Digital Twin Applications Research Articles

Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing

Background The evolution of product expectations in the era of mass personalization implies an improvement and a better control of individualized creation and production processes throughout the product lifecycle. The application of the digital twin seems to be a favoured solution in this context, but its study during the lifecycle of a product has only been partially evoked in the literature. Methods The purpose of this research is to identify the leverages and barriers to support the digital twin diffusion in the manufacturing industry from a technological, operational, and social standpoint. To determine these elements, this paper will identify current digital twins applications in the literature under two main dimensions: the type of digital twin, and its applications along the product lifecycle. To achieve this analysis a systematic literature review was carried out. The publications selection was based on the presence in these of a case of application of a digital twin with a focus in the Manufacturing sector. Within this review, 188 scientific papers were comprehensively compiled and analyzed. Results Results showed that although the term digital twin is widely used, the deployment of digital twin technologies in manufacturing is still at an early stage as most of the reported digital twin applications were in fact prototypes focused on the real-time observability of the physical system, either for optimization or predictive maintenance. Moreover, regarding the product lifecycle, most of the applications have been focused on the production and operational phases whereas those at the design and disposal phases are still limited. Conclusions This paper presents an original approach to the study of digital twins, focusing simultaneously on the type of digital twin, the application area and the lifecycle phase. Under the basis of the obtained results, future perspectives on the use of digital twins along the lifecycle are proposed.

Research on the development and application of digital twin for the full life cycle of bridge engineering

Bridges play an important role in modern society, whereas many bridges are in poor condition and are characterized by complexity, randomness, and risk no matter what phase the bridges are in. Meanwhile, with advanced technologies such as IoT and Big Data, Digital Twin has been developed in different industries, such as aerospace. With multiple superiorities of real-time data detection, big data processing, full life-cycle management, etc., it is appropriate for bridge engineering. The paper aims to present a state-of-the-art literature review on the full life cycle of the application of Digital Twin in Bridge Engineering. The article discusses the relationship between Digital Twin and Building Information Model (BIM) and constructs a comprehensive framework for Digital Twin in Bridge Engineering. The review is classified into three main parts design and construction phase, operation and maintenance phase, and demolition phase based on different stages of the full life cycle. The results show the applications high potential and feasibility, and show that most of the current research focuses on the operation and maintenance phases. However, intensive research is still necessary to improve the accuracy, quality, efficiency, timeliness, and versatility of the Digital Twin in Bridge Engineering.

Integrating FMI and ML/AI models on the open‐source digital twin framework OpenTwins

AbstractThe realm of digital twins is experiencing rapid growth and presents a wealth of opportunities for Industry 4.0. In conjunction with traditional simulation methods, digital twins offer a diverse range of possibilities. However, many existing tools in the domain of open‐source digital twins concentrate on specific use cases and do not provide a versatile framework. In contrast, the open‐source digital twin framework, OpenTwins, aims to provide a versatile framework that can be applied to a wide range of digital twin applications. In this article, we introduce a re‐definition of the original OpenTwins platform that enables the management of custom simulation services and the management of FMI simulation services, which is one of the most widely used simulation standards in the industry and its coexistence with machine learning models, which enables the definition of the next‐gen digital twins. Thanks to this integration, digital twins that reflect reality better can be developed, through hybrid models, where simulation data can feed the scarcity of machine learning data and so forth. As part of this project, a simulation model developed through the hydraulic software Epanet was validated in OpenTwins, in addition to an FMI simulation service. The hydraulic model was implemented and tested in an agricultural use case in collaboration with the University of Córdoba, Spain. A machine learning model has been developed to assess the behavior of an FMI simulation through machine learning.

A full-scale operational digital twin for a water resource recovery facility-A case study of Eindhoven Water Resource Recovery Facility.

