An Effective Digital Twin Modeling Method for Infrastructure: Application to Smart Pumping Stations

Additive manufacturing (AM) has revolutionized the manufacturing industry by offering flexibility, customization, and rapid prototyping capabilities. Complex geometries and low volume parts can be produced via AM in much lower lead time and cost compared to traditional manufacturing methods. However, ensuring the quality and reliability of AM parts remains a significant challenge due to variations in material properties and process parameters. Traditional material property analysis methods, that require experimental testing and computational modeling, are often costly and time-consuming and often not applicable to the complex geometries of AM components. These challenges are further magnified when repetitive sampling is required. To address these challenges, this paper presents a novel approach that combines experimental findings with Finite Element Modeling (FEM) to construct a digital twin (DT) model of AM parts. The DT model will serve as a virtual representation along with real-time monitoring, simulation, and optimization capabilities. Numerical simulations are conducted to correlate the resonance frequency of parts at different process conditions with their material characteristics, represented as RF Z-score values. Hence, the results suggest that such an approach provides manufacturers with cost-effective and efficient means of analyzing material properties and optimizing AM processes. Moreover, by leveraging digital twin technology, manufacturers can achieve greater process control and quality assurance in additive manufacturing. The findings of this study contribute to advancing the understanding and implementation of digital twin technology in AM; therefore, paving the way for enhanced productivity and reliability in additive manufacturing processes.

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

In the process of the construction of China's new power system, we will vigorously promote the research and development of UHV power equipment and the wide application of power electronic devices. UHV power equipment has complex insulation structure and huge volume, bear impact energy load of wind power and photovoltaic of new power system for a long time. It will cost a lot to carry out on-site operation and maintenance tests. Digital twin technology is becoming more and more perfect, and new power system construction is gradually introduced from automobile, aviation and other manufacturing industries. Based on this, this paper introduces the digital twin technology into the high-end power equipment of the new power system, and carries out on-site operation and maintenance simulation test and functional response analysis under high current, high voltage and multi harmonic loads according to its twin model. From the four sensing dimensions of mechanical vibration, gas composition, optical vision and electrical parameters, the improvement of intelligent sensing technology of new power system equipment is analyzed, and the interaction between on-site operating parameters and digital twin model data is realized. On the other hand, GPU computing power expansion technology supporting digital twin multi-source sensing technology is proposed, which can effectively support the dynamic behavior simulation monitoring of equipment from 10-5 seconds to 103 seconds, and the operation life evaluation strategy of high-end equipment is proposed. This paper focuses on the 3D construction of the digital twin model of the high-end equipment of the new power system, and its research method can be extended to the construction of the whole network digital twin model of the new power system. The research results can provide theoretical guidance and technical reference for the application of digital twin technology in high-end power equipment scenarios, and effectively support the safe and stable operation of the new power system with "double high characteristics".

This paper presents an autonomous drilling advisory system powered by digital twins and AI solutions. Such an advisory system aims to automate real-time monitoring and parameter optimization, reduce subject-matter experts, and meet the demands for safer and more efficient drilling toward autonomous operation. The methodology proposed in this research involves the creation of a comprehensive Digital Twin model that accurately replicates the drilling process by integrating hydraulic, thermal dynamic, and mechanical models. To ensure high model accuracy, an auto-calibration approach is developed, driven by real-time data, to fine-tune the Digital Twin models. Additionally, AI-based model reasoning techniques are employed to detect potential hazards and risks ahead of the bit proactively. This is achieved by comparing the ideal behavior of the digital twin replica with the actual behavior observed from downhole and the rig. As a result, real-time diagnostics are generated to supervise ongoing operations, accompanied by suggestions to mitigate identified risks. Furthermore, the system leverages the capabilities of the Digital Twin and optimization methods to create multiple combinations of operational parameters. These parameters are optimized by ranking the predicted performance derived from the Digital Twin. The optimized operational parameters are automatically generated as forward advice to drillers, enabling them to make informed decisions and enhance drilling performance. Testing results on multiple wells from different operators are presented, showcasing the system's capabilities in real-time monitoring and drilling parameter optimization. The system demonstrates its effectiveness in providing diagnostic messages with early anomaly detection during drilling and casing running. These diagnostic warnings include losses, leakage, poor hole cleaning, and stuck pipe, enabling proactive intervention to mitigate risks. Furthermore, the system optimizes operational parameters during drilling and tripping in real-time without requiring human intervention. This optimization covers parameters such as flow rate, rotary speed (RPM), and rate of penetration (ROP) during drilling, and tripping speed during tripping in and pulling out of the hole. The time savings achieved through the use of optimized parameters are quantified for both cases, demonstrating a substantial improvement in operational efficiency while maintaining safety margins. The scalability and adaptability of the system are also highlighted, emphasizing its ability to accommodate diverse drilling scenarios and integrate with existing solutions in various deployment conditions. The proposed methodology demonstrates the development of a robust and efficient system that enhances decision-making and improves drilling performance. In addition, the results highlight the potential benefits of combining AI and Digital Twin technologies in the drilling industry, paving the way for future innovations and advancements in the field.

