Digital Twin Research Articles

A fast scene geometric modeling approach for digital twins combining neural rendering and model retrieval

ABSTRACT Digital twin technology is a crucial driving technology for the realization of Industry 4.0, which enables simulation, analysis, and prediction by constructing geometric scenes that correspond to physical scenes. Current automation in scene modeling is limited, relying heavily on costly and inefficient manual operations. This hinders digital twin technology progress. This paper proposes a framework for a fast scene geometric modeling method that combines neural rendering and model retrieval for digital twins. Neural rendering techniques first train the collected image data, and the point cloud data of physical entities are rendered. Then based on the semantic mapping algorithm between the point cloud data of physical entities and the multi-view image data of 3D CAD models proposed in this study, the corresponding geometric models are retrieved from the geometric asset library by inputting the point cloud data, and all the retrieved geometric models are embedded in the geometric scene to complete the scene geometric modeling. Finally, in a case study of the digital twin-based scene construction for decommissioned lithium battery dismantling, the effectiveness of this method is demonstrated, which can improve the speed and reduce the cost of scene geometric modeling.

Digital Twins in Agriculture: Orchestration and Applications.

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

Bridging Specified States With Stochastic Behavioral-Consistent Vehicle Trajectories for Enhanced Digital Twin Simulation Realism

Bridging Specified States With Stochastic Behavioral-Consistent Vehicle Trajectories for Enhanced Digital Twin Simulation Realism

Digital twin publications in construction (2017–2023): a bibliometrics-based visualization analysis

Digital twin publications in construction (2017–2023): a bibliometrics-based visualization analysis

Digital Twins in Agriculture and Forestry: A Review

Digital Twins in Agriculture and Forestry: A Review

Revolutionizing Combat Casualty Care: The Power of Digital Twins in Optimizing Casualty Care Through Passive Data Collection.

The potential impact of large-scale combat operations and multidomain operations against peer adversaries poses significant challenges to the Military Health System including large volumes of critically ill and injured casualties, prolonged care times in austere care contexts, limited movement, contested logistics, and denied communications. These challenges contribute to the probability of higher casualty mortality and risk that casualty care hinders commanders' forward momentum or opportunities for overmatch on the battlefield. Novel technical solutions and associated concepts of operation that fundamentally change the delivery of casualty care are necessary to achieve desired medical outcomes that include maximizing Warfighter battle-readiness, minimizing return-to-duty time, optimizing medical evacuation that clears casualties from the battlefield while minimizing casualty morbidity and mortality, and minimizing resource consumption across the care continuum. These novel solutions promise to "automate" certain aspects of casualty care at the level of the individual caregiver and the system level, to unburden our limited number of providers to do more and make better (data-driven) decisions. In this commentary, we describe concepts of casualty digital twins-virtual representations of a casualty's physical journey through the roles of care-and how they, combined with passive data collection about casualty status, caregiver actions, and real-time resource use, can lead to human-machine teaming and increasing automation of casualty care across the care continuum while maintaining or improving outcomes. Our path to combat casualty care automation starts with mapping and modeling the context of casualty care in realistic environments through passive data collection of large amounts of unstructured data to inform machine learning models. These context-aware models will be matched with patient physiology models to create casualty digital twins that better predict casualty needs and resources required and ultimately inform and accelerate decision-making across the continuum of care. We will draw from the experience of the automotive industry as an exemplar for achieving automation in health care and inculcate automation as a mechanism for optimizing the casualty care survival chain.

Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning

Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning

Research on deep learning rolling bearing fault diagnosis driven by high-fidelity digital twins

Research on deep learning rolling bearing fault diagnosis driven by high-fidelity digital twins

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination

BackgroundIntegration of a patient’s non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019–10–07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013–11–12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient (ICC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${ICC}$$\\end{document}) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error (χ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\chi}}}^{{2}}$$\\end{document}) of LV myocardial strain, strain rate, and cavity volume.ResultsA subset of 270 parameters remained after MSM which produced high-quality DTs of all patients (χ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\chi}}}^{{2}}$$\\end{document} < 1.6), but minimum parameter reproducibility was poor (ICCmin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{ICC}}_{{min}}$$\\end{document} = 0.01). Iterative reduction yielded a reproducible (ICCmin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{ICC}}_{{min}}$$\\end{document} = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs (χ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\chi}}}^{{2}}$$\\end{document} < 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p < 0.05).ConclusionsBy applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work distribution was representative for the patient’s underlying disease substrate. This DT technology enables patient-specific substrate characterization and can potentially be used to support clinical decision making.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Social Learning with Actor–Critic for dynamic grasping of underwater robots via digital twins

