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Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model.

While digital twin techniques have been applied successfully in manufacturing and construction, cities are so complex that they require more measuring and modelling eff ort. Moreover, since they are evolving all the time, it is not enough to simply measure and model cities only once. This article explores whether digital twin techniques can meet city planning needs and help them face their wide-ranging challenges – from sustainability to education, digitalization, infrastructure and healthcare.

Publisher Copyright: Author In this paper, we describe how the up-to-date state of a digital twin, and its corresponding simulation model, can be used as a fitness function of an evolutionary algorithm for optimizing a large-scale industrial process. An ICT architecture is presented for solving the computational challenges that arise when the fitness function evaluation takes considerable amount of time. Parallel computation of the fitness function in a cloud computing environment is proposed and the evolutionary algorithm is connected to the computational environment using the Function-as-a-Service approach. A case-study was conducted on the district heating network of Espoo, the second largest city in Finland. The study shows that the architecture is suited for optimizing the operating costs of the large district heating network, with over 800 km of water pipes and over 14 heat producers, reaching a cost-saving of an average of 2%, and up-to 4%, over the current industrial state-of-the-art method in use at the city of Espoo.

Extended version of the executive summary that incorporates presentations from the Consultation meeting held in Brussels on May 11, 2023.

Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, where a constantly changing environment is expected and network configuration must adapt accordingly. Modeling tools are required to predict the impact on performance (packet and delay loss) when new traffic demands arrives and when changes in routing paths are applied in the network. Mathematical analysis and network simulators are techniques for modeling networks but both have limitations, as the former provides low accuracy and the latter requires high execution times. To overcome these limitations, Machine Learning (ML) algorithms, and more specifically, Graph Neural Networks (GNNs), are proposed for network modeling due to their ability to capture complex relationships from graph-like data while predicting network properties with high accuracy and low computational requirements. However, one of the main issues when using GNNs is their lack of generalization capability to larger networks, i.e., when trained in small networks (in number of nodes, paths length, links capacity), the accuracy of predictions on larger networks is poor. This paper addresses the GNN generalization problem by the use of fundamental networking concepts. Our solution is built from a baseline GNN model called RouteNet (developed by Barcelona Neural Networking Center - Universitat Polit`ecnica de Catalunya (BNN-UPC)) that predicts the average delay in network paths, and through a number of simple additions significantly improves the prediction accuracy in larger networks. The improvement ratio compared to the baseline model is 101, from a 187.28% to a 1.828%, measured by the Mean Average Percentage Error (MAPE). In addition, we propose a closed-loop control context where the resulting GNN model could be potentially used in different use cases.&nbsp

The “Digital Earth” (DE) metaphor is very useful for both end users and hydrological modelers (i.e., the coders). In this opinion paper, we analyze different categories of models with the view of making them part of Digital eARth Twin Hydrology systems (DARTHs). We stress the idea that DARTHs are not models, rather they are an appropriate infrastructure that hosts (certain types of) models and provides some basic services for connecting to input data. We also argue that a modeling-by-component strategy is the right one for accomplishing the requirements of the DE. Five technological steps are envisioned to move from the current state of the art of modeling. In step 1, models are decomposed into interacting modules with, for instance, the agnostic parts dealing with inputs and outputs separated from the model-specific parts that contain the algorithms. In steps 2 to 4, the appropriate software layers are added to gain transparent model execution in the cloud, independently of the hardware and the operating system of computer, without human intervention. Finally, step 5 allows models to be selected as if they were interchangeable with others without giving deceptive answers. This step includes the use of hypothesis testing, the inclusion of error of estimates, the adoption of literate programming and guidelines to obtain informative clean code. The urgency for DARTHs to be open source is supported here in light of the open-science movement and its ideas. Therefore, it is argued that DARTHs must promote a new participatory way of performing hydrological science, in which researchers can contribute cooperatively to characterize and control model outcomes in various territories. Finally, three enabling technologies are also discussed in the context of DARTHs – Earth observations (EOs), high-performance computing (HPC) and machine learning (ML) – as well as how these technologies can be integrated in the overall system to both boost the research activity of scientists and generate knowledge.

