Digital Twin Applications Research Articles

Application of Digital Twin in Structural Health Monitoring of Civil Structures: A Systematic Literature Review Based on PRISMA

Structural Health Monitoring (SHM) systems are critical for ensuring the safety and longevity of structures. Digital twin technology has emerged as a promising tool to improve the accuracy and efficiency of SHM systems. This paper presents a systematic literature review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the applications of digital twin for SHM of civil structures. And this systematic literature review examines the current trends and developments in the application of digital twin technology in SHM systems for structures. The review identified 45 relevant articles published between 2010 and 2023 that covered various applications of digital twin in SHM systems, including buildings, bridges, wind turbines, trains, and gas turbines. Key themes identified from the literature review included the integration of physical models and machine learning algo-rithms, the use of distributed sensor networks, and the importance of real-time data analysis for effective SHM. The findings suggest that digital twin technology has the potential to significantly improve the accuracy and efficiency of SHM systems, but further research is needed to develop more advanced and integrated Digital Twin-Based SHM systems for different types of civil structures.

Developing an integrative framework for digital twin applications in the building construction industry: A systematic literature review

In response to the global advocacy for digitalization, numerous industrial sectors are actively advancing the implementation of digital twins (DTs). However, the construction industry has been slow to adopt DT, lagging behind sectors such as manufacturing and medicine, where digitalization has made significant strides. Given the demonstrated benefits and potential of DT, it is crucial to increase research efforts for enhancing its application in the construction industry. To accurately identify prospective areas for future research, a comprehensive review and analysis of the status quo of relevant research is urgently needed. This study undertook a systematic literature review to understand how DT can enhance operational intelligence in the building construction industry. The findings, based on a review of pertinent literature, revealed that existing studies predominantly focused on the application of DT during the construction phase and the operation and maintenance phase. However, its potential use during the planning and design phase and the demolition and recovery phase was largely overlooked. Three main categories of key digital facilitating technologies for DT in the building construction industry have been identified: (1) data-related technologies; (2) high-fidelity modeling technologies; and (3) model-based simulation technologies. These technologies have finally culminated into an integrative framework. Furthermore, three significant research gaps in the application of DT in the building construction industry have been identified, suggesting future studies explore how to (1) centralize diverse stakeholders from a lifecycle perspective; (2) address various existing technological defects; and (3) establish a universal industry standard. This paper provides a valuable roadmap for relevant stakeholders to understand the status quo of DT application and the existing deficiencies in its full implementation, which should facilitate the efficient digitalization of the construction industry.

Applications of Digital Twins in Reinforced and Prestressed Concrete Bridge Infrastructure

Applications of Digital Twins in Reinforced and Prestressed Concrete Bridge Infrastructure

Machine Learning based calibration SDR in Digital Twin application

Software-Defined Radios are radio communications devices that have been growing and developing on a larger scale in recent years. Communications are intrinsically embedded in our day by day, thus presenting a higher motivation to use software-defined radios due to its attractive cost. However they present technical limitations. This paper addresses this problem, which is the non-linearity behaviour of gain and frequency in the LimeSDR-USB. That is, this equipment is used to produce a FM signal with an associated frequency and gain before being parameterised according to the internal parameters of each software-defined radio. Each software-defined radio presents a value of frequency and gain of its own, which correlates to the generated signals at the output level. To avoid this, machine learning networks were used, in which networks were trained to adapt to the non-linearity of these devices and correct it without the user noticing. This way, the user sets a desired frequency and gain in a signal, at the output of the software-defined radios, and a neural network calculates which values the software-defined radios should be parameterised, thus mitigating the non-linearity behaviour. This paper presents the evaluation of a laboratory prototype based on low-cost commercial software-defined radios equipment, to replace an expensive metrologically calibrated equipment used for radio frequency tests on a new concept of industrial test station, with description of the integration of Digital Twins, with their physical and virtual parts.

A Fingerprint Approach for Computationally Efficient Digital Twins of Precision Manufacturing Processes

Over the past decade, digital twin (DT) technology has seen significant advancements, enabling it to effectively address complex manufacturing challenges. However, the effectiveness of data-driven DTs is hindered by the sheer volume of data generated by sensors and devices which limits the traditional computational resources causing latency issues. To address this challenge, there is an urgent need to optimize data processing and ensure real-time responsiveness in DT applications. This paper presents a novel fingerprint-driven approach aimed at building computationally efficient DTs for smart manufacturing. When embedded within the DT framework, process and product fingerprints capture the essential manufacturing parameters while reducing the overall computational overhead of the DT. A case study on machining Inconel 718 for a high-fatigue application demonstrates the practical application of the proposed approach in ensuring computational efficiency. When compared to conventional machine learning (ML) methods, the fingerprint approach exhibited substantially superior performance in terms of responsiveness (20 times less latency) and prediction accuracy (3% more accurate).

