Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, Biotechnology, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. By introducing the technology and development process of digital twinning, this paper puts forward a model for real-time analysis and monitoring combined with the establishment method of digital twinning model of transformer electromagnetic and thermal field in power system and the technologies of Internet of things, cloud computing and big data.

Abstract. A Digital Twin is a virtual representation of a physical asset or system with the purpose of optimizing intelligent behaviour of said physical entity. Digital Twin is a promising tool for asset management as the virtual entity can exist and aid at every stage of a systems life. However, the infancy of the concept means implementation remains at an early stage and particularly poorly defined within an asset management context. Practical case studies of digital twinning (the modelling process of generating and updating Digital Twins) are an important tool to ensure definitions from research are applied rigorously and to aid in their deployment with practitioners in real industrial applications. This-being-said, there are insufficient case studies for asset management digital twinning. In particular, the Digital Twinning process for utility-scale solar has not been considered. Utility-scale solar asset management often suffers challenges due to remoteness and scale of assets, contributing to high labour costs and thus could benefit enormously from an effective Digital Twin to increase precision and accuracy of fault detection and efficiency of labour for O&M tasks. In addition, the data sharing and analysis Digital Twins provide is vital for the immature solar sector. However, Digital Twinning of utility-scale solar has not been well considered and presents issues around cost-effective data collection and modelling. Therefore, this paper details the current state-of-the-art and challenges surveying utility-scale solar and the progress and application of Digital Twin to utility-scale solar. Then a novel proof of concept process for digital twinning of utility-scale solar is presented with a focus on geometric data capture for updating as-is models. Furthermore, the paper will consider Digital Twin requirements and their prescription to current O&M methods in utility-scale solar. Finally, the paper highlights currently available required technology as well as highlighting future technological improvements that would benefit the proposed proof of concept.

In current practice, digital twins are often created with a set of perspectives in mind. This makes it difficult to separate the content of its structure, which results in less tailored use of the digital twin. Next to that, it becomes hard to utilise the information supplied by the digital twin as different users have different perspectives, and therefore expect and need different information to make decisions. Digital twins can be seen as recursive, which means that a digital twin consists of multiple digital twins with different granularity and functionality. The modularity of digital twins creates a hierarchy between these digital twins. In current practice, this implies that digital twins may encompass and thus prevail over others.This article proposes an autorarchic approach to digital twinning, which can be used to make informed decisions and determine the impact of modules based on their dependence of and relation to other modules. Additionally, this approach allows for the creation of tailored digital twin hierarchies (autorarchies) for specific users, thus creating instantiated and controlled digital twins.

The paper assesses the impact of digital twinning (DT) on health service quality (HSQ) and sustainable competitive advantage (SCA) at private hospitals in Egypt. Data were collected from 300 of 69,650 employees at private hospitals in Egypt. A questionnaire was posted on private hospitals’ intranet and could be accessed by clicking on a link in the e-mails. Participants consisted of hospital employees who completed a questionnaire that assessed the relationship of DT with HSQ and SCA. The results showed DT has a strong relationship with HSQ and SCA. Specifically, there is a positive link between HSQ and DT dimensions (DT goals, policies and legislation for DT, digital infrastructure, trained human resources, electronic communities, and obstacles to applying). Also, there is a positive relationship between the dimensions of DT and SCA. The research provides an explicit relationship for DT with other variables in the organization. The findings contribute to a better understanding of DT’s influence on HSQ and SCA. DT acts as an important tool for enhancing HSQ and SCA. The paper promotes understanding the dimensions of DT, HSQ, and SCA. The study uses DT dimensions as independent variables, with HSQ and SCA as dependent variables, which is new. In this study, a model has been built to analyze the relationship between DT, HSQ, and SCA. DT is a groundbreaking technology that creates virtual replicas of physical objects, systems, and even processes. The DT’s benefits are far-reaching. By simulating real-world scenarios in a virtual environment, managers can test, analyze, and optimize their operations without risks and costs associated with physical prototyping. Digital twins can also be used to monitor and predict the performance of complex systems, such as industrial equipment or entire cities. Additionally, digital twinning can facilitate collaboration and knowledge-sharing among stakeholders to make data-driven decisions and drive innovation. The DT implications are significant, with potential applications across various industries: healthcare, manufacturing, energy, and transportation. For instance, digital twins can be used to create personalized models of patient anatomy for more accurate diagnoses and treatments. In manufacturing, digital twins can optimize production processes, reduce waste, and improve product quality. However, the widespread adoption of DT also raises important questions about data ownership, security, and ethics. As digital twins become increasingly sophisticated, there is a growing need for standards and regulations to ensure that they are developed and used responsibly. Moreover, the integration of DT with other emerging technologies, such as artificial intelligence and the Internet of Things (IoT), will require careful consideration of the potential risks and benefits, as well as the development of new business models and partnerships.

