Review of digital twin about concepts, technologies, and industrial applications

Developments in virtual technology and data acquisition technology put way to digital twin (DT) technology. Digital twin is a virtual entity that is linked to a real-world entity. Both the link and the virtual representation can be realized in several different ways. Digital Technology plays a very much key role in different areas like in production management, manufacturing, health care, smart cities and so on. Mainly Digital Twin Technology is developed to improve manufacturing processes. With the development of new-generation information and digitalization technologies, more data can be collected, and it is time to find a way for the deep application of all these data. As a result, the concept of digital twin has aroused much concern and is developing rapidly. Digital twins facilitate to monitor, understand, and optimize the functions of all physical entities and for humans and also provide continuous feedback to improve quality of life and well-being. Digital Twin is best described as the effortless integration of data between a physical and virtual machine in either direction. This paper provides an overview of the Digital Twin technology used in different work spaces and also how it will be effective in the Internet of Things network.

The widespread adoption of Building Information Modeling (BIM) and the recent emergence of Internet of Things (IoT) applications offer several new insights and decision-making capabilities throughout the life cycle of the built environment. In recent years, the ability of real-time connectivity to online sensors deployed in an environment has led to the emergence of the concept of the Digital Twin of the built environment. Digital Twins aim to achieve synchronization of the real world with a virtual platform for seamless management and control of the construction process, facility management, environment monitoring, and other life cycle processes in the built environment. However, research in Digital Twins for the built environment is still in its nascent stages and there is a need to understand the advances in the underlying enabling technologies and establish a convergent context for ongoing and future research. This paper conducted a systematic review to identify the development of the emerging technologies facilitating the evolution of BIM to Digital Twins in built environment applications. A total of 100 related papers including 23 review papers were selected and reviewed. In order to systematically classify the reviewed studies, the authors developed a five-level ladder categorization system based on the building life cycle to reflect the current state-of-the-art in Digital Twin applications. In each level of this taxonomy, applications were further categorized based on their research domains (e.g., construction process, building energy performance, indoor environment monitoring). In addition, the current state-of-art in technologies enabling Digital Twins was also summarized from the reviewed literature. It was found that most of the prior studies conducted thus far have not fully exploited or realized the envisioned concept of the Digital Twin, and thus classify under the earlier ladder categories. Based on the analysis of the reviewed work and the trends in ongoing research, the authors propose a concept of an advanced Digital Twin for building management as a baseline for further studies.

The digital twin (DT) concept has a key role in the future of the smart manufacturing industry. This review paper aims to investigate the development of the digital twin concept, its maturity and its vital role in the fourth industrial revolution. Having identified its potential functionalities for the digitalisation of the manufacturing industry, the digital twin concept, its origin and perspectives from both the academic and industrial sectors are presented. The identified research gaps, trends and technical limitations hampering the implementation of digital twins are also discussed. In particular, this review attempts to address the research question on how the digital twin concept can support the realisation of an integrated, flexible and collaborative manufacturing environment which is one of the goals projected by the fourth industrial revolution. To address this, a conceptual framework supporting an integrated product-process digital twin for application in digitised manufacturing is proposed. The application and benefits of the proposed framework are presented in three case studies.