Digital Twins in Manufacturing: A Three-layer Heat-map Analysis

This paper explores the landscape of Digital Twins (DTs) in smart and sustainable manufacturing through a systematic literature review. In particular, the paper aims to identify the widespread trends and intersections of Digital Twin (DT) applications, maturity levels, and sustainability dimensions, by means of a three-layer heat-map chart. Observations indicate a concentration of DT applications in the production side of manufacturing, with notable impacts on economic and environmental sustainability. However, major deficiencies in addressing social sustainability persist in overall applications. Further insights suggest that DT research requires ongoing advancements in the establishment of granular maturity levels, in response to the need for a clearer categorization and definition of DTs.

Application of Digital Twin Combined LSSVM Algorithm in Control Loop State Monitoring

Application of Digital Twin Combined LSSVM Algorithm in Control Loop State Monitoring

ОСНОВНЫЕ ПРОБЛЕМЫ ВНЕДРЕНИЯ ЦИФРОВЫХ ДВОЙНИКОВ В ПРОМЫШЛЕННОСТЬ

This article is devoted to the main problems of implementing digital twins in the industry. The paper explores the key aspects of the implementation of this innovative technology, the main difficulties encountered by companies in the implementation of digital twins, and offers recommendations for overcoming them. The author applies theoretical research methods, uses the results of domestic and foreign scientific research. The article discusses the advantages of digital twins for manufacturing and highlights the main problems such as accurate model building, data processing, security of digital assets and standardization. It is concluded that the successful application of digital twins requires solving the above challenges to realize their full potential in industry.

Digital Twin as A New Approach to Data Management: A Review

The concept of digital twin has gained substantial attention in both academic and industrial circles in recent years due to its potential for facilitating smart manufacturing and contributing to Industry 4.0. The digital twin concept revolves around the idea of replicating the characteristics and behaviors of real-world objects to provide accurate representations in virtual environments. It has been widely used in various industries for different purposes, such as virtual experimentation, prediction and performance optimization, monitoring, and control. However, the existing studies have not investigated the potential application of digital twin for data management in a structured and methodological manner. Therefore, this paper aims to review articles that explored the use of digital twin as a data management method at different levels and dimensions. Moreover, this review critically analyses studies that explored relevant methods that can support digital twin applications in a data management context. This study follows PRISMA guidelines and reviewed 51 academic articles from various academic databases. The primary findings indicate that the digital twin can serve as a comprehensive platform for collecting, processing, analyzing, and managing data from various sources in a unified approach. Based on the critical review, future directions in this research context are outlined.

Reflective optical mirror group assembly process optimization method based on accurate digital twin

The reflective optical mirror group is the core component of precision optical instruments, and their assembly accuracy will directly impact the imaging of optical instruments. In recent years, with the rapid development of digital twin technology in the field of assembly, the application of digital twin to assembly process optimization is currently a research hotspot. This paper proposes an assembly process optimization method for a reflective optical mirror group based on an accurate digital twin model. By accurately simulating the geometrically distributed errors generated by machining and the physical properties of the assembled parts of the reflective optical mirror group during assembly and use, an accurate digital twin model is established to accurately characterize the geometrical and physical properties of the reflective optical mirror group. Using the accurate digital twin modeling method, the mirror deformation and stress distribution of a reflective optical mirror group are accurately predicted, and the main process parameters in the assembly process are optimized according to the prediction results, so that the target value of the imaging phase of the mirror group after assembly is improved by 24%, and the encircled energy is improved by 15.1%. The optimization calculation method proposed in this paper can obtain the optimized assembly process parameters more accurately, and the research results provide effective support for improving the assembly accuracy and performance of optical mirrors and precision digital twin modeling research.

METHODOLOGY FOR INTELLIGENT PLASTIC INJECTION POINT LOCATION BASED ON GEOMETRIC ALGORITHMS AND DISCRETE TOPOLOGIES FOR VIRTUAL DIGITAL TWIN ENVIRONMENTS