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB.

Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security

Environmental factors, such as climate change, have serious consequences for existing buildings, including increased resource consumption and footprint, adverse health effects, and reduced comfort for the occupants. To promote sustainability and address climate change, architecture must embrace digitalization. Buildings can be built digitally, analyzed in real time, optimized for energy consumption, and utilized to reduce carbon emissions and achieve zero energy consumption using digital twin technology. Currently, Lebanon’s residents are turning to solar power to generate renewable energy as a result of a lack of energy supplied by the government. In this study, a digital twin model was designed using an artificial neural network (ANN) to investigate the energy consumption of residential buildings. The main idea was to assist architects and engineers in forecasting energy consumption for different design materials by selecting the most effective alternate design for materials with building envelope characteristics, such as exterior walls, roof insulation, and windows, to minimize the consumption of energy in a residential building, hence resulting in a green building. The data simulations used in the digital twin model were carried out using Quick Energy Simulation Tool (eQuest) software; 1540 simulation results were used for different thicknesses of insulation material, values of conductivity, and window types. The digital twins were designed using an artificial neural network model. The results of the investigation and the accompanying eQuest output results were found to be precise and very similar.

Paper concerns the novel modelling technology called Digital Twin in aspect of physical phenomenon simulation analysis. Digital Twin model concerning electrodynamic forces in three-phase high voltage disconnector was presented, validated and discussed. Experiences related to laboratory work and measurement of electrodynamic forces clearly showed that often, despite the use of modern computing machines, the research process is hard, very expensive and values of electrodynamic forces are almost impossible to measure in real scale conditions. Hence the idea to use very advanced field models minimized to a reduced order model (ROM), using the Digital Twin Technology seemed valid and rendered possibilities for advancements concerning this type of research. Due to the large weight of devices (chokes, transformers, disconnectors) on high-voltage networks it is often time-consuming or even impossible to perform the entire simulation. Proposed modelling approach helps to obtain results from very complex models also for long-term thermal tests. The reduced order model can be used not only by researchers, but also in the development departments of enterprises. Digital Twin model results were compared and validated with values derived from short-circuit tests made in Institute of Power Engineering, Research Institute.

As an innovative information technology, Digital Twin has greatly promoted the development of intelligent manufacturing in the industry. However, in the field of geotechnical engineering, there are still few researches on this aspect, which is still a “new territory” and “no man's land”. The concept of digital twin coincides with the needs of geotechnical engineering informatization, so the introduction of digital twin technology into the field of geotechnical engineering will help to promote the development process of geotechnical engineering informatization and digitization. This paper puts forward and defines the digital twin model of geotechnical engineering, describes the connotation of the digital twin model, and studies the architecture of the digital twin model of geotechnical engineering. On this basis, the integration and sharing mechanism of geotechnical engineering digital twin data based on BIM technology is proposed. In order to break through the defect that the geotechnical engineering information model has not fully played its role for a long time, an integrated model of geological body and structural body is constructed based on the construction and integration module of geotechnical digital twin model. Furthermore, the geotechnical engineering digital twin simulation analysis module is developed to initially form the geotechnical engineering digital twin model, so as to realize the geotechnical engineering digital design, collaborative construction, visual decision-making and transparent management.

This article analyzed approaches related to the development of digital twins. Interest in digital twin technology is growing in science and law enforcement. In this regard, we can talk about the formation of the Concept of “digital twins”, which is based on the fact that it is a virtual copy of a physical object that can track it in real time. Accordingly, a framework has been established to compare digital twin approaches in terms of research areas, technologies and models used in digital twin, and verification methods used. Using the proposed framework, gaps and future directions for digital twin research are identified along five dimensions: digital twin definitions, applications, integration, modeling, and data.

