Search Results (407)

Urban infrastructure construction poses significant risks to surrounding the infrastructure due to ground settlement, structural disturbances, and underground utility disruptions. Traditional risk assessment methods often rely on static models and experience-based decision-making, limiting their ability to adapt to dynamic construction conditions. This study proposes an integrated framework combining digital twin and knowledge graph technologies to enhance real-time risk assessment and emergency response in tunnel construction. The digital twin continuously integrates real-time monitoring data, including settlement measurements, TBM operational parameters, and structural responses, creating a virtual representation of the tunneling environment. Meanwhile, the knowledge graph structures domain knowledge and applies rule-based reasoning to infer potential hazards, detect abnormal conditions, and suggest mitigation strategies. The proposed approach has been successfully applied to a practical tunnel project in China, where it played a crucial role in emergency response and risk mitigation. By integrating real-time monitoring data with the knowledge-driven reasoning system, the developed framework enabled the early identification of anomalies, rapid risk assessment, and the formulation of effective mitigation strategies, preventing further structural impact. This bidirectional interaction between the digital twin and the knowledge graph ensured that the real-world data informed the automated reasoning, while the inference results were visualized within the digital twin for intuitive decision support. The proposed framework not only enhances current risk management practices but also serves as a foundation for future innovations in smart infrastructure and automated emergency response systems. Full article

This study addresses the challenges associated with virtual–real interactions, the limitations of one-dimensional data presentation, restricted real-time functionalities, and the lack of effective models for monitoring production line status. It specifically investigates intelligent production lines for automotive wheels as the focal point of the research. This study explores the construction methodology and the application of intelligent production lines through the utilization of digital twin technology. A hierarchical design approach is employed, integrating industrial Internet of Things (IoT) technology to create a comprehensive digital twin system. This system consists of four layers: the physical production line layer, the data acquisition and processing layer, the digital twin production line layer, and the application service layer. Precise mapping from the physical production line to the digital twin model is achieved using the advanced 3D modeling and simulation software, PQ Factory, while real-time data collection and transmission are facilitated through the standardized OPC UA protocol. The effectiveness of the system is substantiated through a detailed case study. The findings demonstrate that the intelligent production line system, which leverages digital twin technology for automotive wheels, enables real-time monitoring of the production process and provides innovative solutions, along with a robust theoretical framework for comprehensive analysis, diagnosis, evaluation, optimization, prediction, and decision making in the production of automotive wheels. Full article

Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians to simulate disease progression and treatment responses, offering a personalized approach to cancer treatment. Aim: This narrative review aimed to synthesize existing review studies on the application of digital twins in oncology, focusing on their potential benefits, challenges, and ethical considerations. Methods: The narrative review of reviews (NRR) followed a structured selection process using a standardized checklist. Searches were conducted in PubMed and Scopus with a predefined query on digital twins in oncology. Reviews were prioritized based on their synthesis of prior studies, with a focus on digital twins in oncology. Studies were evaluated using quality parameters (clear rationale, research design, methodology, results, conclusions, and conflict disclosure). Only studies with scores above a prefixed threshold and disclosed conflicts of interest were included in the final synthesis; seventeen studies were selected. Results and Discussion: DTs in oncology offer advantages such as enhanced decision-making, optimized treatment regimens, and improved clinical trial design. Moreover, economic forecasts suggest that the integration of digital twins into healthcare systems may significantly reduce treatment costs and drive growth in the precision medicine market. However, challenges include data integration issues, the complexity of biological modeling, and the need for robust computational resources. A comparison to cutting-edge research studies contributes to this direction and confirms also that ethical and legal considerations, particularly concerning AI, data privacy, and accountability, remain significant barriers. Conclusions: The integration of digital twins in oncology holds great promise, but requires careful attention to ethical, legal, and operational challenges. Multidisciplinary efforts, supported by evolving regulatory frameworks like those in the EU, are essential for ensuring responsible and effective implementation to improve patient outcomes. Full article

This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R² up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article