Implementing intelligent design models can revolutionize the use of digital twins, which are crucial in product design by incorporating intelligent algorithms. This perspective is especially important for the design of injection-molded plastic parts, a complex process that often requires expert knowledge and costly simulation software not available to all companies. This article presents an innovative methodology for locating injection points in injection-molded parts using intelligent models with geometric algorithms for discrete topologies. The first algorithm calculates the center of mass of the discrete model based on the center of mass of each triangular facet in the system, ensuring uniform molten plastic distribution during mold cavity filling. Two sub-algorithms intelligently evaluate the geometry and optimal injection point location. The first sub-algorithm generates a geometric matrix based on a two-dimensional nodal quadrature adapted to the part's bounding box. The second sub-algorithm projects the nodal matrix and associated circular areas orthogonally on the part's surface along the demolding direction. The optimal injection point location is determined by minimizing the distance to the center of mass from the first algorithm's result. This novel methodology has been validated through rheological simulations in six case studies with complex geometries. The results demonstrate uniform and homogeneous molten plastic distribution with minimal pressure loss during the filling phase. Importantly, this methodology does not require expert intervention, reducing time and costs associated with manual injection mold feed system design. It is also adaptable to various design environments and virtual twin systems, not tied to specific CAD software. The validated results surpass the state of the art, offering an agile alternative for digital twin applications in new product design environments, reducing dependence on experts, facilitating designer training, and ultimately cutting costs Keywords: Injection Moulding, Product Design, Industrial Design, Geometrical Algorithms, Digital Twin

Field testing of low-cost particulate matter sensors for Digital Twin applications in nanomanufacturing processes

The EU-project ASINA is testing Low-Cost Particulate Matter Sensors (LCPMS) for industrial monitoring of the concentration of airborne particles, with the purpose of integrating this sensor technology within the data collection layer of Digital Twins (DTs) for manufacturing.This paper shows the results of field performance evaluations carried out with five LCPMS from different manufacturers (Alphasense OPC-N3, Plantower 9003, Sensirion SPS30, Sensirion SEN55 and Tera Sensor NetxPM), during several field sampling campaigns, conducted in four pre-commercial and commercial pilot lines (PLs) that manufacture nano-enabled products, belonging to the ASINA and OASIS H2020 EU-projects [2,28]. Field tests consisted of deploying LCPMS in manufacturing process, measuring in parallel with collocated reference and informative instruments (OPS TSI 3330/CPC TSI 3007), to enable intercomparison.The results show the complexity and differential response of the LCPMS depending on the characteristics of the monitored scenario (PL). Overall, they exhibit uneven precision and linearity and significant bias, so their use in industrial digital systems without proper calibration can lead to uncertain and biased measurements. In this sense, simple linear models are not able to capture the complexity of the problem (non-linear systems) and advanced calibration schemes (e.g. based on machine learning), applied “scenario by scenario” and in operating conditions as close as possible to the final application, are suggested to achieve reliable measurements with the LCPMS.

Digital Twin of a battery-based EV charging station to support services

Digital Twin (DT) concepts and applications have been evolving to explore the potential to support Product-Service Systems (PSS). At the same time, DT newest developments include Electric Vehicle’ (EV) battery systems. However, the literature on Digital Twins of EV charging stations as service systems is still limited. This work presents a Digital Twin information architecture of an EV charging station aiming to support the related services. The proposed information architecture has been applied to a novel battery-based EV mobile charging platform under development. The prototyped application utilizes battery charge and discharge data to enable a scheduling service. Other services, such as reservation, SoX estimation, and lifecycle management, are also discussed. Results contribute to the expanding literature on the relationship between DT and PSS, focusing on mobility electrification. Such a service-oriented perspective of the Digital Twin application has the potential to foster innovative business models and highlight value for stakeholders.

Time Series Prediction with Digital Twins in Public Transportation Systems

Classical traffic and transportation control centres are becoming insufficient with the rapid spread of electric, intelligent, autonomous, and software-defined vehicles. Existing traffic management strategies have significant drawbacks in public safety, predictive maintenance, tuning the core functionality of vehicles, and managing mobility. We can renovate this system with next-generation intelligent Digital Twin (DT) technologies. This research proposes a timeseries prediction system through Digital Twins to manage public transportation system with Facebook’s Prophet. This study presents a model framework to build Digital Twin application in Intelligent Public Transportation Systems and used a public data set to validate model with Facebook’s Prophet library by forecasting metro lines usage. Obtained results presented in the study and it is shown that forecasting error interms of Mean Absolute Percentage Error (MAPE) is 0.017 for 1 day horizon.

Optimizing smart manufacturing systems using digital twin

Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective.