Meta-model-based shop-floor digital twin architecture, modeling and application

Digital twin technology has been increasingly applied in healthcare and patient well-being in recent years. This paper provides an overview of the current methods and applications of digital twins in the healthcare field. One such application is digital twins in precision healthcare, where digital twins are used to create patient-specific models to assist in diagnosis and treatment planning. Digital twins are also used in hospital/clinic management, where they help to optimize resource allocation and workflow processes. In response to the COVID-19 pandemic, digital twins have been utilized to detect outbreaks and predict disease spread. In addition, digital twins have been applied in bio-manufacturing and pharmaceutical industry to improve manufacturing processes. Another application area is machine learning and modeling, where digital twins are used in machine learning, data generation, and system modeling for applications in healthcare and disease prediction. Security and ethical issues related to digital twins are also discussed in this paper, as privacy concerns and data protection remain important considerations in the application of digital twin technology in healthcare. Finally, the paper concludes by discussing the future challenges and directions of future work in this field. These include the need to develop more accurate and sophisticated digital twin models, addressing interoperability and integration issues, and further exploring the potential of digital twin technology in emerging areas such as telemedicine and personalized medicine.

The Halal supply chain, which encompasses the sectors of food, medicines, cosmetics, has enormous challenges to comply with Islamic rules, maintain product traceability and combating fraud. Given the increasing global demand for Halal products, the demand for technology innovations to enhance supply chain transparency and ensure Halal compliance is growing. This paper explores the application of Digital Twin (DT) technology in Halal supply chain management, highlighting its ability to address these challenges. The main research question of this paper is as follows: How can Digital Twin technology facilitate transparency, traceability, and compliance in the Halal supply chains? The current study employs a qualitative methodology through a review of the extant literature and reviews of industrial case studies. The study reveals that the Digital Twin technology promises to considerably improve the operational excellence of Halal supply chains through real-time Halal status monitoring ensuring that products comply with religious and ethical requirements along the supply chain. The main applications include tracking Halal certification, preventing cross-contamination, and enhancing transparency for certification bodies, manufacturers, and consumers. The results indicate that Digital Twin technology has the potential to transform Halal supply chains by reducing fraud, improving operational efficiencies, and building consumer trust. As a comprehensive Halal compliance solution, Digital Twin can enable a more transparent and trustworthy Halal certification process on a global scale. The study extends the knowledge body on technology innovations in supply chain management especially with religious and ethical product certifications and sets the stage for future research in the area.

Background: With the rapid development of digital twinning technology, the compatibility of digital twinning technology to other technologies is continuously enhanced. It is because of this that the application of image recognition technology in digital twinning technology becomes a reality. However, the key technology of digital twin, the virtual-actual mutual control technology, is not mature enough, and the image recognition technology applied in digital twin technology also has the problem of coordinate system transformation, which becomes an important link of image recognition technology in digital twin. Methods: Based on the above two problems, we take the tangram splicing project as an example to realize a virtual-actual mutual control method, so that the digital twinning technology can be well presented. Furthermore, we implement an image recognition applied to the conversion of digital twinning technology, so that digital twinning technology and image recognition have a seamless connection, allowing the application range of digital twinning technology to be further expanded. Results: In this paper, image recognition technology is successfully applied to digital twin technology by adopting the conversion between different coordinate systems and the real and virtual real time control, which makes the applicability of digital twin technology in high-tech fields such as smart factories and smart manufacturing to a higher level. Conclusions: Finally, through the tangram splicing project, the motion trajectories of the robotic arm and the tangram are consistent with the virtual robotic arm and the tangram in the computer. It is proved that our method can well combine digital twinning technology and image recognition technology.

Existing centralized building management systems suffer from high response latency and limited control capabilities. This study develops a hierarchical edge computing framework that integrates digital twin technology and model predictive control to achieve real-time, multi-objective environmental optimization. The proposed architecture consists of four distributed layers: a heterogeneous sensor network, edge preprocessing on a Raspberry Pi 4 device, digital twin modeling using Kalman filtering, and MPC optimization using the SLSQP algorithm. Multi-source data fusion integrates 44,326 records from the EPA, PhysioNet, and NOAA databases using cubic spline interpolation and PCA dimensionality reduction. The digital twin utilizes a simplified physics-based model for six-hour condition predictions, while MPC optimizes HVAC operation at 15-minute control intervals using ventilation rates and temperature setpoints. System evaluation results demonstrated an end-to-end response latency of 1.8 seconds with 71% CPU utilization on edge hardware. Prediction accuracy reached 75%, energy savings reached 10.4%, and target maintenance achieved 94.7%. Compared to PID control, this framework improved CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> management efficiency by 41.4% and reduced comfort violations by 63.2%. The distributed edge computing architecture demonstrated superior computational efficiency and economic feasibility, with a payback period of 2.2 years.