In mobile multi-agent systems (MASs), achieving effective leader–follower coordination under unknown dynamics poses significant challenges. This study proposes a two-stage cooperative strategy that integrates Gaussian Processes (GPs) for modeling and a Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy optimization (GPTD3), aiming to enhance adaptability and multi-objective optimization. Initially, GPs are utilized to model the uncertain dynamics of agents based on sensor data, providing a stable and noiseless training virtual environment for the first phase of TD3 strategy network training. Subsequently, a TD3-based compensation learning mechanism is introduced to reduce consensus errors among multiple agents by incorporating the position state of other agents. Additionally, the approach employs an enhanced dual-layer reward mechanism tailored to different stages of learning, ensuring robustness and improved convergence speed. Experimental results using a differential drive robot simulation demonstrate the superiority of this method over traditional controllers. The integration of the TD3 compensation network further improves the cooperative reward among agents. Full article

This study introduces a novel framework with the use of extended reality (XR) systems in hydrology, particularly focusing on immersive visualization of hydrologic data for enhanced environmental planning and decision making. The study details the shift from traditional 2D data visualization methods in hydrology to more advanced XR technologies, including virtual and augmented reality. Unlike static 2D maps or charts that require cross-referencing disparate data sources, this system consolidates real-time, multivariate datasets, such as streamflow, precipitation, and terrain, into a single interactive, spatially contextualized 3D environment. Immersive information systems facilitate dynamic interaction with real-time hydrological and meteorological datasets for various stakeholders and use cases, and pave the way for metaverse and digital twin systems. This system, accessible via web browsers and XR devices, allows users to navigate a 3D representation of the continental United States. The paper addresses the current limitations in hydrological visualization, methodology, and system architecture while discussing the challenges, limitations, and future directions to extend its applicability to a wider range of environmental management and disaster response scenarios. Future application potential includes climate resilience planning, immersive disaster preparedness training, and public education, where stakeholders can explore scenario-based outcomes within a virtual space to support real-time or anticipatory decision making. Full article

Integrating digital twins (DTs) into smart grid systems within the Internet of Smart Grid Things (IoSGT) ecosystem brings novel opportunities but also security challenges. Specifically, advanced machine learning (ML)-based Deepfake technologies enable adversaries to create highly realistic yet fraudulent DTs, threatening critical infrastructures’ reliability, safety, and integrity. In this paper, we introduce Authenticating Networked Computerized Handling of Representations for Smart Grid security (ANCHOR-Grid), an innovative authentication framework that leverages Electric Network Frequency (ENF) signals as real-world anchors to secure smart grid DTs at the frontier against Deepfake attacks. By capturing distinctive ENF variations from physical grid components and embedding these environmental fingerprints into their digital counterparts, ANCHOR-Grid provides a robust mechanism to ensure the authenticity and trustworthiness of virtual representations. We conducted comprehensive simulations and experiments within a virtual smart grid environment to evaluate ANCHOR-Grid. We crafted both authentic and Deepfake DTs of grid components, with the latter attempting to mimic legitimate behavior but lacking correct ENF signatures. Our results show that ANCHOR-Grid effectively differentiates between authentic and fraudulent DTs, demonstrating its potential as a reliable security layer for smart grid systems operating in the IoSGT ecosystem. In our virtual smart grid simulations, ANCHOR-Grid achieved a detection rate of 99.8% with only 0.2% false positives for Deepfake DTs at a sparse attack rate (1 forged packet per 500 legitimate packets). At a higher attack frequency (1 forged packet per 50 legitimate packets), it maintained a robust 97.5% detection rate with 1.5% false positives. Against replay attacks, it detected 94% of 5 s-old signatures and 98.5% of 120 s-old signatures. Even with 5% injected noise, detection remained at 96.5% (dropping to 88% at 20% noise), and under network latencies from <5 ms to 200 ms, accuracy ranged from 99.9% down to 95%. These results demonstrate ANCHOR-Grid’s high reliability and practical viability for securing smart grid DTs. These findings highlight the importance of integrating real-world environmental data into authentication processes for critical infrastructure and lay the foundation for future research on leveraging physical world cues to secure digital ecosystems. Full article