Long-term in-service monitoring has been heralded as having the potential to radically reshape the design and operation of civil structures and infrastructure. However, in order for civil and structural engineers to extract the greatest value from in situ structural monitoring there are a range of practical, cultural and social barriers which must be overcome. To explore current perceptions of long-term monitoring in civil and structural engineering, a multi-national survey of 146 participants was conducted to understand perceived uncertainties within the existing civil/structural engineering design process, perceptions of long-term monitoring, and the potential for the future integration and use of long-term monitoring in the civil/structural engineering design process. This study highlights that while views of long-term monitoring as a tool within the engineering design process are broadly positive, there is a wide disparity on its current implementation in practice, little agreement on how it may offer the greatest benefit to civil and structural engineering design, and a current lack of direct financial incentive to prompt its use within industry.

The digital twin (DT) research field is experiencing rapid expansion; yet, the research on industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are involved in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. This study’s findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the breakeven point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. This study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.

The digital twin (DT) research field is experiencing rapid expansion, yet the research in industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. The study findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the break-even point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. The study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.

The intensity and frequency of hydro-meteorological hazards have increased due to fast-growing urbanisation activities and climate change. Hybrid approaches that combine grey infrastructure and Nature-Based Solutions (NBSs) have been applied as an adaptive and resilient strategy to cope with climate change uncertainties and incorporate other co-benefits. This research aims to investigate the feasibility of Real Time Control (RTC) for NBS operation in order to reducefloodingand improve their effectiveness. The study area is the irrigation and drainage system of the Rangsit Area in Thailand. The results show that during the normal flood events, theRTC systemeffectively reduces water level at the Western Raphiphat Canal Station compared to the system without RTC or with additional storage. Moreover, the RTC system facilitates achieving the required minimum volume and increasing the volume in the retentions. These findings highlight the potential of using RTC to improve the irrigation and drainage system operation as well as NBS implementation to reduce flooding. The RTC system can also assists in equitable water distribution between Klongs and retention areas, while also increasing the water storage in the retention areas. This additional water storage can be utilized for agricultural purposes, providing further benefits. These results represent an essential starting point for the development of Smart Solutions andDigital Twinsin utilizing Real-Time Control for flood reduction and water allocation in the Rangsit Area in Thailand.

Natural extracts are broadly utilized as remedies, nutrition additives, cosmetics or flavors as well as natural pesticides, fungicides or herbicides. Green manufacturing technologies are of added market value and are sustainable towards the climate neutrality politically demanded for 2045. The concept of digital twins involves experimentally distinct validated process models combined with process analytical technology that is to be adapted to the existing operations. This is a key technology for the autonomous operations in industry 4.0. This paper exemplifies this approach and evaluates the results of the application and implementation efforts of regulated industries. A conductivity sensor for the measurement of the dry residue content and/or Fourier-transformed infrared spectroscopy for marker/lead or reference substance concentration determination are the most feasible and straight forward solutions. Different process control concepts from simple PID controllers (proportional, integral and differential) to advanced process control using digital twin models are evaluated and discussed in terms of industrialization efforts and benefits. The global warming potential CO2 equivalent per kg of natural product could be decreased by a factor of 5–10 as well as the cost of goods, which makes the pay-out time for the industrialization investment less than 1 year and the approach highly competitive. The success rate of the extraction process under regulatory constraints can be raised to 100%, reducing waste, overall solvent consumption, personnel efforts and energy requirements to a minimum.