The objective of this paper is to provide new insights and best practices towards the digital twin concepts, particularly in the oil and gas sector and the potential benefits towards achieving the operational excellence and sustainability goals. By using the digital twin technology for asset management, reservoir management, process optimization and training and simulation, the operating companies not only improve efficiency, reduce costs, and achieve operational excellence, more importantly it can also optimize the operations and reduce GHG emissions. The digital twin concept helps in creating a digital replica of the physical assets and the associated processes. This technology can transform the way the oil and gas industry operate, enabling to achieve the operations excellence. As every field produces enormous amount of data which comes from sensors and field automation, the same can be leveraged through a digital twin that can support right from the conceptual design and various other phases of a field development capital project till operations and maintenance up to the retirement of the field and its associated assets. This paper looks in detail at the various approaches wherein the operating companies with their digital information and management system at organization level, can augment such Enterprise level digital twin methodologies. It also addresses the various process of Statement of Requirements (SOR), Data sources analysis, Modelling of the digital twins, Scenario analysis, and Implementation and continuous monitoring and improvement. This paper utilizes the various lessons learned and real time scenarios correlated with specific applications in the upstream oil and gas facilities. The results perceived, noticeably identify the importance for adopting the approach in a structured way rather than starting at the random stages while arraying the Digital twin journey. It further scrutinizes the digital twin solution architecture which can fit within the current organization wide enterprise domain, and also the latent and resultant advantages of implementation within premises or in a cloud environment. Another key aspect in this strategy was to develop the requirement standard for the Digital twin at enterprise level for existing assets and new development fields. In addition, it highlights the application specific scenarios which can be considered prototypical in implementation of this technology in the oil and gas industry having the potential to improve operational and sustainability performance and minimize environmental impact. Lastly, it concludes with the accompanying challenges to ensure that the digital twin solution is cyber secure and compliant while integrating with existing / new control systems such as DCS/ICSS and ERP to provide a comprehensive view of the physical entity's performance and to support effective decision-making. The practice of digital twin technology in the oil and gas industry is a relatively innovative area of research and development, and there is still much to be learned about how to effectively implement this technology and leverage it to achieve sustainability goals. While there is some existing literature on the use of digital twin technology, much of this paper has focused on the digital twin solution in a comprehensive platform leveraging machine learning algorithms that can connect with a plenty of data sources to provide real-time visibility into asset performance, enabling them to optimize operations, reduce emissions, and better manage environmental risks.

Digital twin is driven by multi-dimensional virtual model and fused data, through virtual and real closed loop interaction, to achieve monitoring, simulation, prediction, optimization and other practical functional services and application requirements. At present, there are three problems in digital twin, and the concept of digital twin is controversial. Digital twin lacks a practical model-device data communication method. There are few applications of digital twin, especially in the field of AUV. Based on the above three problems, this paper first summarizes the concept and applications of digital twin. Then, an center server data transfer framework is proposed for model to device data communication. Finally, the application method of digital twin in AUV is discussed.

In today's world, digital transformation is becoming increasingly crucial in industry. The Digital Twins and simulations become much more important with growing complexity of products, the need to accelerate product development processes while reducing costs. The increasing value of Digital Twins is prompting organizations in the aviation industry to actively utilize them in research and development efforts. However, due the idea of difficulties of using Digital Twins in the industries, product developers don’t have a clear idea where to begin. This article provides readers with a general overview of the current situation by offering an examination of the aviation sector. Initially, the concept of Digital Twin, its evolution, and relevant studies are explored, followed by an examination of the impact of creating Digital Twins for companies operating in aviation on the product development process. In this study, unlike other published works, the concept of Digital Twin is explained under three subheadings. These include the Design Digital Twin for design steps, the Production Digital Twin for manufacturing processes, and the Performance Digital Twin for post-delivery work related to the product. The final section of the study evaluates the contribution of Product Lifecycle Management (PLM) systems to the process of creating Digital Twins and the advantages they provide to companies in product development processes. The present research work does not contain any studies performed on animals/humans subjects by any of the authors.

Biopharma Is Going Digital … Bit by Bit

Product Customization based on Digital Twin and Cloud Manufacturing within a Decentralized Production System

Digital twin application in the construction industry: A literature review

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.

A digital twin is a simultaneous digital reflection of object processes and states. Digital twins are usually made of objects that exist in reality or which are very near completion in a design and production process. In our research, we investigate the potential of digital twin technology for early design. Key to the early application of digital twin in design is the role of information and simulation. Since design information is valuable for predicting the future of design, we assume that design will begin to change as digital twin technologies become more and more adaptable, as designers simultaneously have digital twins of the past, present, and future. Digital twin technologies have many capabilities to support the design process at various stages from concept design to the final design. Throughout this process, architects use digital and physical models. Combined with digital twin technology, these models form what we call hybrid prototypes. Estimating that simulation has a vital impact on the design process, we raised the question of what the potential of architectural hybrid prototyping in design processes with digital twin technologies is. Similar to the development of the design through increasingly informed and detailed models, we think that the closest thing to the design process with the digital twin is the so-called foetal, child, and adult digital twin. Based on this classification, we approach the concept of hybrid prototyping and digital twin.

