Concept Of Digital Twin Research Articles

Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.

The concept of digital twins, widely adopted in industry, is entering health care. However, there is a lack of consensus on what constitutes the digital twin of a patient. The objective of this scoping review was to analyze definitions and characteristics of patient digital twins being developed for clinical use, as reported in the scientific literature. We searched PubMed, Scopus, Embase, IEEE, and Google Scholar for studies claiming digital twin development or evaluation until August 2023. Data on definitions, characteristics, and development phase were extracted. Unsupervised classification of claimed digital twins was performed. We identified 86 papers representing 80 unique claimed digital twins, with 98% (78/80) in preclinical phases. Among the 55 papers defining "digital twin," 76% (42/55) described a digital replica, 42% (23/55) mentioned real-time updates, 24% (13/55) emphasized patient specificity, and 15% (8/55) included 2-way communication. Among claimed digital twins, 60% (48/80) represented specific organs (primarily heart: 15/48, 31%; bones or joints: 10/48, 21%; lung: 6/48, 12%; and arteries: 5/48, 10%); 14% (11/80) embodied biological systems such as the immune system; and 26% (21/80) corresponded to other products (prediction models, etc). The patient data used to develop and run the claimed digital twins encompassed medical imaging examinations (35/80, 44% of publications), clinical notes (15/80, 19% of publications), laboratory test results (13/80, 16% of publications), wearable device data (12/80, 15% of publications), and other modalities (32/80, 40% of publications). Regarding data flow between patients and their virtual counterparts, 16% (13/80) claimed that digital twins involved no flow from patient to digital twin, 73% (58/80) used 1-way flow from patient to digital twin, and 11% (9/80) enabled 2-way data flow between patient and digital twin. Based on these characteristics, unsupervised classification revealed 3 clusters: simulation patient digital twins in 54% (43/80) of publications, monitoring patient digital twins in 28% (22/80) of publications, and research-oriented models unlinked to specific patients in 19% (15/80) of publications. Simulation patient digital twins used computational modeling for personalized predictions and therapy evaluations, mostly for one-time assessments, and monitoring digital twins harnessed aggregated patient data for continuous risk or outcome forecasting and care optimization. We propose defining a patient digital twin as "a viewable digital replica of a patient, organ, or biological system that contains multidimensional, patient-specific information and informs decisions" and to distinguish simulation and monitoring digital twins. These proposed definitions and subtypes offer a framework to guide research into realizing the potential of these personalized, integrative technologies to advance clinical care.

Construction Method of a Digital-Twin Simulation System for SCARA Robots Based on Modular Communication

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot’s operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system.

Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes

The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems. Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex geometries. These processes are undergoing a major transformation with the advent of digital technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques for the digitalization of AM processes within a digital twin concept. The main contribution of this study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process data handling, optimization/control, and their integration into the real-time assessment of AM processes. Therefore, a novel combination of efficient data-solver technology and an architecturally designed neural network (NN) module is developed for transient manufacturing processes with high heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process.

Technical Feasibility Analysis of Digital Twin Technology in the Field of Urban Disaster Prevention

Abstract. In recent years, disasters have occurred frequently around the world, causing great harm to national finances and people's safety. There is still a lack of research on digital disaster prevention technology, but with the development of technologies such as artificial intelligence and computer vision, it has gradually become possible to use digital twin technology in urban disaster prevention. This paper is mainly based on digital twin related technologies, briefly introduces the concept and importance of digital twins, and the applicable fields, and then discusses the mode of data interaction between real entities and twins and the feasibility of digital disaster prevention, so as to prevent and simulate urban disasters and derive strategies for dealing with disasters. This paper reviews the existing research on digital twin technology through the method of literature retrieval and analysis. By analyzing technologies such as the Internet of Things, sensors, three-dimensional laser technology, urban monitoring technology, satellite remote sensing technology, drone remote sensing technology, mobile device technology, lidar technology, and oblique photography, a systematic review of critical analysis is obtained, and a macro method of data interaction and modeling for digital twin technology for disaster prevention is obtained, thereby deriving the feasibility of disaster prevention based on digital twin systems.