The development and optimization of lipid nanoparticle (LNP) formulations through hydrodynamic mixing is critical for ensuring the efficient and cost-effective supply of vaccines. Continuous LNP formation through microfluidic mixing can overcome manufacturing bottlenecks and enable the production of nucleic acid vaccines and therapeutics. Predictive process models developed within a QbD Biopharma 4.0 approach can ensure the quality and consistency of the manufacturing process. This study highlights the importance of continuous LNP formation through microfluidic mixing in ensuring high-quality, in-specification production. Both empty and nucleic acid-loaded LNPs are characterized, followed by a TFF/buffer exchange to obtain process parameters for the envisioned continuous SPTFF. It is shown that LNP generation by pipetting leads to a less preferable product when compared to continuous mixing due to the heterogeneity and large particle size of the resulting LNPs (86–104 nm). Particle size by continuous formation (71 nm) and the achieved encapsulation efficiency (EE) of 88% is close to the targeted parameters for Pfizer’s mRNA vaccine (66–93 nm, 88%EE). With the continuous encapsulation of nucleic acids in LNPs and the continuous production of mRNA in in vitro transcription, the basis for the holistic continuous production of mRNA is now established. We already showed that a fully autonomous process requires the incorporation of digital twins and a control strategy, with predictive process models and state-of-the-art PAT enabling real-time-release testing. This autonomous control can considerably improve productivity by about 15–20% and personnel as well as chemical reduction of about 30%. The results of this work complement this, laying the basis for fully continuous, bottleneck-free production of mRNA and other cell- and gene-therapeutic drug/vaccine candidates in a GMP- and QbD-compliant Biopharma 4.0 facilities on a flexible scale.

Pharmaceutical manufacturing processes are moving towards automation and real-time process monitoring with the help of process analytical technologies (PATs) and predictive process models representing the real system. In this paper, we present a digital twin developed for an adjuvant manufacturing process involving a microfluidic formation of lipid particles. The twin uses a hybrid model for estimating the current state of the process and predicting system behavior in real time. The twin is used to control the adjuvant particle size, a critical quality attribute, by varying process parameters such as the temperature and inlet flow rates. We describe steps in the design and implementation of the twin, starting from the conception of the mechanistic model, up to the generation of its surrogate model used as state estimator, PATs and the setup of the information technology—Operational technology architecture. We demonstrate the performance of the twin by introducing different disturbances in the process and comparing the effect on the product critical quality attributes with and without the control of the digital twin. Finally, we showcase the digital twin implementation for the process in good manufacturing practice, through an engineering run, which demonstrated the robustness of the process when controlled by the digital twin.

Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external factors like pollution and congestion. To counter this, smart cities deploy technologies such as digital twins (DT)s to achieve sustainability. Research suggests that DTs can be beneficial in optimizing the physical systems they are linked with. The concept has been extensively studied in many technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics applications. To do this, we survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the identification of key factors in urban logistics, we produce a conceptual model for the general design of an urban logistics DT through a knowledge graph. We provide an illustration on how the conceptual model can be used in solving a relevant problem and showcase the integration of relevant DDO methods. We finish off with a discussion on research opportunities and challenges based on previous research and our practical experience.

Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external factors like pollution and congestion. To counter this, smart cities deploy technologies such as digital twins (DT)s to achieve sustainability. Research suggests that DTs can be beneficial in optimizing the physical systems they are linked with. The concept has been extensively studied in many technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics applications. To do this, we survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the identification of key factors in urban logistics, we produce a conceptual model for the general design of an urban logistics DT through a knowledge graph. We provide an illustration on how the conceptual model can be used in solving a relevant problem and showcase the integration of relevant DDO methods. We finish off with a discussion on research opportunities and challenges based on previous research and our practical experience.

ispartof: INFORMATION & MANAGEMENT vol:60 issue:4 status: accepted

Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and congestion. In order to counter this, smart cities deploy technological tools to achieve sustainability. Such tools include Digital Twins (DT)s which are virtual replicas of real-life physical systems that control the systems. Research points that DTs can be very beneficial in how they control a physical system by constantly optimizing its performance. The concept has been extensively studied in other technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics networks. To do this, we provide a characterization of key factors in urban logistics for dynamic-decision making. We also survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the characterization, we produce a conceptual model that describes the ontology, learning capabilities and optimization prowess of an urban logistics digital twin through its quantitative models. We finish off with a discussion on potential research benefits and limitations based on previous research and our practical experience.