Dynamic grasping is a crucial technology in the field of underwater robotics, playing an essential role in executing complex tasks. However, traditional control methods encounter challenges in achieving an efficient and stable dynamic grasping process due to the complexities and uncertainties of underwater environments. This paper introduces a novel black-box intelligent optimization algorithm named Social Learning with Actor–Critic (SLAC) for the dynamic grasping of underwater robots. The core architecture of SLAC is based on the integration of two key algorithms: Intelligent Social Learning (ISL) for intelligent optimization and Soft Actor–Critic (SAC) for reinforcement learning. ISL enhances SAC by supplying a larger number of transitions, whereas SAC improves ISL with more effective strategies. These algorithms interact synergistically, augmenting their respective strengths throughout the learning process. To evaluate SLAC’s performance, a comparison is made with six state-of-the-art methods across eight continuous control benchmark cases. The results highlight SLAC’s exceptional learning capability and performance benefits. Furthermore, virtual reality software for the underwater robot and a corresponding digital twin system have been developed. The SLAC algorithm is trained in the digital twin environment before its application in the actual underwater setting. Through interactive training and iterative learning, both simulated and experimental results demonstrate the robot’s proficiency in achieving efficient and stable dynamic grasping, effectively adapting to various underwater environments’ variations and complexities.

Current status and future challenges of digital twins for structural health monitoring in civil infrastructures

Abstract Structural Health Monitoring (SHM) ensures the reliability, effectiveness, and performance of buildings and other structures. The introduction of digital twin technology in the last several decades has changed the field of SHM, allowing for real-time monitoring, analysis, and prediction of structural behavior. This research looks in depth at the usage of digital twins in the structural health monitoring of civil infrastructure developments. It analyzes the main features, benefits, challenges, and recent advancements in this rapidly increasing area. The review also discusses case studies and practical applications of digital twins for tracking and managing the health of civil infrastructures, providing insights into their potential for improving structural performance, lowering maintenance costs, and increasing the resilience of our built environment. This review paper provides a comprehensive overview of the usage of digital twins in critical infrastructure project structural health monitoring. It's an excellent tool for academics, scientists, and practitioners interested in utilizing digital twin technologies to improve our built environment's safety, performance, and resilience. This study aims to promote more significant research and innovation in developing and deploying digital twins for structural health monitoring by stressing the benefits, limits, and current advances in this field.&amp;#xD;

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

A digital twin model of the axial temperature field of a DC cable for millisecond calculations

The most important features of digital twin technology for transmission and transformation equipment are symbiotic evolution and state prediction. In constructing the digital twin model for DC cables, the transient and steady-state temperature fields need to be considered comprehensively in the modeling process to consider the multi-physical field coupling, mathematical model, operational data, and environmental conditions in specific cases, which makes it difficult to satisfy the real-time requirements of the digital twin. To solve this problem, in this paper, a digital twin technology architecture for DC cables is proposed, and a neural network learning model is constructed to minimize the latency due to the complexity of the model simulation. Moreover, an efficient digital twin model that is based on the model-driven approach is developed for the fast computation of the axial temperature field of DC cables. The use of the Sequence-to-Sequence residual neural network algorithm for the reduced-order multi-physics field coupling modeling reduces the computational time and ensures as much accuracy as possible up to the finite element model simulation. The simulation results show that the use of the digital twin model reduces the simulation time from 275 s to 0.56 s, and the relative error between the accuracy of the digital twin model and the finite element model for the axial temperature field of the DC cable is 0.46 %. Finally, verification of the reasonableness and feasibility of the proposed method is carried out via an experimental platform according to the standard of CIGRE TB496. In particular, the maximum value of the DC cable temperature is verified to have a small error compared with the result of the Sequence-to-Sequence calculation under specific working conditions.