Many studies have been made on the attitudes of Digital Twin, Industrial Internet of Things and cloud services. Indeed, there are positive results in the application of new ways of using these technologies. But separately each of these technologies is limited by its capabilities. So there is a way to use them in collaboration. Computer numerical control (CNC) machines and sensors, Industrial Internet of Things platform and Digital Twin on the one hand, cloud computing on the other. The synergistic effect of the application of those technologies provides new opportunities and could increase manufacturing efficiency. In this research Digital Twin works with IIoT platform Winnum combined with cloud services came to a concept of Digital Twin as Service. The research shows development from classical Digital Twin model to modernized model with a concept of Digital Twin as Service.

'Digital twins', also called 'digital patient twins' or 'virtual human twins' - digital patient-specific models derived from multimodal health data - are a strong focus in health care and are emerging as a promising tool for improving personalized care in uro-oncology. These models can integrate clinical, genomic, imaging and histopathological information to simulate organ behaviour and disease progress as well as predict responses to treatments. The concept of digital twins has shown potential in various fields, but its application in uro-oncology is still evolving, with few assessments of their feasibility and clinical utility. The advent of artificial intelligence adds a new dimension to their development, potentially enabling the synthesis of diverse, high-quality datasets to improve modelling accuracy and support real-time decision-making. However, substantial challenges exist, including data integration, patient privacy, computational demands and ethical frameworks. In addition, the interpretability of predictions remains essential for gaining clinical trust and guiding patient-centred decisions. The use of digital twins in uro-oncology has the potential to improve patient stratification and treatment planning; however, barriers must be overcome for their successful implementation in clinical routine. By integrating new technologies, fostering interdisciplinary collaboration and prioritizing transparency, digital twins could shape the future of precision uro-oncology.

Digital twin has recently received considerable attention in various industry domains. The digital twin replicates physical objects (e.g., people, objects, spaces, systems, and processes) in the real world into digital objects in the digital world. It also provides various simulations to solve problems in the real world or to improve situational operations. Therefore, the digital twin is a convergence of various technologies, such as advanced machine-learning algorithms, data analytics, super-resolution visualization and modeling, and simulation. Because the digital twin is a complicated technology, a step-by-step implementation that includes many technology elements should be considered to create a digital twin model. In this study, implementation layers are introduced to guide practical implementations of the digital twin. In addition, technology elements were suggested for the presented implementation layers. Because the suggested technology elements include clear technology definitions, various application domains (e.g., energy, transportation, logistics, environment, manufacturing, and smart cities) can easily utilize the introduced implementation layers and technology elements according to the intended purpose. Furthermore, this paper describes the evolution of digital twins. Digital twin technology has evolved continuously since 2002, when the digital twin concept was first introduced. In the described evolution levels, we show the future aspects of digital twin technology, according to the technological evolution direction. Therefore, the digital twin model can be efficiently created by considering the evolution direction and future aspects by using the suggested digital twin evolution levels.

Digital twin technology, aligned with Industry 4.0 standard, has witnessed widespread adoption in various industries, notably in manufacturing. Meanwhile, the concept of digital twin itself is yet to be clearly defined in wind farm sector. Our primary contribution lies in investigating the potential for transferring knowledge of industrial digital twins for the wind farm industry. Through a comprehensive literature study, we explored the digital twin concept within the context of wind farm applications. Also, we conducted a comparative analysis of digital twin frameworks employed in wind farm and manufacturing sectors. We aim to identify commonalities and differences between these frameworks and to determine how they could be adapted to the unique requirements of the offshore wind sector. A case study is presented, wherein the Industry 4.0 standard framework, Asset Administration Shell (AAS), is conceptually applied to the wind farm sector. Additionally, we briefly explored the AASX Package Explorer and concluded that implementing the AAS could be a promising option for enhancing digital twin functionality in offshore wind farms, and for achieving interoperability in line with Industry 4.0 standard.