Smart cities in the time of climate change and Covid‐19 need digital twins

An intelligent digital twinning approach for complex circuits

Digital twins are a new paradigm for our time, offering the possibility of interconnected virtual representations of the real world. The concept is very versatile and has been adopted by multiple communities of practice, policymakers, researchers, and innovators. A significant part of the digital twin paradigm is about interconnecting digital objects, many of which have previously not been combined. As a result, members of the newly forming digital twin community are often talking at cross-purposes, based on different starting points, assumptions, and cultural practices. These differences are due to the philosophical world-view adopted within specific communities. In this paper, we explore the philosophical context which underpins the digital twin concept. We offer the building blocks for a philosophical framework for digital twins, consisting of 21 principles that are intended to help facilitate their further development. Specifically, we argue that the philosophy of digital twins is fundamentally holistic and emergentist. We further argue that in order to enable emergent behaviors, digital twins should be designed to reconstruct the behavior of a physical twin by “dynamically assembling” multiple digital “components”. We also argue that digital twins naturally include aspects relating to the philosophy of artificial intelligence, including learning and exploitation of knowledge. We discuss the following four questions (i) What is the distinction between a model and a digital twin? (ii) What previously unseen results can we expect from a digital twin? (iii) How can emergent behaviours be predicted? (iv) How can we assess the existence and uniqueness of digital twin outputs?

This chapter discusses the need and merit for a possible alternative view on digital twinning. First, the potential that the digital twin will not be IFC-based; in fact, the possibility that the digital twin will not be based on any common model of data. Second, and consequently, the fact that the process of digital twinning becomes more important than the digital twin itself. The arguments for these are based on the very role and definition of digital twins. In this chapter, they are not just a repository of data. In addition to data, a digital twin includes two major elements: representation of workflows, which are needed for supporting an automated/algorithmic operation; and simulation models of, for example, energy management and/or user comfort scenarios. Such data will span structured and unstructured data; building, operator, and user-generated data; historical, real-time, and simulated-futures data. Consequently, the hard-to-achieve interoperability within the traditional structured BIM data will be impossible. Of course, BIM will be a major component in any digital twin, but not the core. The role of a digital twin, in this context, is to discover knowledge: learning what the data is telling us; supporting predictive analysis. This stands in sharp contrast to IFC mentality: compliance to a pre-defined model of knowledge; and achieving interoperability. To this end, the process of composing, analysing and learning from digital twin data becomes the key contribution. We first present our proposition for predictive digital “twinning”, the business case for their existence and value. We situate that against recent advances in IFC-based twinning and, equally important, the criticism for IFC (model-driven) mentality. To illustrate the arguments made, we showcase an ongoing digital twinning project at the University of Toronto. It is built on top of a non-IFC legacy system for building automation. A no-model architecture is proposed to achieve the following: adding BIM to the digital twin as a component, not as the core element; using machine learning to discover patterns and support predictive analysis and business intelligence applications; and engaging all stakeholders in the learning process.

Digital twins are virtual counterparts of objects throughout their entire life cycle. Primarily adopted by NASA, today are a crucial component of the ongoing digital transformation. Civil engineering has spotted the benefits coming from digitization, but the current stage of technological adoption is still in its infancy. Digital twinning can empower the automation and efficiency of civil engineering and prepare it for future challenges. However, for the growth of the digital twinning idea, extended research and practical implementations are needed. Current trends in civil engineering, such as Building Information Modeling and Structural Health Monitoring, prepare civil engineering to adopt the concept of digital twins. Nevertheless, to fulfill the requirements of and benefit from multi-industrial digitization, civil engineering must evolute its methodologies in the digital twinning fashion. Reviewed examples of implementations (focusing on concrete bridges) show practical benefits for automating processes and increasing the resilience of structures. The article describes the key components of a proposed bridge digital twin framework: BIM, SHM, and Artificial Intelligence, and explains their roles in the digital twinning concept.