A Self‐Powered Dual Ratchet Angle Sensing System for Digital Twins and Smart Healthcare

AbstractIn the swiftly progressing landscape of wearable electronics and the Internet of Things (IoTs), there is a burgeoning demand for devices that are lightweight, cost‐effective, and self‐powered. In this study, a self‐powered bidirectional knee joint motion monitoring system is introduced, leveraging a dual ratchet sensing (DRS) system fabricated using 3D printing technology. This approach offers substantial economic and portability benefits. The DRS system is engineered to harness the negative work generated from knee joint movements to power commercial electronic devices, obviating the need for additional metabolic energy from the human body. By synergizing the DRS with virtual reality technology, it becomes feasible to monitor knee joint movements in real‐time with remarkable accuracy, presenting a novel avenue for the integration of digital twin technology. Through the amalgamation of convolutional neural network machine learning algorithms with Bayesian optimization techniques, the DRS system can discern up to 97% of knee joint movements, paving the way for innovative applications in smart rehabilitation and healthcare.

Reconciling spatiotemporal conjunction with digital twin for sequential travel time prediction and intelligent routing

Reconciling spatiotemporal conjunction with digital twin for sequential travel time prediction and intelligent routing

Analysis of Digital Twins in the Construction Industry: Practical Applications, Purpose, and Parallel with other Industries

Digital twins (DTs) have become a widely discussed subject, believed to have the potential to solve various problems across different industries, including Engineering &amp; Construction (E&amp;C). However, there is still significant misconception concerning the definition of DTs and their purpose within E&amp;C. This study dives deep into identifying DT applications within E&amp;C and the other prominent industries, i.e., Aerospace &amp; Aviation, Manufacturing, Energy &amp; Utilities, Automotive, Healthcare, Smart Cities, Oil &amp; Gas, and Retail. The main challenges to the evolution of DT practical applications are also analyzed. A combination of a literature review, multi-case study analysis, and comparative analysis compose the deployed methodology. Standardization and a maturity level classification are proposed to drive progress of the adoption of DTs. The distinct aspects of the different industries and their assets are evaluated to the conclusion that DTs are better employed for maintenance of structures within E&amp;C. DTs have become a well-worn topic, but the abundance of complex theoretical frameworks is met with simple or inexistent practical applications. Therefore, the novelty of this study lays in its comprehensive analysis of DT applications and real-world implementations—a departure from the often-theoretical discussions surrounding DTs.

Application of Digital Twin in Electric Vehicle Powertrain: A Review

Digital Twin (DT) is widely regarded as a highly promising technology with the potential to revolutionize various industries, making it a key trend in the Industry 4.0 era. In a cost-effective and risk-free setting, digital twins facilitate the interaction and merging of the physical and informational realms. The application of digital twins spans across different sectors, including aerospace, healthcare, smart manufacturing, and smart cities. As electric vehicles have experienced rapid growth, there is a growing demand for the development of innovative technologies. One potential area for digital twins application is within the automotive sector. The powertrain system of electric vehicles (EVs) consists of three parts, power source, power electronic system, and electric motor, which are considered as the core components of electric vehicles. The focus of this paper is to conduct a methodical review regarding the use of digital twins in the powertrain of electric vehicles (EVs). While reviewing the development of digital twin technology, its main application scenarios and its use in electric vehicle powertrains are analysed. Finally, the digital twins currently encounter several challenges that need to be addressed, and so the future development of their application to electric vehicles are summarized.

Digital twins in aircraft production and MRO: challenges and opportunities

AbstractThe digital twin (DT) concept, value-adding connecting the real and digital world, has been a rising trend in recent years, while the implementation and observation of challenges are still subject to research. Implementations of holistic Digital Twins of tangible and intangible assets of complex products or processes are often ideal-theoretic; instead, only subsystems and processes are replicated, which digital representations serve specific, meaningful applications. Specifically, with its distinct characteristics, the aviation industry and its production show various future application scenarios, which we use case-driven outline in this work. Therefore, we first summarize common, industry-neutral challenges of implementing Digital Twins and give an overview of aircraft production characteristics. Then, we will outline different fields of utilizing the Digital Twin concept and highlight integrational, organizational, and compliance-related challenges as well as opportunities in the context of aircraft production and Maintenance, Repair, and Overhaul (MRO). The use cases are located at different aircraft life cycle phases, from production system development, production supplying logistics, and Quality Assurance (QA) up to retrofit.