Drilling Advisory Automation with Digital Twin and AI Technologies

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

Digital twin technology has evolved from a theoretical concept to practical application, facilitating seamless data exchange between virtual and physical domains. Although there has been progress, the infrastructure industry, which is recognized for its intricate nature and the need for timely action, is still in the first phases of digital twin advancement. A significant obstacle in this field is the absence of established definitions and modeling standards, which impede the precise depiction of infrastructure systems. To address these challenges, this paper proposes a high-precision digital twin modeling method tailored for pumping stations. The method focuses on two key scenarios: first, we construct an overall digital twin model that contains both physical entities and operational processes of pumping stations; second, we design a modeling process applicable to pumping stations by analyzing the deficiencies of the existing standard system. Additionally, we selected the East–West Water Transfer Project in China as a case study to demonstrate the high-precision digital twin model of a pumping station. This model will include essential components, such as the modeling of pumping stations, the operational processes of pumping stations, and the modeling of system operation analysis. Serving as the database for the digital twin, it can complete the automatic inspection of the pumping station, optimization of scheduling, prediction and regulation of energy and carbon emissions, and visualization of results for display and other applications. The model realized the benefits of 100% automatic inspection rate, reduction of eight corresponding operating personnel, and comprehensive cost saving of RMB 2.25 million. The objective of this research is to narrow the divide between theoretical concepts and real-world implementations by pushing the boundaries of digital twin modeling and offering valuable insights for its utilization in the infrastructure industry. It establishes the foundation for progress in the field of digital twin technology in the specific context of intricate infrastructure projects. This project aims to improve the practicality of digital twin technology in real-world situations, namely in the infrastructure industry.

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

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.

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".

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

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.

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.

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.

This research investigates the use of digital twin (DT) technology to improve building energy management and analyse occupant behaviour. DTs perform the function of acting as virtual replicas of physical assets, which facilitates real-time monitoring, predictive maintenance, and data-driven decision-making. Consequently, energy performance and occupant comfort can be enhanced. This study evaluates the efficiency of DTs in optimising energy usage by a mix of a systematic literature review and scientometric analysis of 466 articles from the Scopus database. Among the main obstacles noted are interoperability issues, privacy and data quality difficulties, and the requirement for a more thorough integration of digital and physical interactions. The results highlight the necessity of standardised frameworks to direct DT implementations and suggest areas for further study, especially in the areas of improving cybersecurity and incorporating occupant behaviour into DT models. This research makes practical recommendations for using DT technology to increase energy efficiency and sustainability in the built environment.

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance.

Urban centers serve as dynamic hubs of data and information, continually shaping the modern landscape. The fusion of Big Data and Digital Twin (DT) technology plays a pivotal role in advancing smart city initiatives. DT, acting as a comprehensive virtual replica mirroring physical entities' lifecycles, utilizes real‐time data, simulations, and machine learning to enrich decision‐making processes. In urban development, Big Data assumes diverse roles, particularly in urban planning, resource management, and traffic optimization, providing valuable, data‐driven insights to decisionmakers. Simultaneously, DT technology contributes significantly to modeling urban environments, enabling real‐time simulations, and strengthening decision support systems. However, challenges persist, notably in data security and model precision. Addressing these challenges necessitates concerted efforts to enhance data privacy measures and refine the cognitive capabilities of DT models. This paper examines the intricate interplay between Big Data and DT technology in shaping the evolution of smart cities, offering insights into their roles, applications, and implementation challenges. Furthermore, it advocates for future research endeavors aimed at overcoming existing obstacles, thereby fostering secure and effective deployment of Big Data‐driven DT technology and promoting innovative advancements in smart city management and sustainable development.

The construction of cleanrooms is a critical endeavor in industries such as pharmaceuticals, semiconductors, and biotechnology, where stringent contamination control, cost efficiency, and adherence to strict timelines are paramount. Traditional cleanroom construction methods often face challenges related to budget overruns, schedule delays, and contamination risks, necessitating innovative technological solutions. The integration of Building Information Modeling (BIM), Digital Twin technology, and AI-driven project management systems is transforming cleanroom construction by enhancing precision, efficiency, and risk mitigation. BIM facilitates real-time collaboration, clash detection, and resource optimization, reducing construction errors and rework, ultimately minimizing costs and project delays. Digital Twin technology, by providing a dynamic virtual representation of the construction process, enables real-time monitoring, predictive maintenance, and enhanced quality control, ensuring compliance with strict cleanroom standards. Furthermore, AI-driven project management tools leverage predictive analytics, automation, and machine learning algorithms to optimize scheduling, labor allocation, and material procurement, preventing cost escalations and streamlining workflows. This paper explores the synergistic impact of BIM, Digital Twin, and AI technologies in cleanroom construction, emphasizing how their combined application improves cost efficiency, accelerates project timelines, and mitigates contamination risks. Through case studies and performance analysis, we demonstrate the effectiveness of these technologies in revolutionizing cleanroom project execution. By adopting these cutting-edge digital solutions, stakeholders can achieve unprecedented efficiency, regulatory compliance, and contamination-free environments, ensuring the sustainable and future-proof development of critical cleanroom infrastructure.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

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.