, we tackle the behavioral identification and structure of these evolving multi-DT throughout product lifecycle through the following approaches. Initially, drawing inspiration from biological cytology, we have conceived a concept of Collective DTs (CollDTs) to observing a collective behavioral of multi-DT. Subsequently, we further developed CoEDT to depict co-evolutionary behavior patterns and established a co-evolution architecture for all DT throughout the lifecycle by fusing Model-Based Systems Engineering (MBSE). Then, dynamic expressions and algorithm that can measure the co-evolution of every DT are inferred using information theory. Finally, the viability of the proposed CoEDT framework is demonstrated through the development of a solid rocket engine, which promotes the application of DT in the DE.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices.

Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

This comprehensive review explores the application and impact of digital twin (DT) technology in bolstering the reliability of Floating Offshore Wind Turbines (FOWTs) and their supporting platforms. Within the burgeoning domain of offshore wind energy, this study contextualises the need for heightened reliability measures in FOWTs and elucidates how DT technology serves as a transformative tool to address these concerns. Analysing the existing scholarly literature, the review encompasses insights into the historical reliability landscape, DT deployment methodologies, and their influence on FOWT structures. Findings underscore the pivotal role of DT technology in enhancing FOWT reliability through real-time monitoring and predictive maintenance strategies, resulting in improved operational efficiency and reduced downtime. Highlighting the significance of DT technology as a potent mechanism for fortifying FOWT reliability, the review emphasises its potential to foster a robust operational framework while acknowledging the necessity for continued research to address technical intricacies and regulatory considerations in its integration within offshore wind energy systems. Challenges and opportunities related to the integration of DT technology in FOWTs are thoroughly analysed, providing valuable insights into the role of DTs in optimising FOWT reliability and performance, thereby offering a foundation for future research and industry implementation.

Digital twin-assisted service function chaining in multi-domain computing power networks with multi-agent reinforcement learning

The emerging computing power network (CPN) is believed to undergo the paradigm reformation of network function virtualization (NFV) and service function chaining (SFC). It is prerequisite to explore the performance upper bound of NFV-assisted CPN before truly deploying the NFV and SFC technologies onto physical networks. Inspired by the application of digital twin (DT) in the industry and due to its advantage in synchronizing physical objects with their virtual replicas, we propose to use the DT to assist the SFC deployment in the multi-domain CPN, with the aid of multi-agent deep deterministic policy gradient (MADDPG) framework. First, we build a dynamic SFC mapping problem in the virtual twin network layer, by modeling the computing power, link bandwidth, delay performance and the VNF ordering as DT objects and constraints, to jointly optimize the energy consumption, end-to-end delay and the VNF re-deploying cost. Then, the prioritized experience replay and re-parameterization trick-empowered centralized training and decentralized execution MADDPG framework is utilized to learn the SFC deployment, by taking each domain controller as one agent. Finally, numerical experiments are carried out to validate the effectiveness of MADDPG in the cross-domain SFC deployment. For performance verification, the deployment success rate, number of crossed domains, energy consumption, end-to-end latency and load balancing degree are all taken as metrics, to show the performance of MADDPG compared to other learning frameworks.

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

A Data-Driven Design Approach of an Energy Data Exchange Platform

The current digitalization of energy systems, particularly in urban contexts, lags behind the requirements set by urban energy system modelling, energy management, the development of digital twins, and other data-driven services. Additionally, existing energy data lacks standardized formats and coherence, hindering its full utilization. To address this challenge, this paper introduces a use case-driven design approach tailored to reflect the requirements for data integration and privacy set by city stakeholders. The objective is to design a data exchange platform (DEP) capable of serving a diverse range of use cases. This platform will facilitate the integrated utilization of energy data in energy management systems, digital twin applications, and other activities aimed at extracting valuable insights. These efforts are specifically designed to support decision-making processes of public stakeholders, private investors, and service providers. Consequently, the platform holds potential to attract additional investments in both the clean energy transition and the digitalization of energy infrastructure.