Digital transformation for the water sector has gained momentum in recent years, and many water resource recovery facilities modelers have already started transitioning from developing traditional models to digital twin (DT) applications. DTs simulate the operation of treatment plants in near real time and provide a powerful tool to the operators and process engineers for real-time scenario analysis and calamity mitigation, online process optimization, predictive maintenance, model-based control, and so forth. So far, only a few mature examples of full-scale DT implementations can be found in the literature, which only address some of the key requirements of a DT. This paper presents the development of a full-scale operational DT for the Eindhoven water resource recovery facility in The Netherlands, which includes a fully automated data-pipeline combined with a detailed mechanistic full-plant process model and a user interface co-created with the plant's operators. The automated data preprocessing pipeline provides continuous access to validated data, an influent generator provides dynamic predictions of influent composition data and allows forecasting 48 h into the future, and an advanced compartmental model of the aeration and anoxic bioreactors ensures high predictive power. The DT runs near real-time simulations every 2h. Visualization and interaction with the DT is facilitated by the cloud-based TwinPlant technology, which was developed in close interaction with the plant's operators. A set of predefined handles are made available, allowing users to simulate hypothetical scenarios such as process and equipment failures and changes in controller settings. The combination of the advanced data pipeline and process model development used in the Eindhoven DT and the active involvement of the operators/process engineers/managers in the development process makes the twin a valuable asset for decision making with long-term reliability. PRACTITIONER POINTS: A full-scale digital twin (DT) has been developed for the Eindhoven WRRF. The Eindhoven DT includes an automated continuous data preprocessing and reconciliation pipeline. A full-plant mechanistic compartmental process model of the plant has been developed based on hydrodynamic studies. The interactive user interface of the Eindhoven DT allows operators to perform what-if scenarios on various operational settings and process inputs. Plant operators were actively involved in the DT development process to make a reliable and relevant tool with the expected added value.

Supervised multi-regional segmentation machine learning architecture for digital twin applications in coastal regions

Supervised multi-regional segmentation machine learning architecture for digital twin applications in coastal regions

Digital twin enabled cellular network management and prediction

Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.

Enhancing shipyard transportation efficiency through dynamic scheduling using digital twin technology.

Uncertainties, such as road restrictions at shipyards and the irregular shape of blocks, pose challenges for transporter scheduling. Efficient scheduling of multiple transporters is critical to improving transportation efficiency. The digital twin (DT) technology offers numerous benefits, enabling interactions between the virtual and real worlds, real-time mapping, and dynamic performance evaluation. Based on DT technology, this study proposes a dynamic scheduling approach for cooperative transportation utilizing multiple transporters. The scheduling problem for multiple transporters is addressed and modeled in this study, considering factors such as block size and transporter loading. To solve this problem, a framework of DT-based multiple transporters system is established in a virtual environment. By inputting block information into this system, a solution is generated using transporter scheduling rules and interference detection methods. Experimental comparisons are conducted in this paper, exploring various scenarios with different number of tasks and the application of DT. The results demonstrate that the proposed approach effectively enhances transportation efficiency and improves ship construction efficiency. Hence, this study expands the application of DT technology in dynamic scheduling of transportation in shipyards and provides new ideas for shipbuilding company managers.

Virtual testbed for development and evaluation of power system digital twins and their applications

Digital twins (DTs) show considerable promise for development of applications and solutions across a diverse range of power system services. Ensuring these applications function as intended without causing disruptions or unexpected issues requires comprehensive testing prior to practical deployment. However, current approaches to power system digital twin (PSDT) application development and testing experience significant obstacles, including limitations and risks associated with accessing real-world power systems, models and data. This article introduces a virtual testbed based on the concept of a virtual physical twin (VPT), that replaces the need for access to a physical twin (PT), as a platform designed to remove the heavy reliance on online data flows and provide a close to real-world environment. A testbed enables simultaneous development and testing of DTs and their related applications in a realistic, interactive, and safe setting without impact on the operation of the actual power system. A complete workflow for PSDT testbed implementations is proposed, with all necessary steps from concept to the eventual connection to the physical system. To validate the practical implementation steps, utility, and value of the testbed, a real-time transient stability analysis with two artificial intelligence algorithms is provided. The proposed testbed is expandable and adaptable, promoting the development of advanced and intelligent PSDTs. The key benefit of the testbed is that it enables the development of DT and its application closely in a realistic environment without affecting the operation of PT.

