SIGNED: Smart cIty diGital twiN vErifiable Data Framework

End-of-life decision support to enable circular economy in the automotive industry based on digital twin data

Cities worldwide are rapidly integrating urban management information technology (IT) systems to address increasing urban challenges effectively. This paper examines the evolution of these systems, from the Ubiquitous City (U-City) to contemporary Smart Cities, and explores emerging technologies like the Metaverse and Digital Twin. Despite their growing relevance, there is ambiguity in their conceptual definitions and how they are applied interchangeably in urban management contexts. This study aims to clarify these technologies by analyzing U-City, Smart City, Metaverse, and Digital Twin within an Analysis Framework of Urban Management IT Systems. Our analysis reveals distinct roles and interconnections among these technologies. U-City initiated the development of essential IT infrastructure, laying the groundwork for subsequent systems. Smart City technology, leveraging the infrastructure provided by U-City, addresses real urban issues but lacks predictive capabilities for future challenges. In contrast, the Metaverse facilitates the simulation of urban changes and planning in a virtual environment, influencing real-world city development. Digital Twin technology enhances the operational efficiency of U-City and Smart City by simulating real-world data and optimizing urban management processes. The study highlights that while U-City and Smart City function in the tangible realm, Metaverse and Digital Twin operate in the virtual domain, yet they are closely integrated. Metaverse assists in urban planning through simulations that inform real-world Smart City designs, while Digital Twin optimizes real-time city operations and informs Metaverse simulations for future planning. The findings underscore the importance of understanding these technologies’ unique contributions and their interplay. Although current urban management IT systems have limitations, especially in technology classification and data accessibility, this research paves the way for future investigations. Further research should focus on refining the definitions and applications of Metaverse and Digital Twin, clarifying their relationships with Smart City and U-City, and addressing security and privacy concerns associated with these technologies.

This study examines the potential of digital twin (DT) technology to strengthen urban security, with a specific focus on flood risk management in smart cities. A DT is understood as a virtual representation of real-world assets and processes, continuously synchronised with data from the physical environment. Building on an analysis of the existing DT literature and maturity assessment, identified operational requirements and the authors’ expertise in crisis management, this study proposes a structured set of DT maturity levels with stage boundary conditions and illustrative measurable indications and designs a maturity-driven data content model for a flood-oriented DT. The framework identifies essential data layers, sensing requirements and integration mechanisms necessary for representing hydrological, infrastructural and environmental conditions at operationally meaningful update frequencies. This study further outlines the conceptual architecture of a flood DT and discusses its potential to support prediction, situational awareness and decision making across crisis management phases. By providing recommendations for DT implementation and highlighting opportunities for future development, this study contributes to ongoing efforts to enhance the resilience and safety of urban areas through advanced digital technologies.

As a promising technology to converge the traditional industry with the digital economy, digital twin (DT) is being investigated by researchers and practitioners across many different fields. The importance of data to DT cannot be overstated. Data plays critical roles in constructing virtual models, building cyber-physical connections, and executing intelligent operations. The unique characteristics of DT put forward a set of new requirements on data. Against this background, this paper discusses the emerging requirements on DT-related data with respect to data gathering, interaction, universality, mining, fusion, iterative optimization, and on-demand usage. A new notion, namely digital twin data (DTD), is introduced. This paper explores some basic principles and methods for DTD gathering, interaction, storage, association, fusion, evolution and servitization, as well as the key enabling technologies. Based on the theoretical underpinning provided in this paper, it is expected that more DT researchers and practitioners can incorporate DTD into their DT development process.

Based on the current applications in the oil and gas industry, this paper approaches the main challenges in the development of a Digital Twin for subsea production system assets. Bibliographical research was carried out to select relevant digital twin solutions developed in the different sectors of the oil and gas industry, in order to identify the main challenges, risks and prospects for implementing this type of technology. Furthermore, this manuscript also provides solutions developed by the authors based on the experiences in research, development and innovation of national projects for digital transformation. These solutions contemplate data analysis, connections with databases, definitions of recurrent degradation models in the oil and gas industry. A digital twin can be defined as the digital representation of a system or asset, which calculates states and provides relevant information through integrated models and data, to support decisions throughout the life cycle. In the upstream, whose main activities involve the exploration and production of oil and gas, digital twin can be used as a tool for asset integrity management in the production system and associated with integrity management methodologies such as Risk Based Inspection (RBI). The implementation of a digital twin is a complex task and has several challenges, since it is composed of many models and data that need to be interconnected. However, it is common for data from different sources to be stored on different systems. Another relevant challenge concerns the models associated with digital twins, which are sources of uncertainties. The lack of consistent models and coordination between current physical and virtual models, complex data validation, collection and processing challenges are obstacles in implementing a digital twin. If well implemented, the digital twin can help in the decision-making process related to asset integrity management by following the asset throughout its life cycle. In an RBI approach, it can reduce the OPEX cost of subsea systems. This paper will approach the stages of the development of a digital twin for subsea production systems assets and the main challenges based on recent studies carried out in the oil and gas industry such as the requirement for transparency in data exchange while maintaining security and confidentiality, data-model assimilation, uncertainty and validation of models. In addition, it will be presented the experience acquired by the authors in the development of solutions for integrity management.