Performance evaluation of electro-mechanical railway interlocking system for digital twin application

The industries engaged healthcare, manufacturing, and construction automated with exquisite creativity and innovation at the doorstep of Industry 4.0. In this process, digital twins are diligently used to supplement industrial processes, reduce costs, monitor assets, streamline maintenance, reduce downtime, and enable the creation of connected goods. The research is aimed at finding the digital representation of electro-mechanical railway interlocking elements are simulated and its functionality is verified accordingly. With respect to carry out this research, the Indian Railways Mangalagiri Railway Station blueprints were collected to represent the physical Model which consists of signals, points, a crank handle, an LC gate, and many more elements. In this paper, three interlocking elements such as points, crank handle, and LC gate, are analysed, modelled, and simulated by using Model Sim ME of Libero SoC software. Representation of these digital elements were developed using a smart design canvas. Therefore, the proposed methodology will enhance the safety measures and security levels of the train movements and reduces the time required to replace the faulty relay. Similarly, in future, the proposed model could be dumped into the FPGA kit to verify the real-time data for finding the faulty relay in the relay-based interlocking system.

ЗАСТОСУВАННЯ ГЛИБОКИХ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ КЛАСИФІКАЦІЇ МУЛЬТИМОДАЛЬНИХ ДАНИХ

Multimodal data analysis is gaining attention in recent research. Pu Liang et al. (2023) provide a comprehensive overview on multimodal machine learning, highlighting its founda-tions, challenges and achievements in recent years. More problem-oriented works propose new methods and applications for multimodal ML, such a Ngiam et al. (2011) propose to use joint audio and video data to improve speech recognition accuracy; Sun, Wand and Li (2018) describe application of multimodal classification for breast cancer prognosis prediction; Mao et al. (2014) propose an architecture of multimodal recurrent network to generate text de-scription of images and so on. However, such works usually focus on the task itself and meth-ods therein, and not on integrating multimodal data processing into other software systems. The goal of this research is to propose a way to conduct multimodal data processing, specifically as a part of a digital twin systems, thus efficiency and near-real-time operation are required. The paper presents an approach to conduct parallel multimodal data classification, adapting to available computing power. The method is modular and scalable and intended for in digital twin application as a part of analysis and modeling tools. Later, the detailed example of such a software module is discussed. It uses multimodal data from open datasets to detect and classify the behavior of pets using deep learning mod-els. Videos are processed using two artificial neural networks: YOLOv3 object detection net-work to process individual frames of the video and a relatively simple convolutional network to classify sounds based on their frequency spectra. Constructed module uses a producer-consumer parallel processing pattern and allows processing 5 frames per second of a video on available hardware, which can be sufficiently improved by using GPU acceleration or more paralleled processing threads.

Digital twins for smart building at the facility management stage: a systematic review of enablers, applications and challenges

PurposeDespite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.Design/methodology/approachA systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.FindingsThe study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.Practical implicationsThe findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.Originality/valueThis study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

Development and implementation of unmanned vehicles through artificial intelligence involving communication system with sensors and control parameters

Physical situations that are duplicated in virtual settings are what the term "digital twin" refers to. Utilizing the Internet of Things and sensors to improve microgrid operation, the Microgrid Digital Twin (MGDT) exhibits bidirectional data sharing and sophisticated communication characteristics. Smart grid functionality is enhanced, limits are set in real-time, and data is securely stored by MGDT. It directs the planning and execution phases of microgrid operations. Concurrently, industry and transportation innovation is propelled by the Industrial Revolution. AI-driven ground-dependent controls, communication systems, and sensors are essential for breakthroughs in unmanned vehicle safety. This abstract clarifies how important digital twins more specifically, MGDTs are to system operations optimization. In addition to highlighting technological advancement and the use of AI to ensure the safety of unmanned vehicles, it provides insights into the potentially revolutionary applications of digital twins across a range of industries.

Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities

Digital twins provide insights into physical objects by serving as advanced virtual representations. Their sensors capture detailed information about an object’s functionality through their use of various sensors. It is possible to gain a deep understanding of the object’s performance and potential areas for improvement by collecting data, which includes metrics such as energy output, temperature, and weather conditions. Digital twins are becoming important in a variety of research and industrial application sectors as production lines and processes become more digitalized and as improved data analysis techniques such as machine learning and enhanced visualization techniques are used. There is no unified definition of the digital twin concept in scientific literature, which results in imprecise applications and the weakening of its terminology. However, this study demonstrates how digital twin models can be applied to urban drainage systems. As a result, this highlights the relatively novel use of digital twins within the field of urban water system engineering. Our review of the language, practices, and directions in smart stormwater management provides a framework to organize and comprehend the current research landscape while highlighting crucial areas for future research. Our results demonstrate that there is near-unanimous agreement within the literature that smart technology has been, or will be, advantageous for stormwater management. However, while some progress has been made in terms of quantity management, maturity in water quality management has not yet been achieved. This study examines the scientific literature on digital twins in the application of artificial intelligence for smart city stormwater infrastructure systems, specifically focusing on urban drainage systems. A demonstration of the workflow and features of current digital twin applications in urban drainage systems is also presented, providing valuable insights and guidance for future research and development in this field.

Practical Application of Digital Twins for Transportation Asset Data Management: Case Example of a Safety Hardware Asset

Digital Twins have emerged as a transformative technology with immense potential for enhancing asset data management in various industries. The transportation industry relies heavily on properly functioning and maintaining safety hardware assets, such as crash barriers, guiderails, and road signs, among others, to ensure the safety of road users. However, traditional asset data management practices for ancillary assets often fall short of providing real-time, comprehensive insights into the condition and performance of these critical assets. Conversely, Digital Twins can analyze data for predictive and preventive maintenance using cutting-edge technologies. This will enable asset managers to proactively address potential safety concerns, plan maintenance schedules efficiently, and optimize resource allocation. This can be achieved by leveraging historical asset data and real-time condition updates and providing the Digital Twin with an information-rich analytical model. The key steps to creating an information-rich analytical model are identifying the asset data that should be collected, understanding data architecture throughout the asset lifecycle, and generating asset data governance and stewardship frameworks. As such, this paper investigates the current state of practice for ancillary asset data collection and management and investigates the generation of information-rich analytical models for guiderails, a critical roadway safety hardware. Moreover, this study demonstrates how Digital Twins can be integrated and affect the development of transportation asset management systems. The findings of this article emphasize the significance of adopting Digital Twins in transportation asset data management. The case example showcases a compelling roadmap for other transportation agencies to consider integrating digital twins into their asset management strategies.

Proxy re-encryption with plaintext checkable encryption for integrating digital twins into IIoT

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT.

Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities

Abstract The rapid growth of outdoor digital twin data sets and advancements in 3D data acquisition technology have sparked interest in improving segmentation performance using deep learning. This research aims to analyze and evaluate different sampling strategies and optimization techniques while exploring the intensity information of outdoor point cloud data. Two sampling strategies, random and stratified sampling, are employed to divide a limited data set. Additionally, the data set is divided into point cloud data with and without intensity. The PointNet++ model is used to segment the point cloud data into two classes, vegetation and structure. The results indicate that stratified sampling outperforms random sampling, yielding a considerable improvement in mean intersection over union scores of up to 10%. Interestingly, the inclusion of intensity information in the data set does not universally enhance performance. Although the use of intensity improves the performance of random sampling, it does not benefit stratified sampling. This research provides insights into the effectiveness of different sampling strategies for outdoor point cloud data segmentation. The findings can contribute to the development of optimized approaches to improving segmentation accuracy in outdoor digital twin applications using deep learning techniques.