Humans have probably always been trying to organise the world around us, and we have accounts from at least Aristotle to formalise organisation systems [1]. While this has largely been a manual effort (cf. Linnaeus’ Systema Naturae), technological advances in natural language processing and the advent of the Semantic Web research domain have made (semi-)automatic organisation of knowledge about the world possible [2]. Now, on-the-fly DNA sequencing, nanoprocessors, nanosensors, nanobots, and smart surfaces are enabling organic self-organisation of real-world objects. In this paper, we present the theoretical framework and first experiments that ring in this new era of dynamic auto-taxonomising. Disclaimer: This paper is a work of fiction, written in 2023 and describing research that may be carried out in 2043. For this reason, it includes citations to papers produced in the period 2024-2043, which have not been published (yet); all citations prior to 2024 refer instead to papers already in the literature. Any reference or resemblance to actual events or people or businesses, past present or future, is entirely coincidental and the product of the author’s imagination.

We discuss the collaboration support of loosely coupled Smart Systems through configurable hyper-frameworks. Based on the system-of-systems (SoS) paradigm, in this article, we propose the model of a novel extendible conceptual framework with domain-specific moderation support for model-based simulations and the engineering of complex heterogeneous systems. The domain knowledge meta-model and corresponding management enterprise architecture enable the creation of template-based specializations. The proposed SoS conceptual framework meta-model represents an initial framework prototype that supports modeling, simulation, analysis, and utilization of dynamic architecting of heterogeneous SoS configurations. A Smart-Habitat concept encapsulating Smart-Area, Smart-City, Smart-Lot, Smart-Building, and Smart-Unit abstractions illustrate the frameworks’ applicability. The proposed SoS conceptual framework represents the initial conceptual support for modeling, simulation, analysis, and dynamic architecting of heterogeneous SoS configurations. We plan to refine the component architecture meta-model, specify a language workbench with Domain-Specific Orchestration Language support, and verify the configuration-based simulation manifest creation. These actions lead to the framework’s next stage, an operational framework (OF) instance, as a transitional artifact to the aimed software framework (SwF) counterpart.

Prototyping and validating hardware–software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt to develop such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single- and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path-planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

This paper proposes a slope stability prediction model based on deep learning and digital twinning methods. To establish a reliable slope database, 30 actual slopes were collected, and 100 digital twin (DT) models were generated for each actual slope by fine-tuning the slope profiles. The safety factors of all slope samples were calculated using the Limit Equilibrium Methods (LEMs). A convolutional neural network (CNN) regression model was established, and the root mean square error (RMSE) was used as the evaluation indicator. In order to find an excellent CNN model, the K-fold (K = 10) cross-validation was used to compare the predictive effect of 1D CNN and 2D CNN on the slope safety factor. On this basis, CNN models with different network depths were compared. The results showed that the 2D CNN model with six convolutional layers had the best network prediction effect for the slope dataset. To validate the generalization ability of the model, an actual slope was input into the CNN model; its prediction result was 1.0229, and the absolute error with its real safety factor (1.0197) was 0.0032. With the slope stability prediction model proposed in this paper, the safety factor of slopes can be obtained from their geological and physical data, which greatly simplifies the calculation of the safety factor and has great engineering significance.

ISC High Performance 2023 is a highly-anticipated annual event that provides a valuable opportunity for public and industry users to connect with technology developers. Taking place in Hamburg, Germany from May 21st to May 25th, 2023, this year's conference features a rich program of keynotes, tutorials, workshops, and focused sessions centered around high-performance computing, machine learning, data analytics, and quantum computing. BioDT partners presented theposter at the IT4I booth.