Review of Digital Twins enabled applications for demand response

Digital Twins (DT), as one of the emerging trends in digitalisation, has attracted a large amount of research in the past few years due to its ability to provide more accurate models and better predictions for various energy-related applications. In the context of demand response (DR), the real-time bidirectional communication characteristic of DT allows for functions such as real-time energy forecast and decision making aid, which provided high potential in DR applications due to the nature of DR being highly time sensitive. The objective of this paper is to explore the concept and application of DT in the domain of DR, as there are currently limited numbers of review on this topic. It was identified that most work in this area shows promising results but is still exploratory. The main application of DT in the domain of DR is using DT as an enabling tool by leveraging the real-time and data processing function of DT for monitoring and decision-making, and providing a medium for better visualisation. Future studies can experiment with applying DT frameworks to larger case studies, and apply more elaborate DT with higher maturity levels.

A sensing system and solving method for dynamic detection of relative pose of hydraulic support group

The precise detection of the pose of hydraulic support groups is crucial for the construction of intelligent coal mines. Addressing current challenges in comprehensive position sensing of support groups, such as one-sided descriptions, missing information, and limited detection capabilities, this paper, based on the concept of digital twin, proposes a sensing system and solving method for dynamic detection of the relative pose of hydraulic support group. Initially, a relative pose detection model, based on the six-point localization principle, is proposed by combining existing sensor information. Then, a sensing system is designed, incorporating a photoelectric detection device for monitoring key position parameters and virtual sensors for determining pose information. Consequently, a method for dynamically deriving relative positions, driven by the fusion of real-virtual iterative solving, real-virtual error feedback, and iterative condition optimization, is established. Finally, a relative pose dynamic detection system for a hydraulic support prototype is constructed in a laboratory environment. Experimental results demonstrate that the sensor system and the relative position detection method designed in this paper achieve a relative position accuracy of over 95.32 % for the hydraulic support, with a dynamic reconstruction time of less than 0.2 s. This verifies the rationality of the sensor system and the feasibility and accuracy of the dynamic deduction method. To sum up, this work provides a theoretical foundation and technical support for the autonomous perception of hydraulic support group, autonomous frame adjustment between frames, and overall straightness adjustment. It also lays the groundwork for further research and the advancement of information-physical systems in the fully-mechanized mining face.

Complexity data science: A spin-off from digital twins.

Digital twins offer a new and exciting framework that has recently attracted significant interest in fields such as oncology, immunology, and cardiology. The basic idea of a digital twin is to combine simulation and learning to create a virtual model of a physical object. In this paper, we explore how the concept of digital twins can be generalized into a broader, overarching field. From a theoretical standpoint, this generalization is achieved by recognizing that the duality of a digital twin fundamentally connects complexity science with data science, leading to the emergence of complexity data science as a synthesis of the two. We examine the broader implications of this field, including its historical roots, challenges, and opportunities.

The e-COL+ Project, an Opportunity to Reflect on the Concept of Digital Twin

Abstract The mass digitization efforts associated with the e-COL+ project, focusing on French natural history collections, provide an invaluable opportunity to reassess the need to build a comprehensive 3D digital collection to complement existing 2D digital repositories. This article explores the importance of incorporating 3D data into digital collections, highlighting its significant impact on scientific research, conservation efforts, and public outreach campaigns. Although the e-COL+ project faces practical obstacles related to digitization, it also raises important theoretical questions about the complex interplay between physical and digital objects. Here, we challenge the concepts of digital clone and digital twin that are commonly used in the construction of digital collections. The digital twin concept more accurately captures the intricate and adaptive connection between physical and digital entities that results from the precise digitization techniques used and the evolving characteristics of the specimens found in natural history collections.

Application of Digital Twins in Designing Safety Systems for Robotic Stations

The aim of this paper is to present examples and original solutions related to the application of the digital twin concept in designing safety systems for robotic stations. This paper includes a review of publications on robot safety systems and digital twins. Based on this review, it was concluded that further work in this area is justified. This paper demonstrates the use of a digital model of a robotic casting mold preparation station to design safety components for an industrial cell. A key element of this paper is the presentation of developed algorithms and their applications in building digital twins of existing robotic stations. By characterizing advanced safety systems used in robotic stations, an example of using a digital twin of a robotic station to create safety zones and so-called restricted zones for the robot was developed. As part of the research conducted, a real, comprehensive example of creating safety zones based on the robot’s TCP paths was carried out.