Digital twin modeling and leak diagnosis of temperature and stress fields in LNG storage tanks

Temperature and stress serve as crucial indicators for monitoring the health of LNG storage tanks. To address the limitations of traditional point-based monitoring and transition to comprehensive digital monitoring, this paper introduces the application of digital twin (DT) technology. The DT model plays a pivotal role in accurately representing the state of the physical entity and serves as the foundation for monitoring services and meeting application requirements within DT systems. This article introduces a numerical simulation method guided by temperature sensor data. It utilizes a temperature and stress calculation program as a platform to create twin models for real-time assessment of the temperature and stress within the storage tank. By comparing with the Fluent simulation of physical field leakage, the DT model realizes real-time inversion of the temperature field and stress field from sensor data to the whole region. To enhance the diagnostic and predictive capabilities of the DT system, we flatten the computed temperature nephogram from the DT model and employ machine vision techniques to extract cloud map features. Machine learning methods such as support vector machine (SVM) and linear regression are utilized to achieve leakage diagnosis, leakage volume calculation, and leakage location calculation. Additionally, we introduce the sequence-to-sequence (Seq2seq) framework and combine it with convolutional neural networks (CNN) and long short-term memory (LSTM) to train the time series of nephogram, enabling spatiotemporal prediction of leakage areas.

Digital twin applications in an archaeological site: A virtual reconstruction of the Pishan site, Zhejiang, China

A digital twin is a virtual counterpart of a physical object or system based on precise data collection. Although digital twin applications are gaining traction in the virtual reconstruction of built heritage, their relevance in archaeological sites remains limited, especially for those with only foundations. The Pishan (毘山) site in Huzhou, featuring the remains of a high-platform building and a large stilt-style architecture, represents the largest settlement site from the late Shang (商, ca. 1600 – 1046 BCE) and early Western Zhou (西周, 1046 – 771 BCE) dynasties in Zhejiang Province, China. At present, confronting contradictions among preservation, restoration, and reuse as a heritage park, the site leverages digital twin technologies to address two concerns: (i) reconstructing a 3D scene for further restoration and related studies and (ii) integrating multimedia to enhance visitors’ experiences and dissemination. Photogrammetry, unmanned aerial vehicle, and a mirrorless camera are employed to collect sky and ground dual graphic data and reconstruct the 3D model of the loess terrace. A panoramic roaming environment is created through panoramic photography. Geographic information system is integrated to enable visual analysis and information management while building information modeling facilitates the integration of parametric modeling and point cloud, aiding virtual restoration research. In conclusion, a workflow entitled “Virtual Reconstruction – Management and Analysis – Restoration – Exhibition” is proposed, promising in-depth exploration in further studies.

From Pixels to Precision: A Survey of Monocular Visual Odometry in Digital Twin Applications.

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry.

Applications of digital twins in medicine and the ontological model of medical digital twins

The article demonstrates the applications, essence and possibilities of digital twin technology and explains the advantages of digital twin technology and potential challenges. A digital twin is a digital copy that can be used to simulate the status of a physical object or system. The integration of digital twin technologies with the Internet of things, Big Data and Artificial intelligence offers innovative solutions for quicker detection of arisen problems (or problems to arise) related to changes in a real physical object and for making relevant decisions. The development of such solutions in the healthcare is crucial for preserving human health and providing people with higher-quality medical care. It is possible to get an early diagnosis of the disease and the selection of a more effective treatment method on the produced digital twin by transferring the patient’s physical characteristics and changes in his/her body to the digital environment. The article analyzes medical digital twins, categorizing their benefits into patient health, cost reduction, self-management, and other benefits. In order to ensure adaptability and effectiveness in disease therapy and healthcare management decision-making, the patientoriented ontology of healthcare is examined, and a four-level ontological model is suggested to create its digital twin. The creation of a patient-oriented digital twin of healthcare requires the creation of a digital twin of existing physical objects at each level of its ontological model. The creation of digital twin in healthcare opens wide opportunities for making decisions on provision of safe and high-quality medical care to patients.

Современная палитра управленческих средств и механизмов эффективного руководителя

The article considers the dynamics of various managerial approaches, means, methods and mechanisms used in organisational systems in the process of analysis of the activity of enterprises and organisations in order to form adequate managerial decisions by managers of companies of various types of activity. It is shown that the modern “palette” of actual methods, techniques and other means of management of an effective manager, in addition to heterogeneous managerial technologies, should include the latest methods and models created within the framework of automated systems of company management using the means of digitalisation, digital transformation, application of digital twins et al. Aim. The aim of this work is to create a comprehensive set of managerial tools, methods, and mechanisms that an effective ma¬nager should possess when preparing, adopting, implementing, and controlling managerial decisions and their outcomes. Materials and methods. The article presents a comprehensive analysis of managerial approaches, means, technologies, and mechanisms aimed at enhancing the performance of organizational systems. The analysis is based on a significant amount of scientific and technical literature. Results. The authors have developed a series of holistic tools and mechanisms to be used in the process of forming and adopting managerial decisions in organisational systems. Conclusion. The article argues that an effective manager should possess a diverse set of approaches, techniques, methods, and mechanisms to manage all processes and areas of activity in various enterprises and organizations. This includes the use of automated management systems, digitalization, and digital transformation, as well as the introduction of digital twins to improve organizational systems.

