Search Results (2,030)

Pore structure heterogeneity of coal reservoirs restricts the diffusion-seepage process of coalbed methane, thereby restricting the production capacity of coalbed methane. Therefore, 10 coal samples from the Linxing area are taken as an objective, and high-pressure mercury intrusion testing was used to describe the pore structure distribution of all the coal samples. On this basis, four single and multifractal models were used to perform fractal calculations, and correlation analysis was conducted on the mercury advance and retreat fractal dimension values to clarify the physical significance of mercury removal fractal dimension values. Finally, the relationship between fractal dimension values of mercury curves and pore structure parameters was clarified, and the applicability of various fractal models in characterizing pore structure heterogeneity was explored. All the samples can be divided into type A and B by using pore volume percentage greater than 1000 nm and the mercury removal efficiency. The T model has universality and the strongest correlation in characterizing the heterogeneity of pore volume distribution in samples. A fractal parameter based on high-pressure mercury injection curve was obtained, and was then used to quantitatively characterize the pore and fracture structure of coal reservoirs. This parameter is used to characterize the complexity of gas and water transport during coalbed methane production, further elucidating the coalbed methane production process under the constraint of pore and fracture structure in coal reservoirs. Full article

In conventional engineering education, issues such as the discrepancy between virtual and real environments, rigid practical operations, lack of reflective support, and a disconnect between online and offline learning prevail. Digital twin technology, with its high fidelity and real-time interaction features, presents an innovative instructional aid for engineering education. This study developed a digital twin learning system to assist instructors in implementing project-based teaching models in landscaping technology courses. To assess the effectiveness of this system, a quasi-experiment was designed. Seventy students from a vocational high school majoring in landscaping technology in China were recruited as participants. These students were divided into two groups, each consisting of 35 students, with the same teaching pace. The experimental group utilized the system to supplement the instructor’s teaching of landscaping courses, while the control group received instruction through traditional methods. The experiment lasted for eight weeks, comprising a total of 16 classes. Ultimately, the results indicated that students in the experimental group significantly outperformed those in the control group in critical thinking, cognitive load, learning experience, and academic performance. Additionally, this research examined the acceptance of learners toward using the digital twin learning system and its influencing factors based on the Technology Acceptance Model, aiming to provide insights into enhancing engineering education courses teaching effectiveness and targeted technological development. Full article

Facility Management (FM) has increasingly focused on integrating Building Information Modeling (BIM) with the Internet of Things (IoT), known as digital twins, in large-scale development projects. Effective BIM integration in FM requires improved cooperation among participants across various project stages. This digital revolution aims to enhance planning, construction, and asset management efficiency, benefiting all parties. However, BIM utilization in FM is limited by incomplete owner understanding, insufficient data accessibility, and stakeholders’ unfamiliarity with BIM procedures and standards. Despite recognizing BIM’s significance, the FM industry faces significant implementation challenges. Facility managers often lack a comprehensive understanding of BIM’s benefits in streamlining operations and enhancing cost efficiency, as well as the necessary skills for its use. Addressing these barriers requires developing an Employer’s Information Requirements (EIR) document at a project’s outset, providing a strategic plan and vision for all involved parties. BIM and IoT are pivotal technologies for transitioning to efficient building operations and crucial for reducing time, costs, and operational challenges throughout any project. This research aims to establish a digital trio workflow, integrating BIM, EIR, and IoT to maximize stakeholder benefits. It explores how preparing the EIR through stakeholder communication can improve design processes, sustainability, efficiency, cost, and time, especially for megaprojects. Full article

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling the loads of air-conditioning systems in summer, and the urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D building data. It aims to explore the methods of geometry optimisation, limitations, and performance issues of a 3D computer-aided design (CAD) tool dedicated to small-scale solar analysis and employed at the city scale. Two cases were considered at the city and district scales, respectively. The total face count of meshes for the simulations constituted approximately 2,000,000 faces. A total of 64,379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualised in a 3D web platform. The use of a 3D CAD environment establishes a seamless process of updating 3D models and simulations, while preprocessing in Geographic Information System (GIS) ensures working with large-scale datasets. The proposed method showed a moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future. Full article

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities’ definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities’ emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs. Full article

The digitalisation of production has a positive impact on manufacturing processes in terms of resource efficiency and environmental impact, particularly in the form of increased efficiency as well as cost and resource savings. However, the use of digitalisation technologies is also associated with efforts such as costs, CO₂ emissions, and raw material consumption. When planning or deciding on the digitalisation of manufacturing systems, it is therefore necessary to assess whether these technologies pay off in terms of sustainability over their life cycle. This literature review (based on the PRISMA guidelines) analyses the relevance of sustainability assessment and its methods for the digitalisation of production in research. The review reveals that research focuses on the benefits of digitalisation technologies in manufacturing, while the assessment of efforts and their benefits is in its infancy. There is a need for further research on holistic assessment methods for digitalisation technologies. In particular, there is a lack of assessment methods that consistently link the economic and environmental dimensions of sustainability, and there is also a lack of guidance for the application of assessment methods in production. Full article

Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide range of nuclear sensors for flexible and modular inspection tasks in these operations. This AMR is part of the CLEANDEM solution, which uses Unmanned Ground Vehicles (UGVs), nuclear sensors, and a Digital Twin to facilitate a tool for improving D&D operations in nuclear sites. Both the AMR used as a UGV and the system have been successfully tested in real nuclear sites, showing that these tools can greatly aid in operations management and hazard reduction. Full article

Construction sites are highly unpredictable environments involving a wide variety of stakeholders with complex information exchanges, which lead to the well-known inefficiencies and unproductivity of the construction sector. The adoption of Building Digital Twins (BDT) in the construction site is a promising solution to this issue, by automating data acquisition and knowledge extraction processes and providing what-if scenario simulation capabilities. Furthermore, the current research sets the principles to define, replicate, and scale-up the architecture of a Building Digital Twin Platform (BDTP), conceived as a scalar ecosystem, which allows to seamlessly manage on-site construction processes, integrating cross-cutting domains for the construction site optimization (Progress monitoring, Quality control, Operational Health and Safety, Equipment control, and Production planning). The starting point of the research is a comprehensive diagnosis of on-site process inefficiencies and the barriers to its digitalization leading to the user requirements, which have been underpinned by questionnaires and interviews addressed within an open innovation user-centered approach around Living Labs. The research has been conceived following the Design Science Research (DSR) methodology and based on the Plan-Do-Check-Act (PDCA) analysis for the continuous improvement of the construction process. By means of the adoption of the standard Business Process Model and Notation (BPMN), based on the BDTP architecture, the research has resulted in BPMN workflows stemmed from the Digital Twin (DT) where the DT itself is an actor in a service-oriented data-exchange workflow. Moreover, the use of a BDTP can pave the way for the transition from user-driven construction management to hybrid management, coexisting with both human and digital actors and merging expert knowledge with artificial intelligence techniques. Full article

Persistent security challenges in Industry 4.0 due to the limited resources of IoT devices necessitate innovative solutions. Addressing this, this study introduces the ASCON algorithm for lightweight authenticated encryption with associated data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission and bolstering system resilience against cyber threats. Practical validation using the MQTT protocol confirms ASCON’s efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era. Full article

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation. Full article

Robotics includes complex mathematical calculations and coordinate transformations in forward and inverse kinematics, path planning, and robot dynamics. Students may experience a high cognitive load and lose learning motivation because robotics can be complex and challenging to understand. This study applied virtual reality (VR) technology in robotics education to simplify and visualize complex robot kinematics, aiming to increase learning motivation and reduce cognitive load. This study incorporated real and virtual robot control to develop an integrated robot learning system. This system enables learners to control the digital twin of a physical robot and observe the synchronized motion of both the virtual and physical robots. Users can operate the virtual robot to achieve the target position by setting joint parameters or using values calculated from inverse kinematics. They can also understand the principle of digital twins by observing the synchronous motion of both robots. A teaching experiment was conducted to explore the performance of applying VR in robotics education and its impacts on cognitive load and learning motivation. The system was improved based on user responses to facilitate subsequent promotional activities. VR can transform complex robotics into easily understandable learning experiences and provide an interactive user interface, making the system a suitable learning tool for STEM education. Full article

Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a “particle model” approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations. Full article

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms. Full article

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field. Full article

Despite the considerable advances that industrial manufacturing has undergone as a result of digitalization, the real-time monitoring of assembly processes continues to present a significant technical challenge. This article presents a solution to this problem by integrating digital twin technology with radio frequency identification (RFID) in order to improve the monitoring and optimization of assembly processes. The objective of this research is to develop a methodology that ensures synchronized data exchange between physical components and their digital counterparts using RFID for improved visibility and accuracy. The methodology entails the configuration of radio frequency identification systems to track the positions of products on conveyor belts, thereby facilitating real-time monitoring and the prompt detection of any deviations. This integration enhances remote monitoring capabilities and markedly optimizes assembly processes in comparison to traditional methods. The research findings suggest that this approach offers real-time data and monitoring capabilities, which can contribute to improved operational efficiency. This study presents an introduction to digital twins and RFID technology, a review of related research, a detailed methodology, an implementation plan, results and analysis, a discussion of the findings, and conclusions with future recommendations. This article presents a comprehensive discussion of the configuration of an RFID-based digital twin for an assembly line, highlighting the benefits and challenges of integrating these technologies into industrial processes. Full article