3D Remote Monitoring and Diagnosis during a Pandemic: Holoportation and Digital Twin Requirements

COVID-19 regulations presented clinicians with a new set of challenges that affected their ability to efficiently provide patient care and, as a result, telemedicine was rapidly adopted to deliver care remotely. However, these telemedicine platforms undermine patient care due to clinicians' inability to acquire all the relevant patient information required to diagnose and treat the patient. To explore this gap, we conducted a requirements analysis for the development of a 3D remote patient monitoring and diagnosis platform, by using a user-centric design methodology. In this requirements analysis, we elicited information about the clinical domain, identified clinicians’ requirements, elicited clinicians’ insights, and documented the clinicians' requirements. The outcome was the emergence of refined clinicians' requirements to guide the implementation of the Digital Twin concept paired with holoportation for remote 3D monitoring and diagnosis of patients. We anticipate that the application of a 3D telemedicine platform with these requirements for patient care during a pandemic could potentially enhance clinicians' efficiency and the effectiveness of remote patient care.

Open-Source Solutions for Real-Time 3D Geospatial Web Integration

Abstract. The recent development of Urban Digital Twin (UDT) structures and the spread of Internet of Things (IoT) technology have extended the concept of Digital Twin (DT) to urban environment representation, enabling real-time connections between geospatial representations and their real-world counterparts. Concurrently, advancements in surveying technologies and web geospatial services have facilitated the acquisition of extensive 2D and 3D geospatial data, making their spatial integration essential. While some proprietary software solutions have recently enabled initial examples of UDT by integrating various 3D geospatial data and real-time sensor acquisition, the development of open-source solutions for real-time web-based geospatial management remains a challenge. This work demonstrates a possible open-source solution for developing a UDT accessible via the web. The case study involves the web visualization platform of the new Faculty of ITC building at the University of Twente in Enschede (The Netherlands), which is connected in real-time with weather data services provided by Visual Crossing (1). The platform is based on an HTML5 framework and employs WebGL JavaScript graphic libraries to visualize different modules of 3D web visualization that integrate various local and remote 3D datasets. The framework adopted in this experiment can be used in the future for developing low-cost UDT web platforms beneficial for researchers, municipalities, and private companies interested in the real-time geospatial management of urban environments.

3D Data Integration in the Voxel Domain

Abstract. The interest in 3D data integration is growing as the concept of Spatial Digital Twins gains traction among local governments and stakeholders. Spatial data is typically organized into standardised themes such as buildings, transport, and vegetation, or as ad hoc data products in either 2D or 3D dimensions. However, integrating these data sets into a cohesive 3D model presents several challenges. The foundational data sets may vary in terms of accuracy, resolution, representation, and actuality due to differences in creation standards and procedures. Consequently, inconsistencies often arise within the 3D models, making them difficult to identify and rectify. Many of these data integration issues can be traced back to the 3D vector representation. Vector 3D models exhibit significant geometric diversity, which complicates the computational operations required for examining and validating the models. In this paper, we propose a novel voxel-based approach for integrating 3D data. Voxels, analogous to pixels in 2D raster images, inherit several key properties. We employ a dedicated voxel data structure and a set of operations tailored for this purpose. First, we voxelise vector datasets, converting continuous geometric information into discrete voxel representations. Next, we merge voxel layers using a specialized voxel overlay operation. The resulting 3D model adheres to most validity and integrity requirements. To highlight the flexibility of our voxel data structure, we transformed it into point clouds for visualisation in ArcGIS Pro, enabling the addition of 3D base maps and GIS layers. We then published these datasets to Scene Services, granting public access. This allows users to easily explore the 3D scenes through a web browser, making complex geographic data more accessible. By leveraging voxels, our approach facilitates efficient and accurate 3D data integration, making it a valuable contribution to the field.

A LiDAR-Based Digital Twinning Workflow for Traffic Monitoring and Simulation

Abstract. The process of ensuring efficient and safe urban transportation is closely linked to urban planning, particularly through the aspects of transportation planning. Transportation planning is a pivotal concern for urban regions worldwide, reflecting the growing need to increase mobility while ensuring safety and sustainability in densely populated areas. This research focuses on developing a novel digital-twin-based approach for micro-traffic simulation to support data-driven decision-making for increasing traffic safety through scenario planning. Leveraging the traffic data obtained through monitoring one of the busiest intersections in Sofia city, this research workflow shows the effective integration of LiDAR data and the urban digital twin concept in intelligent transportation systems (ITS). The research addresses problems related to moving object classification, trajectory analysis, and reclassification of unrecognised objects by processing the LiDAR data, pre-processed in a .osef format, thereby transforming it to make it suitable for simulation. The proposed solution for the monitoring of urban traffic is demonstrated by the usage of SUMO (Simulation of Urban MObility) for performing simulations and a Random Forest model for unrecognized object reclassification to pre-existing vehicles and pedestrian classes. The architecture of the proposed workflow can possibly be applied in other similar urban settings, providing a scalable solution for both traffic management and urban planning. The study’s results support the wider use of urban digital twin principles in ITS by highlighting the value of advanced modelling tools and high-quality data in addressing today's urban transportation challenges.