The digital twin model, which serves as a virtual counterpart symmetric to the physical entity, enables high-fidelity simulation and real-time monitoring. However, digital twin implementation for marine steam turbines (MSTs) faces dual multi-domain simulation fidelity and computational efficiency challenges. This study establishes a MST digital twin modeling methodology through two interconnected innovations: (1) a Modelica-based modular architecture enabling cross-domain coupling across mechanical, thermodynamic, and hydrodynamic systems via hierarchical decomposition, ensuring bidirectional symmetry between physical components and their virtual representations; and (2) a hybrid support vector regression-bidirectional long short-term memory (SVR-BiLSTM) surrogate model combining Gaussian radial basis function-supported SVR for steady-state mapping with Bi-LSTM networks for dynamic error compensation. Experimental validation demonstrates: (a) the SVR component achieves <1.57% absolute error under step-load conditions with 85% computational time reduction versus physics-based models; and (b) Bi-LSTM integration improves transient prediction accuracy by 14.85% in maximum absolute error compared to standalone SVR, effectively resolving static–dynamic discrepancies in telemetry simulation. This dual-approach innovation successfully bridges the critical trade-off between real-time computation and predictive accuracy while maintaining symmetric consistency between the physical turbine and its digital counterpart, providing a validated technical foundation for the intelligent operation and maintenance of MSTs. Full article

Digital twin is a virtual replica of a physical system that updates in real time using sensor data to enable simulations and predictions. For bridges constructed using rotation construction methods, the rotation phase demands continuous monitoring of structural behavior and coordination with surrounding traffic infrastructure. Therefore, a digital twin system for monitoring rotation construction is vital to ensure safety and schedule compliance. This paper explores the application of model-based systems engineering (MBSE), a modern approach that replaces text-based documentation with visual system models, to design a digital twin system for monitoring the rotation construction of a 90 m + 90 m single-tower cable-stayed bridge. A V-model architecture for the digital twin system, based on requirements analysis, functional analysis, logical design, and physical design analysis (RFLP), is proposed. Based on SysML language, the system’s requirements, functions, behaviors, and other aspects are modeled and analyzed using the MBSE approach, converting all textual specifications into the unified visual models. Compared to the traditional document-driven method, MBSE improves design efficiency by reducing ambiguities in system specifications and enabling early detection of design flaws through simulations. The digital twin system allows engineers to predict potential risks during bridge rotation and optimize construction plans before implementation. These advancements demonstrate how MBSE supports proactive problem-solving (forward design) and provides a robust foundation for future model validation and engineering applications. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

To address the key challenges of poor real-time interaction, insufficient integration of operating rules, and limited virtual–physical synergy in current carrier-based aircraft scheduling simulations, this study proposes an immersive digital twin platform that integrates a two-layer genetic algorithm (GA) with hardware-in-the-loop (HIL) semi-physical validation. The platform architecture combines high-fidelity 3D visualization-based modeling (of aircraft, carrier deck, and auxiliary equipment) with real-time data exchange via TCP/IP, establishing a collaborative virtual–physical simulation environment. Three key innovations are presented: (1) a two-tier genetic algorithm (GA)-based scheduling model is proposed to coordinate global planning and dynamic execution optimization for carrier-based aircraft operations; (2) a systematic constraint integration framework incorporating aircraft taxiing dynamics, deck spatial constraints, and safety clearance requirements into the scheduling system, significantly enhancing tactical feasibility compared to conventional approaches that oversimplify multidimensional operational rules; (3) an integrated virtual–physical simulation architecture merging virtual reality interaction with HIL verification, establishing a collaborative digital twin–physical device platform for immersive visualization of full-process operations and dynamic spatiotemporal evolution characterization. Experimental results indicate that this work bridges the gap between theoretical scheduling algorithms and practical naval aviation requirements, offering a standardized testing platform for intelligent carrier-based aircraft operations. Full article

In response to the issues of unreachable targets, local minima, and insufficient real-time performance in drone path planning in urban low-altitude complex scenarios, this paper proposes a fusion algorithm based on digital twin, integrating LSTM (long short-term memory), DQN (Deep Q-Network), and APF (artificial potential field). The algorithm relies on a twin system, integrating multi-sensor fusion technology and Kalman filtering to input obstacle information and UAV trajectory predictions into the DQN, which outputs action decisions for intelligent obstacle avoidance. Additionally, to address the blind search problem in trajectory planning, the algorithm introduces exploration rewards and heuristic reward components, as well as adding velocity and acceleration compensation terms to the attraction and repulsion functions, reducing the path deviation of UAVs during dynamic obstacle avoidance. Finally, to tackle the issues of insufficient training sample size and simulation accuracy, this paper leverages a digital twin platform, utilizing a dual feedback mechanism from virtual and physical environments to generate a large number of complex urban scenario samples. This approach effectively enhances the diversity and accuracy of training samples while significantly reducing the experimental costs of the algorithm. The results demonstrate that the LSTM-DQN-APF algorithm, combined with the digital twin platform, can significantly improve the issues of unreachable goals, local optimality, and real-time performance in UAV operations in complex environments. Compared to traditional algorithms, it notably enhances path planning speed and obstacle avoidance success rates. After thorough training, the proposed improved algorithm can be applied to real-world UAV systems, providing reliable technical support for applications such as smart city inspections and emergency rescue operations. Full article