Trial of Utilization of an Environmental Map Generated by a High-Precision 3D Scanner for a Mobile Robot

In recent years, high-precision 3D environmental maps have attracted the attention of researchers in various fields and have been put to practical use. For the autonomous movement of mobile robots, it is common to create an environmental map in advance and use it for localization. In this study, to investigate the usefulness of 3D environmental maps, we scanned physical environments using two different simultaneous localization and mapping (SLAM) approaches, specifically a wearable 3D scanner and a 3D LiDAR mounted on a robot. We used the scan data to create 3D environmental maps consisting of 3D point clouds. Wearable 3D scanners can be used to generate high-density and high-precision 3D point-cloud maps. The application of high-precision maps to the field of autonomous navigation is expected to improve the accuracy of self-localization. Navigation experiments were conducted using a robot, which was equipped with the maps obtained from the two approaches described. Autonomous navigation was achieved in this manner, and the performance of the robot using each type of map was assessed by requiring it to halt at specific landmarks set along the route. The high-density colored environmental map generated from the wearable 3D scanner’s data enabled the robot to perform autonomous navigation easily with a high degree of accuracy, showing potential for usage in digital twin applications.

Application analysis of digital twin technology in power system

In the construction and development of modern society, in order to achieve the goal of double carbon, energy and power should build a digital, intelligent and electrified energy and power system in the transformation and development. According to the current statistical data analysis of China's energy Bureau, in recent years, more and more installed capacity of new energy generation in China, has become the world's second largest power source, while the proportion of non-fossil energy generation is also gradually rising, some scholars predict that in 2050, China's non-fossil energy will account for more than 50%. After understanding the concept of digital twin application technology, this paper mainly studies the application content and main direction of digital twin technology in power system according to the operation status of power system in the new era and the challenges faced by energy development, and then defines the development prospect of digital twin technology application in power system in the future.

Особенности проектирования и использования цифрового двойника проектной организации

The author of the article considers the issues related to the development and effective application of digital twin in the activities of design organizations. A brief comparative analysis of the concepts of "digital twin" (DT) and "BIM-model" is given. The specifics of creating a digital model of a project organization, which forms the basis of the DT, are analyzed. The processes that need to be modeled online are given. 
 It is emphasized that the main project processes are extremely creative and are directly related to the effective formation of project development teams. Team building models that can be implemented in DT are presented and analyzed. Two problem statements are formulated for realization in the DT of a project company: direct and reverse. An enlarged approach is proposed to determine the parameters of the digital twin of the project organization when solving the direct problem.
 The final conclusion is that DT methods and tools are necessary for complex analysis, diagnostics and modeling of production, technical and social processes of design companies in the construction industry.

Survey and insights on digital twins design and smart grid’s applications

Digital twins are a promising technology for simulating complex systems, especially in the smart grid domain. This paper offers a comprehensive literature review on digital twins, focusing on data gathering, data management, and human-in-the-loop control design aspects. Emphasizing the integration of AI and machine learning in big data, it enhances analytics and decision-making capabilities. We introduce a collaborative framework involving multiple stakeholders to maximize the potential of digital twins. The paper examines digital twin applications in smart grids, covering areas like asset management, predictive maintenance, energy optimization, and demand response. By synthesizing research and implementation findings, we identify trends, challenges, and opportunities in the field.

Application of digital twin technology in Russia: Development opportunities and constraints

Aim. To identify trends in the development of the digital twin concept in Russia.Objectives. To analyze the experience of applying such an innovative tool as a digital twin at Russian enterprises and consider the benefits of its implementation; to characterize the aspects of regulation and state support in the field of digital twin application in Russia; to reveal the main problems hindering the development of digital twin technology, as well as to propose posible solutions to the identified problems.Methods. The authors used empirical methods, in particular, the analysis and generalization of data from scientific and methodological literature; methods of observation and comparison. The article also reflects the results of scientific research of Russian and foreign scientists.Results. It is established that today the method of digital twins in the economy is becoming more and more popular. Thanks to the introduction of digital twin technology, companies manage to speed up production processes, increase their transparency, reduce costs, improve the quality of manufactured products and, in general, achieve greater efficiency, competitiveness and investment attractiveness. The main constraints to development include insufficient funds required to implement digital transformation, lack of necessary experience and highly qualified specialists, low level of legal framework in the field of digital twins and high level of risks associated with data security. For successful implementation of digital transformation, it is advisable to study and generalize successful examples of digital twins implementation both in foreign and domestic companies, to create a unified management system for this area, to develop a motivation system for companies applying digital twin technology, to increase the level of information security.Conclusions. In Russia, innovative technologies are a priority for ensuring technological leadership and entry of domestic companies into international markets. Despite lagging behind some foreign countries, Russia has great potential for the formation of a national market and the introduction of digital twin technology, especially among large corporations and high-tech enterprises. But for this purpose it is necessary to eliminate the factors that hinder the development of the digital twin method, such as high risks associated with cybersecurity, insufficient funding in the use of technology, the lack of specificity in some cases in the field of legal regulation and a number of others.