A digital twin is a virtual representation of a physical entity. By bringing together data, modelling and simulation, digital twins enable insights about the performance of structures, products and processes. They are powerful tools for understanding and optimising complex systems that are already beginning to demonstrate their potential across multiple sectors, from engineering and manufacturing to health and the environment. In this white paper, we share learnings from The Alan Turing Institute’s AI for science and government (ASG) programme, which has been conducting foundational research and applied work in digital twins under the ‘Ecosystems of digital twins’ (EDT) theme. ASG researchers are expanding the scale and complexity, and deepening the application and deployment, of digital twins. We organise digital twins into three groups according to their level of complexity, scaling up from a digital twin of a single asset or system, to a digital twin incorporating multiple assets or systems. At the third level, digital twins of differing levels of complexity are linked together in an ‘ecosystem of digital twins’. We provide case studies demonstrating the EDT theme’s multidisciplinary work on digital twins for each of these three levels. Through digital twins, we can develop unprecedented capabilities for understanding, manipulatingand managing complex systems. Within the EDT theme, we have found that for the full promise of the technology to be realised, we must consider the development and deployment of digital twins at the ecosystem-level. By connecting together digital twins within a virtual environment, we can enable sharing of information between ‘federated’ assets – assets under different jurisdictions and restrictions – unlocking more advanced insights and capabilities than are possible at the lower levels. Within an ecosystem, connected digital twins can also promote more varied applications, for example, by combining representations of, and insights from, both physical and social systems to support decision-making. There are, however, certain technical, social and institutional issues that need to be negotiated in developing ecosystems of digital twins. We therefore provide the following three recommendations in support of the progress which needs to be made: Elevate cross-disciplinary activities and spaces for digital twins. Invest in open infrastructure, with a focus on data and technical standards. Prioritise tools for building trust in and understanding of digital twins. By following these recommendations, with support from the right investments, we can build robust ecosystems of digital twins capable of helping us tackle some of the most important global challenges, from pandemics to adapting to climate change.

The Social and hUman ceNtered XR (SUN) project is focused on developing eXtended Reality (XR) solutions that integrate the physical and virtual world in a way that is convincing from a human and social perspective. In this paper, we outline the limitations that the SUN project aims to overcome, including the lack of scalable and cost-effective solutions for developing XR applications, limited solutions for mixing the virtual and physical environment, and barriers related to resource limitations of end-user devices. We also propose solutions to these limitations, including using artificial intelligence, computer vision, and sensor analysis to incrementally learn the visual and physical properties of real objects and generate convincing digital twins in the virtual environment. Additionally, the SUN project aims to provide wearable sensors and haptic interfaces to enhance natural interaction with the virtual environment and advanced solutions for user interaction. Finally, we describe three real-life scenarios in which we aim to demonstrate the proposed solutions.

The growing demand for personalized medicine we are currently witnessing has given rise to more in-depth research in the field of biomarker discovery and, thus, in biological banks that hold the ability to process, collect, store, and distribute “high-quality” biological specimens. However, the notion of “specimen quality” is subject to change with technological advancements. In this perspective, we propose that the notion of sample quality should shift from a broad definition of “high-quality” to a “fit-for-purpose” concept more suitable for precision medicine studies. Digital twins are a digital replica of real entities. These are largely adopted in any digitalized domain and are currently finding applications in biomedicine. The adoption of digital twins for biosamples, proposed in this paper, can provide prompt information about the whole lifecycle of the physical twin (i.e., the biosample) and substantially extend the possible matching criteria between the available samples and the researchers’ and physicians’ requests. This fine-tuning matching could greatly contribute to improving the “fit-for-purpose” quality, not only for studies based on current needs, but also to improve the identification of the best available samples in future situations, determined by the evolution of technologies and biosciences. Assuming and exploiting a data-science view in our biobank perspective, the more (accurate) data there are available, the more information can be extrapolated from them, the more opportunities there are for matching future, currently unknown, needs. This should be a mandatory principle that the ‘time machines’ called biobanks should follow.

Most of the buildings that exist today were built based on 2D drawings. Building information models that represent design-stage product information have become prevalent in the second decade of the 21st century. Still, it will take many decades before such models become the norm for all existing buildings. In the meantime, the building industry lacks the tools to leverage the benefits of digital information management for construction, operation, and renovation. To this end, this paper reviews the state-of-the-art practice and research for constructing (generating) and maintaining (updating) geometric digital twins. This paper also highlights the key limitations preventing current research from being adopted in practice and derives a new geometry-based object class hierarchy that mainly focuses on the geometric properties of building objects, in contrast to widely used existing object categorisations that are mainly function-oriented. We argue that this new class hierarchy can serve as the main building block for prioritising the automation of the most frequently used object classes for geometric digital twin construction and maintenance. We also draw novel insights into the limitations of current methods and uncover further research directions to tackle these problems. Specifically, we believe that adapting deep learning methods can increase the robustness of object detection and segmentation of various types; involving design intents can achieve a high resolution of model construction and maintenance; using images as a complementary input can help to detect transparent and specular objects; and combining synthetic data for algorithm training can overcome the lack of real labelled datasets.