LSTM‐based post‐processing for noise reduction in SVD‐based particulate matter digital twinning on lightweight embedded devices

AbstractAmong various applications of digital twins, measuring particulate matter in the context of air pollution has become increasingly important due to growing interest in atmospheric environments. By obtaining particle count values from analogue‐to‐digital converter raw data through photodiode and calibrating them, particulate matter values can be acquired. The singular‐value–decomposition is efficient transfer function learning method. However, due to the linear nature of the transfer function of singular‐value–decomposition, it also conveys noise information, necessitating post‐processing to suppress noise. This study proposes a method that utilizes long short‐term memory neural networks to effectively stabilize noise based on the output of continuous dust sensor particulate matter distributions. Using the singular value decomposition based particulate matter shows relative root‐mean‐square error (4.4761, 100%) as a reference, the proposed long short‐term memory post‐processing demonstrates an improved result (2.9328, 65.52%) compared to other post‐processing method: the mean filter (3.6704, 82.00%), low‐pass filter (3.7719, 84.27%) and Kalman filter (3.5550, 79.42%). Furthermore, to address the limitation of initial delay in long short‐term memory to achieve stable data output, a method of iteratively training the initial input data sample is proposed. For the initial input data, the non‐initialization approach exhibited a relative error of 87.57% and it took around 5 samples to predict stable output. In contrast, the proposed iterative training method, applied five times to the initial data sample, achieved a relative error of 9.51% and immediately obtained stable data.

A Review on Digital Twin Application Research for Urban Water Security

A Review on Digital Twin Application Research for Urban Water Security

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

AbstractDigital twin, as an important enabling tool for digital transformation, has received increasing attention from researchers and practitioners since its definition was formalised. Especially in the global context and exacerbated by Covid-19, the applications of the digital twin have offered opportunities for many industries. While the digital twin has already been widely used in many sectors such as manufacturing and the construction industry—one of the key engines of economic development, is still lagging behind many other sectors. This study uses the systematic literature review to assess the applications of digital twin in manufacturing and construction respectively, the benefits it brings, and the impediments to its application. Based on this, a comparison is made of digital twin applications in the manufacturing and construction industries to draw lessons. This study concluded that although the use of digital twin in manufacturing is better than construction overall, it is still not reaching its full potential. Despite many benefits brought by the digital twin to construction during the project lifecycle, the construction sector faces even greater challenges than manufacturing in digital twin adoption. By comparison, this study drew five lessons to drive better adoption of the digital twin. The construction industry needs to accelerate the deployment of relevant hardware, promote the standard unification of digital twin, explore the whole lifecycle application of the digital twin, enhance data protection, and embrace changes. This study was limited in the scope of data collection. Future research could focus on gathering information from specific case studies, to produce more comprehensive perspectives.

An enhanced state-aware model learning approach for security analysis in lightweight protocol implementations

Owing to the emergence and rapid advances of new-generation information and digitalization technologies, the concept of model-driven digital twin has received widespread attentions and is developing vigorously. Driven by data and simulators, the digital twin can create the virtual twins of physical objects to perform monitoring, simulation, prediction, optimization, and so on. Hence, the application of digital twin can increase efficiency and security of systems by providing reliable model and decision supports. In this paper, we propose a state-aware model learning method to simulate and analyze the lightweight protocol implementations in edge/cloud environments. We introduce the data flow of program execution and network interaction inputs/outputs (I/O) into the extended finite state machine (EFSM) to expand the modeling scope and insight. We aim to calibrate the states and construct an accurate state-machine model using a digital twin based layered approach to reasonably reflect the correlation of a device’s external behavior and internal data. This, in turn, improves our ability to verify the logic and evaluate the security for protocol implementations. This method firstly involves instrumenting the target device to monitor variable activity during its execution. We then employ learning algorithms to produce multiple rounds of message queries. Both the I/O data corresponding to these query sequences and the state calibration information derived from filtered memory variables are obtained through the mapper and execution monitor, respectively. These two aspects of information are combined to dynamically and incrementally construct the protocol’s state machine. We apply this method to develop SALearn and evaluate the effectiveness of SALearn on two lightweight protocol implementations. Our experimental results indicate that SALearn outperforms existing protocol model learning tools, achieving higher learning efficiency and uncovering more interesting states and security issues. In total, we identified two violation scenarios of rekey logic. These situations also reflect the differences in details between different implementations.