Digital twins combine modelling, domain knowledge, computing power, and multiple datasets to offer the potential to unlock new insights into biodiversity (de Koning et al. 2023). The Biodiversity Digital Twin (BioDT) project pioneers this approach to aid in understanding biodiversity through prototyping digital twins (Golivets et al. 2024). However, working with biodiversity data presents challenges due to their dynamic and diverse nature as well as the need for having to deal with incompleteness, uncertainties (Rocchini et al. 2011), disproportionate representation patterns in global studies from wealthier economies (Hughes et al. 2024), and issues with data aggregation and integration (Wüest et al. 2020). Similar to BioDT, there are also plans for creating a Digital Twin of the Ocean (DTO). DTO-Bioflow project addresses these data challenges in the marine domain, where studies show that although European seas host 48,000 marine species (75% described), the data are not yet FAIR (Findable, Accessible, Interoperable, and Reusable) (Ramírez et al. 2022). These challenges hamper the modelling capabilities needed for effective predictions and conservation prioritisation. The adaptability of digital twins across temporal and spatial scales and their ability to model dynamic ecosystems make them ideal for biodiversity research and real-time conservation efforts. However, their success hinges on the consistent integration and alignment of data from disparate sources (Trantas et al. 2023). This integration involves standardising terms used to describe datasets, such as temporal coverage or controlled vocabularies like "Forest" for targetHabitatScope. Thus, adopting data standards is essential. Additionally, challenges such as model bias (Lewers 2023), research context, and data provenance must be considered, adding complexity to metadata capture and alignment. BioDT addresses these challenges with modular building blocks for data integration, model deployment, and workflow management. This approach facilitates the gradual adoption of data standards and FAIR principles, which need to encompass not just data, but also models and software. As automation, ease of reproducibility, and deployability are critical for digital twinning success, data integration and interoperability issues may arise due to missing or insufficient parameter descriptions in the model, incomplete information on data selection, and the unavailability of required software package details. Thus, data standardisation provides a pathway for a consistent approach that can be adopted for different use cases. Common data sources in BioDT, like species occurrences and environmental variables, benefit from standards such as Darwin Core (Wieczorek et al. 2012) and the Ecological Metadata Language (Jones et al. 2019). While valuable, these standards may not fully encompass the complexity needed for comprehensive biodiversity digital twins. Additionally, differing familiarity with these standards and FAIR principles among communities pose challenges. Continuous adoption of data standards, alongside exploring complementary approaches like schema.org or bioschemas.org for capturing diverse (meta)data, is essential. Collaboration with data providers, modellers, and various research infrastructures is also crucial (Andrew et al. 2024). We share our experience using Research Object Crate (RO-Crate), leveraging common JavaScript Object Notation for Linked Data (JSON-LD) representation for metadata profiles and workflow representation, to connect with different infrastructures. In the BioDT project, we are working with various use cases to create prototype digital twins that can serve as valuable resources for other projects. The evolving landscape of digital twin concepts, along with other European Union-funded initiatives like DTO-Bioflow and Destination Earth (DestinE), emphasises the importance of alignment within the digital twin ecosystem. BioDT is committed to aligning with and contributing to this broader context, highlighting the critical role of data standardisation and FAIR implementation.

February 2026 This report provides a technical overview of how digital technologies are being applied across the PV value chain, from manufacturing to operation and maintenance. It highlights the role of digital twins – virtual representations of PV systems updated with real-world data – in supporting performance analysis, predictive maintenance and informed decision-making throughout a system’s lifecycle. The report emphasises the importance of robust, standardised data models and interoperable data structures as a foundation for effective digitalisation, alongside the growing use of artificial intelligence (AI) and Internet of Things (IoT) technologies. It also stresses that cybersecurity must be addressed at all levels as PV systems become increasingly data-driven and interconnected. Key Findings · Digitalisation significantly contributes to risk analysis in PV projects, allowing stakeholders to quantify and mitigate risks associated with component failures, design flaws, and environmental factors. · The emphasis on the digital twin as a core concept signals its potential to revolutionise how PV systems are designed, operated, and maintained, ultimately contributing to the sector’s growth and sustainability in the energy transition. · Two approaches to digital twinning are discussed: physics-based digital twins, which use physical models to simulate behaviour, and data-driven digital twins, which rely on real-world data to model system performance. · The integration of artificial intelligence (AI) and the Internet of Things (IoT) are key components in optimising operations and maintenance (O&M) processes for PV systems. Supporting Reliable and High-Performance PV Systems By providing harmonised definitions, technical frameworks and examples of digital applications, the report supports stakeholders involved in the design, operation and maintenance of PV systems. It contributes to a deeper understanding of how digitalisation and digital twins can strengthen the reliability and performance of PV systems and support the long-term development of the PV sector.