Autonomously driving vehicles and robots that drive in public environments need to be safe and reliable under all weather conditions, including arctic winter conditions. Digital twins provide an opportunity to test autonomous vehicles in a safer, faster, and less expensive environment than carrying out tests in real-life conditions. We developed the data connection via ROS (Robot Operating System) between a mobile robot and its digital twin. This allows for almost real-time exchange of commands, information, and sensor data between the twins.The digital twins of the robo t and the testing ground are constructed in a CARLA-based autonomous driving simulator, which simulates realistic arctic winter weather conditions.The digital twin design was informed by the intended future use cases: Testing, optimizing, controlling, and monitoring autonomous driving and snow cleaning functions first with the digital twin, then in hybrid approaches.In our test setup we tested the hybrid case, where both robot twins were moving in the simulation and the real-world test area at the same time. We verified our digital twin, assessed delays, and the applicability in the intended use cases. Our results show that the digital testing ground would profit from inbuilt reference points to examine the alignment with its real-world counterpart. The communication via ROS was occurring in almost real-time , therefore, the digital twin setup was found to be applicable in hybrid digital twin testing. In the future, we will introduce an autonomous car into this digital twin setup and equip the testing ground with a 5G network.

The rise of Industry 4.0 technologies fosters the creation and use of digital twins (DT), which aids in transforming the manufacturing sector into a more responsive and intelligent domain. DTs are digital replicas of physical systems that emulate the behavior and dynamics of those systems. A comprehensive DT integrates physical elements, virtual components, and the data exchange between them. Integrated DTs are being utilized across various processes and product sectors. Although the pharmaceutical industry has recently progressed by adopting Quality-by-Design (QbD) initiatives and is in the midst of a digital transformation to integrate Industry 4.0, there has yet to be a complete DT implementation in pharmaceutical manufacturing. Consequently, it is essential to evaluate the advancements of the pharmaceutical sector in adopting DT solutions. This narrative literature review aims to provide an overview of the current state of DT development and its application in pharmaceutical and biopharmaceutical production. Additionally, it addresses the challenges and opportunities for future research in this area.

INTRODUCTION: Digital twin technology is at the head of smart city innovations; rapid urbanization and the rising complexity of infrastructure management have driven this technology. Real time virtual replicas of the physical urban systems - digital twins - simulate urban scenarios, monitor conditions, and improve resource allocation more accurately than ever before. Rigorous empirical studies that quantify the impact of these technologies on the efficiency of urban planning are rare, curbing evidence-based policy making and large-scale adoption strategies. OBJECTIVES: This study estimates a panel fixed effects econometric model that quantifies the impact of integrating digital twins on urban planning performance of five European countries (Estonia, Germany, Portugal, the UK, Poland) over the period of 2020–2024. METHODS: Using data from the OECD Smart Cities database, the European Commission Urban Data Platform, the World Bank’s Worldwide Governance Indicators, and national municipal APIs, annual data were assembled on a composite urban efficiency index, a digital twin integration score, real time data usage, per capita infrastructure investment, governance quality metrics, and population density. The model employs city and time fixed effects and addresses heteroskedasticity and serial correlation with clustered robust standard errors. RESULTS: The efficiency index of Germany improved from 53,52 in 2020 to 62,57 in 2024; Poland from 45,51 to 58,73; and Estonia dropped sharply from 70,22 to 39,44. Coefficients on digital twin integration (β₁ = 12,00) and governance quality (β₄ = 8,00) are positive and statistically significant. Real-time data use (β₂ = 0,08) and infrastructure investment (β₃ = 0,004) increase efficiency, while population density has a slight negative effect (β₅ = –0,005). CONCLUSION: This underscores the importance of the network and governance relationships of digital twins in realizing their potential. Future research should include non-European cities, apply spatial econometric techniques to account for intercity spillovers, and use dynamic panel models to assess feedback loops from past efficiency gains and their influence on future technology investment.

Abstract. Technology has advanced and progressed tremendously, and the term city is being elevated to a new level where the smart city has been introduced globally. Recent developments in the concept of smart city have led to a renewed interest in Digital Twin. Using precise Building Information Modelling (BIM) consolidated with big data and sensors, several attempts have been made to establish digital twin smart cities. In recent years, several researchers have sought to determine the capability of smart city and digital twin for various taxonomies such as development and urban planning purposes, built environment, manufacturing, environmental, disaster management, and healthcare. Despite being beneficial in many disciplines, especially in manufacturing, built environment, and urban planning, these existing studies have shown a lack of aspect in terms of emergency or disaster-related as opposed to the elements mentioned above. This is because the researcher has not treated emergencies or disasters in much detail. Therefore, an extensive review on smart city, digital twin, BIM and disaster management and technology that revolves around these terms were summarised. In general, 39 articles from prominent multidisciplinary databases were retrieved over the last two decades based on the suggested PRISMA workflow. These final articles were analysed and categorised into four themes based on the research content, gist, and keywords. Based on the review of 39 articles related to smart city, digital twin and BIM, a workflow for the smart city digital twin and the conceptual framework for indoor disaster management was proposed accordingly. The establishment of smart city digital twins solely for an indoor emergency can be beneficial to urbanites, and it could provide numerous benefits for enhanced situation assessment, decision making, coordination, and resource allocation.