Digital Twins for More Precise and Personalized Treatment.

The use of Digital Twins (DTs) or the digital replicas of physical entities has provided benefits to several industry sectors, most notably manufacturing. To date, the application of DTs in the healthcare sector has been minimal, however. But, as pressure increases for more precise and personalized treatments, it behooves us to investigate the potential for DTs in the healthcare context. As a proof-of-concept demonstration prior to working with real patients, we attempt in this paper, to explore the potential for creating and using DTs. We do this in a synthetic environment at this stage, making use of data that is all computer-generated. DTs of synthetic present patients are created making use of data of synthetic past patients. In the real world, the clinical objective for creating such DTs of real patients would be to enable enhanced real-time clinical decision support to enable more precise and personalized care. The objective of the numerical experiment reported in this paper, is to envisage the possibilities and challenges of such an approach. We attempt to better understand the strengths and weaknesses of applying DTs in the healthcare context to support more precise and personalized treatments.

Energy digital twins in smart manufacturing systems: A case study

Among core Industry 4.0 technologies, the Digital Twin represents a promising smart technology and tool that researchers are investigating to help improve energy efficiency at the shopfloor level by analyzing and optimizing energy consumption. Energy Digital Twins are a relatively new area of research, only gaining popularity in industry applications and academia recently. where a comprehensive literature review uncovered only one ‘true’ Energy Digital Twin application across published research to date. To address this research gap, an Energy Digital Twin for smart manufacturing systems was developed and evaluated in this study. In particular, the study focused on bidirectional parameter communication between the physical and the virtual part with the aim of optimizing the energy used in the manufacturing process. To address this research gap, the research objective is to create and evaluate an application of an energy optimizing Digital Twin for a Heating Tunnel. Following the definition of a Digital Twin, the research methodology and experimental setup have three major components: i) the Heating Tunnel as the physical object, ii) the digital counterpart constructed using Python to house the digital control logic and linear energy optimization feedback model, and iii) the connecting fabric, in form of a bidirectional OPC UA communication protocol. The optimization model ingests input parameters of setpoint temperature, power level, and targeted overshoot time, and after running the simulation, returns a calculated value of the required turn off temperature to the real-time heating process of the physical system. Results show that the Energy Digital Twin is effective at maintaining the maximum temperature range of the Heating Tunnel during the heating process, in addition to reducing the energy consumption and cost for all trial runs compared to the original process. Overall, the study successfully created and evaluated a functioning energy optimizing Digital Twin with bidirectional, automated feedback. The results of this research emphasize the potential impact of Energy Digital Twin applications in any manufacturing process and show the promise of future work in this realm of Energy Digital Twins.

Multiscale numerical modeling of large-format additive manufacturing processes using carbon fiber reinforced polymer for digital twin applications

Large Format Additive Manufacturing (LFAM) has gained prominence in the aerospace and automotive industries, where topology optimization has become crucial. LFAM facilitates the layer-by-layer production of sizeable industrial components in carbon fiber (CF) reinforced polymers, however 3D printing at large scales results in warpage generation. Printed components are deformed as residual stresses generated due to thermal gradients between adjacent layers. This paper tackles the problem at two different scales: the micro and macroscale. Initially, the microstructure characterization of the thermoplastic ABS matrix composite material enriched with 20% short CF is used in the development of numerical models to understand the mechanical behavior of the studied material. Numerical modeling is performed simultaneously by means of Mean-Field (MF) homogenization methods and Finite Element Analysis (FEA). Outcomes validated with corrected experimental mechanical testing results show a discrepancy in the elastic modulus of 7.8% with respect to FE multi-layer analysis. Micro-level results are coupled with the a macroscopic approach to reproduce the LFAM process, demonstrating the feasibility of the tool in the development of a Digital Twin (DT).