Digital twins for electric propulsion technologies

As the space industry is undergoing an evolution, the current approaches toward design, development, and qualification of Electric Propulsion (EP) systems largely based on empirical “trial-and-error” methodologies are falling short of addressing the emerging needs and keeping abreast of the rapid changes in market trends. Furthermore, with the proliferation of Artificial Intelligence (AI) within the space industry toward next-generation autonomous satellites and spacecrafts, the conventional EP monitoring and control strategies become inadequate and need to give way to approaches compatible with satellite-level autonomy requirements. A digital twin (DT) – a technology capable of providing an accurate dynamically adapting virtual representation of a physical asset – is a game-changing concept that catalyzes the transcendence of the EP industry past its pressing challenges today. In this paper, we aim to: (i) define the DT concept, highlighting how it surpasses traditional modelling, (ii) enumerate the DT’s breakthrough promises for the EP industry, and (iii) specify the challenges to realize practical and scalable EP DTs. Additionally, we report on the technical progress achieved and/or planned at Imperial Plasma Propulsion Laboratory to fill the foundational gaps in three building block elements of DTs, namely, (i) a cost-effective kinetic model to generate extensive high-fidelity databases for machine learning (ML), (ii) ML-enabled models for prediction and analysis of performance and operational behavior, and (iii) a DT architecture that integrates the numerical models in terms of a computing infrastructure and provides data pipelines and interfaces for the DT’s data exchanges with the real world, its dynamic updating, and uncertainty quantification.

Building Biodiversity Digital Twins

A digital twin is a replica of a system, process, or object realized as a digital version. Digital twins are widespread in engineering and aim to can help many kinds of organizations simulate real situations and their outcomes, ultimately allowing them to make better decisions. Digital twins combine cutting-edge modelling approaches with powers of supercomputers with data streams. These data resources, coming through the research infrastructures, depend on data standardization, efficient data models and workflows, and on FAIRness (Findability, Accessibility, Interoperability, Reusability) of these elements. Digital twin concepts have been applied to biodiversity data since 2022, most notably through the Biodiversity Digital Twin project. The outcomes of dynamic modeling based on the biodiversity data (which availability changes in volumes and quality) against the globally available changing environmental and human impact data are expected to form the basis for management of natural resources and aims to support biodiversity recovery. The BioDT consortium brings together a dynamic team of experts in biodiversity data and standards, high performance computing, and FAIR data to realize the first set of biodiversity Digital Twin prototypes. Biodiversity evidence generated, standardized and shared from collections, systematic and long term surveys, eDNA and metabaroding, citizen science and other communities representing a range of biodiversity evidence types, can to be applied in the dynamic and interactive modelling solutions, known as digital tiwins. The Biodiversity Digital Twin project (and the twin approach in general) offers advanced simulation and prediction models to tackle global biodiversity challenges. This project is designed to address biodiversity dynamics through a set of use cases, providing insights and tools for conservation and restoration efforts. The BioDT project has revealed similarities and differences of applying Biodiversity Information Standards (TDWG) thinking and principles of data standardization to the communities with different levels of familiarity with standards and FAIR principles. The presentation at SPNCH-TDWG 2024 covered the process of building biodiversity digital twins by the second project year and exposed the work along the gradient of data intensity, of data FAIRness, and of complience to the international data standards. The details of ten BioDT project stories are covered in the special issue of the Pensoft RIO journal on BioDT project (Afsar et al. 2024, Chala et al. 2024, Frøslev et al. 2024, Ingenloff et al. 2024, Groeneveld et al. 2024, Khan et al. 2024, Mikryukov et al. 2024, Ovaskainen et al. 2024, Rolph et al. 2024, Taubert et al. 2024).

The Concept of Building an Integrated Digital Ecosystem to Improve the Quality of Products and Services of Defense Industry Enterprises

Research and development of systems for managing material, technical, and intellectual resources are key directions for improving manufacturing processes in the modern world. In recent years, the question of integrating various management systems has become particularly relevant due to the widespread adoption of quality management systems and their certification to meet current regulatory requirements. This study attempts to merge the advantages of using product lifecycle information support technologies, such as data and information management, with the concept of digital twins, including virtual design, digital product modeling, and real-time analysis. The analysis covers the constituent elements of the generalized product design and manufacturing process, addressing the construction of an integrated digital ecosystem to enhance the quality of products and services in the defense-industrial complex. This ecosystem is based on the integration of technologies for continuous product information support using their digital twins and an integrated quality management system. It is shown that the approach to evaluating the effectiveness of purposeful processes should be complemented by a risk-oriented model, which includes response scenarios to potential adverse effects of the operation of the purposeful organizational-technical system. A structural scheme and formalized description of a generalized product information support model are presented, allowing for the integration of specific quality indicators into a unified strategic outcome. Ultimately, a concept for building an integrated digital ecosystem for defense-industrial enterprises is proposed, aimed at enhancing the overall quality of products and services.