This article analyzed approaches related to the development of digital twins. Interest in digital twin technology is growing in science and law enforcement. In this regard, we can talk about the formation of the Concept of “digital twins”, which is based on the fact that it is a virtual copy of a physical object that can track it in real time. Accordingly, a framework has been established to compare digital twin approaches in terms of research areas, technologies and models used in digital twin, and verification methods used. Using the proposed framework, gaps and future directions for digital twin research are identified along five dimensions: digital twin definitions, applications, integration, modeling, and data.

Measurement errors caused by real-time electromagnetic (EM) noise or the sensitivity of measuring equipment significantly affect Partial Discharge (PD) localization accuracy in open-space substations. This study proposes a Deep Neural Network (DNN) approach combined with an augmented Time Difference of Arrival (TDOA) dataset to improve PD coordinate estimation. A digital twin of a 10 m × 10 m × 2 m space was used to generate over one million synthetic data points, substantially reducing data collection time. The trained DNN demonstrated excellent localization performance in the digital environment, with more than 90% of errors below 5%, as validated by 3D scatter plots. Despite relying on augmented TDOA data from the digital twin, the DNN model exhibited high confidence when applied to real-world measurements, achieving an average localization error of 0.3610 m across 21 test points, outperforming Random Forest Regression (RFR), Gaussian Process Regression (GPR) and 1-dimensional Convolutional Neural Networks (1DCNN). Additionally, an in-depth analysis of the augmented TDOA dataset synthesis was conducted to optimize the DNN model. Key factors investigated included the impact of dataset size on localization accuracy, training time and performance across different training durations. Finally, a benchmarking comparison with existing methods was summarized in tabular form, highlighting the advantages of the proposed work over conventional iterative algorithms and other machine learning (ML) models. • Partial Discharge (PD) localization using Deep Neural Network (DNN) for air-insulated substation. • PD signals measured using four ultra-high frequency (UHF) sensors under dynamic noise environment. • PD localization performances were first analyzed within digital twin environment. • A DNN model trained with augmented dataset bridge the gap between simulated training and field applications. • Localization accuracy surpassed other reported works with 0.3610 m average errors in terms of speed and accuracy.

Secure sharing of big digital twin data for smart manufacturing based on blockchain

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods.

With the developments and applications of the advanced information technologies such as cloud computing, internet of thing, artificial intelligence and virtual reality, industry 4.0 and smart manufacturing era are coming. In this respect, one of the specific challenges is to achieve a connection of physical resources on the shop floor with virtual resources, for real-time response, real time process optimization, and simulation, which is merged by big data problem. In this respect, Digital Twins (DT) concept is introduced as a key technology, which includes physical resources, virtual resources, service system, and digital twin data. DT considers current condition of physical resource and prediction of future events to make a responsive decision. However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little applications have been developed with this purpose, especially in the industrial manufacturing area. Therefore, the types of data and technology required to build the DT for a manufacturing system are presented in this work, trying to develop a framework of DT based manufacturing system, which is supported by the virtual reality for virtualization of physical resources.

Rapid global urbanization and the growth of the urban population lead to the emergence of serious, systemic problems that critically and negatively affect the ecological, economic, and social sustainability of cities. In this context, the search and implementation of effective models of spatial development, which ensure the resilience of the territorial system and increase its competitiveness and adaptability in a changing external environment, is a relevant and strategic task of urban management. The aim of the work is to increase the overall efficiency of urban environment management through in-depth systematization and critical analysis of modern experience in implementing the Smart Cities concept, focused on effective intellectual decision-making. The object of the study is the concept of a "smart city" and the technologies for its implementation within a unified, integrated intellectual management of the urban environment. It is determined that the lack of a single universal definition of a "smart city" and unified approaches to its implementation necessitates the systematic comprehension of this concept and the study of evolutionary smartization models. The systemic representation of the Smart City concept is formulated for the first time using three complementary methods – verbal, graphical, and algebraic modeling. This approach focuses attention on its unique functional aspects, in particular, on data sets, the architecture of software solutions, and functional indicators. Special attention is paid to the effective use of open geospatial data (specifically, OpenStreetMap) in combination with 3D modeling tools. Such an integrated approach ensures the rapid and cost-effective creation of a reliable digital environment (digital twin), which significantly simplifies the implementation of Smart City, guaranteeing the flexibility, scalability, and adaptability of these projects. A formalized component approach to Smart City design is proposed. It regulates the design of functional modules of a smart city using components of existing software solutions, which increases the overall efficiency of their modernization and scaling, and also provides the necessary flexibility in the integration of absolutely new technologies. The obtained results can be successfully implemented in high-level automated urban management systems, where they will ensure comprehensive modeling, monitoring, and intelligent decision-making regarding the long-term development of urban infrastructure.