Optimization of cooling rate of Q-P treated 42SiCr steel using AI digital twinning

A development of physics-based digital twinning of a generic jack-up platform is presented in this paper. Due to lack of field measurement data, a generic large-scale jack-up model was designed, fabricated and tested in TCOMS ocean basin at 1:30 scale under different configurations, with the objective to provide high-quality datasets to validate the proposed digital twin methodologies. The framework and the performance of the digital twin are demonstrated using a realistic and representative basin-scale model as a proof-of-concept. Fundamental to any physics-based digital twins is the establishment of numerical models capable of reproducing consistent behaviors and responses of the physical assets. For this digital twin development, a full order model (FOM) and a reduced order model (ROM) are established. In view of uncertainties associated with the physical asset and numerical modelling, e.g., foundation fixities, leg stiffness, leg-hull connection stiffness and hydrodynamic coefficients, model updating or system identification is performed using the ROM to identify the parameters with relatively large uncertainties. A mapping between the parameters and the associated responses of the FOM and the ROM is subsequently established. After the model updating is completed with the identified parameters, good agreement in terms of the structural responses between the model test and numerical results can be achieved. Both the FOM and ROM are able to reproduce structural responses with good accuracy when compared to physical measurements. The ROM, being a linear structural model based on modal responses, is unable to account for larger non-linear effects due to spudcan fixities, if any. Nevertheless, the ROM is suitable for fatigue evaluation considering fast computational speed and validity of the piecewise linear constraints as assumed for the foundation. The FOM, being less computationally efficient, is suitable for strength evaluation and able to account for any non-linear structural behaviors. The results of boundary displacements from the global dynamic response analysis can be mapped to a detailed local joint model to derive the hotspots stress for a more accurate fatigue evaluation. The digital twin framework for fatigue and strength evaluations based on measured wave loading is demonstrated for a better structural integrity management. As an emerging technology, digital twin will provide visibility of structural health condition to facilitate the transition from preventive to predictive and reliability-centered maintenance strategies. Although the digital twin framework presented in the paper makes use of a representative jack-up at model-scale, the proposed methodology can be potentially applied to full-scale operating jack-ups.

The energy consumption during the operation and maintenance phase of buildings is huge. As the built-up area in China increases, the demand for energy conservation in existing buildings has become a key focus of its dual carbon policy. Intelligent operation and maintenance based on digital twins is an emerging means to reduce carbon emissions from buildings, but it faces some problems in the process of promotion. Complete digital and intelligent transformation requires significant investment and has certain requirements for project parties and operation and maintenance teams. Small businesses or individual households have relatively simple requirements for intelligent operation and maintenance scenarios and do not require complete digital twins. To address the above issues, this article uses an affordable universal digital twin framework to provide a digital solution for intelligent operation and maintenance of existing buildings. This solution allows networking communication between devices and uses IoT modules to monitor and control the environment. This digital twinning model can reduce the measurement and control of energy-consuming end devices without on-site transformation and has rich scalability. This article uses the solution to deploy an office at a university in Henan Province and specifically measures the power consumption of displays, indoor environment, and air conditioning. According to the needs, it expands the space occupation, fans, air handlers, lights, and other end devices of the digital twin. The digital twin accurately presents the energy consumption of the office during extreme weather conditions, which has an auxiliary role in promoting digital twins in the region and optimizing energy consumption in existing buildings.

Digital twins – realistic digital representations of physical entities, connected by real-time data flow – have spread into varied sectors of society. One sector that deserves further attention is the digital twinning of urban areas. The quick deployment of digital twins of cities and their interactions with the material and social elements of the “smart city” have been enabled by discourses of technological optimism and the hope that smart cities can allow for solutions to “hard problems” like the climate emergency. However, the social and cultural values that underpin the creation and practice of these technologies are not yet well understood. In this essay, I tell the story of Joakim, a man who has created a digital twin of his small hometown. The direction he has taken with his digital twin, one which he believes takes his project beyond the limits of city-centric twins, is guided by his notions of community, democracy and sustainability. Attention to small-scale and grassroots digital twins can help us to reconsider the affordances of digital twins: the ways that they can create multiple representations of reality and can allow users to make actionable predictions. Given digital twins’ increasing importance in the governance of urban areas, a vital first step in becoming more literate in our interactions with the technology is to better understand their social construction.