A Digital Twin of a University Campus from an Urban Sustainability Approach: Case Study in Madrid (Spain)

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process.

To the problem of digital immortality

The idea of reproducing personality by means of digital (neural network) technologies (“digital immortality”), together with the concept of Digital Twin (DT), still attracts great attention. Recent advances in the DT industry permit to expect the production of perfect “mirror” DT in the near future. We argue that the “immortality in the memory of other people” could be approached quite closely due to creating an analogue of personal DT by simulating the personality. For this purpose, it is necessary to compose the “constructive portrait” of a chosen person (by extracting the key features and traits of personality) and try to reproduce it by means of a chosen model. We are developing an original model Natural Constructive Cognitive Architecture (NCCA) that inherently provides the interpretation of logical and intuitive thinking, subconscious, etc. This model should be adjusted to specific set of knowledge inherent in a particular person (books, films, photographs, etc.), with an emphasis on personal lexicons (verbal, emotion, behavioral). NCCA contains a large set of free model parameters, which enables us to reproduce a wide range of personality features, from thinking style to temperament. It is shown that popular Generative Pre-trained Transformers (GPTs) have much in common with NCCA and could be adapted and used as an analog of DT of a specific person. We argue that the proposed program would provide the possibility to create an analog of DT, which could give an impression (at least, an illusion) of communication with the desired specific person.

Failure analysis of photovoltaic strings by constructing a digital multi-twin integrating theory, features, and vision

Timely and accurate failure analysis of photovoltaic (PV) systems is crucial forensuring the stable operation of power grids. However, existing failure analysis and diagnosis algorithms based on deep neural networks excessively rely on high-quality failure state data collected by sensors. This is extremely difficult to achieve in real photovoltaic power plants that are commonly equipped with self-protection mechanisms. To address this issue, we propose a Digital Multi-Twin integrating Theory, Features, and Vision (TFV-DMT) for failure analysis of PV strings in PV systems. This method first constructs theoretical simulation twins, feature twins, and visual twins based on the concept of digital twins, specifically tailored for actual PV systems, and designs a multi-twin collaborative model for model updating and failure diagnosis. Secondly, to better construct the visual twin, we introduce a Two-Dimensional Gram Angle Field Transformation Algorithm (TDGAF) to achieve targeted two-dimensional mapping of PV feature data, facilitating a more direct expression of failure characteristics. Furthermore, by constructing a Swish-activated Deep Convolutional Generative Adversarial Network (SDCGAN) to achieve balanced augmentation of mapping data, the model bias of theoretical simulation twins can be reduced. Finally, we propose a Swin-LT network that incorporates a Lightweight Dual-Channel Attention Module (LDAM) to better analyze the features of the visual twin, enabling more precise fault diagnosis. The algorithm has been validated on a real 250 kW grid-connected PV system, with results indicating that the proposed digital twin model is effective, achieving a diagnosis accuracy rate of 98.8 % for string failures.

Potentials of digital twin system for analyzing travel behavior decisions

This review explores the potential of digital twin systems to provide a more holistic representation of travel behavior and support transportation planning and policymaking. The paper introduces the concept of digital twins, their key characteristics, and their applications in various domains, including transportation. It discusses the traditional methods used in travel behavior analysis and their limitations, as well as the potential advantages of digital twin systems, such as the integration of heterogeneous data sources, real-time monitoring and prediction, and the ability to simulate and evaluate various policy scenarios. The review also identifies the key components of digital twin systems, the challenges associated with their implementation, and the current state of research on digital twins and related methods in travel behavior analysis. The paper highlights research gaps and future directions, emphasizing the need for privacy-preserving techniques, real-world case studies, and the integration of digital twins with decision support systems. Finally, the review discusses the broader implications of digital twin systems for transportation planning and policymaking, concluding by emphasizing the need for interdisciplinary collaboration and stakeholder engagement to fully realize the potential of digital twins in analyzing travel behavior decisions and shaping the future of transportation systems.