With rapid urbanization, the utilization of underground space has become an important part of infrastructure. However, the stability of underground spaces such as large caverns remains a key challenge in civil engineering throughout the lifecycle of a project. Traditional methods of stability assessment rely on static models and periodic monitoring and often fail to capture real-time changes in rock behavior, leading to potential safety risks and, in severe cases, even the collapse of underground infrastructure. To address this challenge, this study introduces a digital twin (DT) framework to improve stability assessments and monitor deformations in underground structures. The framework enables the continuous monitoring and adaptive optimization of rock support systems by combining real-time sensor data with virtual simulations. A five-dimensional DT framework comprises physical objects, virtual objects, service systems, DT data, and their interconnections. It incorporates six key modules, which are structure, geology, material, behavior, performance, and environment, to enhance the understanding of cavern stability. The framework is based on Industry Foundation Classes standards to ensure seamless data exchange, interoperability, and the standardized representation of geotechnical and structural data. A seven-step methodology is developed for this framework, encompassing geological assessment, virtual modeling, Building Information Modeling (BIM)-based design, construction processes, real-time monitoring, and optimization strategies. To evaluate its effectiveness, the framework is applied to a case study, demonstrating improvements in deformation monitoring and rock support efficiency. The findings highlight the potential of integrating DT with BIM to enhance safety, reliability, and long-term stability in underground construction projects. Full article

In the application of digital twin technology for the heading workface in coal mining, real-time state data will be transmitted to the remote control platform through a gateway device. This cross-system and cross-software data transmission method inevitably introduces transmission delays, resulting in a certain spatiotemporal discrepancy in the virtual model control for the remote control of the physical equipment. In this paper, by analyzing the operational process of the cantilever roadheader, a state evolution dynamics model construction method for the cantilever roadheader is proposed, which includes three stages, the discretization of the operating state based on the cutting path, event-driven graph construction of the cutting state evolution, and real-time data-driven dynamics evolution, so to continuously monitor, analyze, and adjust the operational dynamics of the cantilever roadheader based on real-time state data, thus improving the efficiency, performance, and adaptability. The construction of the model provides a theoretical basis and technical support for the construction and alignment of the digital twin multidimensional model of the cantilever roadheader. Full article

Digital Twin (DT) technology is emerging as a powerful tool for optimizing energy efficiency and industrial sustainability. By creating virtual replicas of physical systems, DTs enable real-time monitoring, predictive maintenance, and resource optimization, offering new opportunities to meet growing energy demands. Despite its potential, the comprehension of DT technology’s applications, benefits, and challenges remains limited. This systematic review explores the role of Digital Twins in energy efficiency across various industries. A structured literature search was conducted in IEEE Xplore, Elsevier, Springer, MDPI, and Google Scholar, following PRISMA 2020 guidelines. After applying the predefined inclusion criteria, 50 studies were selected for in-depth analysis. The findings highlight that DT implementation can lead to energy savings of up to 30%, reduce operational costs, and improve predictive maintenance strategies. Their impact is particularly notable in smart buildings, manufacturing, and industrial processes, where real-time data analytics contribute to better energy management. However, significant barriers remain, including high implementation costs, data security risks, and the complexity of integrating DTs with existing infrastructures. By synthesizing the current research, this review underscores the transformative potential of Digital Twins while identifying key challenges that need to be addressed for their wider adoption. Future efforts should focus on developing standardized methodologies, reducing implementation costs, and enhancing cybersecurity measures to maximize their benefits in energy efficiency and sustainability. Full article