Heightened awareness of the impact of climate change has led to rapidly increasing penetration of renewable energy resources in electric energy distribution systems. Those distributed energy resources (DERs), mostly inverter-based, can act as resiliency sources for the grid but also introduce new control and stability challenges. In this thesis, a cyber-physical system (CPS) testbed is proposed combining a real-time electro-magnetic transient power system simulation and a practical model for communication network simulation. By regularly updating the CPS testbed with real-world SCADA information, a digital twin is effectively created. The digital twin allows the testing of novel microgrid control and cybersecurity strategies. Simulations using the Virginia Tech Electric Service (VTES) as a test case demonstrate the capability of adequately controlled resources, including solar PV, energy storage, and a synchronous generator, to enhance resilience by providing energy to critical loads. The DERs comply with IEEE disturbance ride-through requirements and switching transients are maintained within acceptable limits. A comprehensive DER-based resiliency plan is developed and validated for the Virginia Tech smart grid. M.S. In the last two decades, the increased occurrence of major power outages in the United States underscores the critical need to improve the reliability and resilience of the power grid. Massive investments have been made to install information and communications technology enabling near real-time monitoring and control of the smart grid. Simultaneously, heightened awareness of the impact of climate change led to rapidly increasing penetration of renewable energy resources at the distribution system level. Those distributed energy resources, mostly inverter-based, can act as resiliency sources for the grid but also introduce new control and stability challenges. In this work, a comprehensive testbed is proposed for the real-time simulation of both the power systems and communication networks. This method allows the testing of novel microgrid control and cybersecurity strategies. The testbed is used to develop and validate a resiliency plan for the Virginia Tech Electric Service using distributed energy resources.

Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.

Modelling a 3D city poses an interesting challenge. To create a virtual city, a road pattern has to be designed and a large number of buildings need to be generated. Every urban place has a road network, often a superimposed pattern plan that serves a population density and buildings which follow statutory rules. This patterned behaviour of the city is why it is possible to develop rules or “computational instructions,” to generate city models. In this article, we are going to discuss how to use procedural modelling and CityEngine, a rule-based application commonly used in the movie industry and gaming to produce vast realistic cityscapes, for regional and urban planning via an urban analytics approach. Unlike cinema’s imaginary worlds, cities have real-life population dependencies that need to be modelled for the development of planning scenarios. The goal is then to use the generative properties of the procedural modelling approach, along with population prediction models, to create informed 3D city scenarios. Instead of designing solutions, the user can use interactive parameters to affect the 3D model globally, thus enabling virtual cities to become active simulators for planning. Using urban analytics and generative environments, procedural cities may be able to create a “teaser” of different versions of how the city would look like in the future.

With the wide use of renewable energy sources and the requirement for energy storage technology, the field of power systems is facing the need for further technological innovation. This paper proposes a wide-area distributed energy model based on digital twins. This model was constructed to more fully optimize the coordination of wide-area distributed energy in order to rationally deploy and utilize new energy units. Moreover, the minimization of the power deviation between the dispatch command and the actual power regulation output was also taken into account. In contrast to previous dispatch research, the cooperative game co-optimization algorithm was applied to this model, enabling a distributed approach that can quickly obtain a high-quality power command scheduling scheme. Finally, the simulation and comparison experiments using this algorithm with the wide-area distributed energy (WDE) model showed that it had the advantages of significantly reducing the tracking error, average error, and total error and effectively improving the tracking accuracy. The proposed method can help reduce total power deviations by about 61.1%, 55.7%, 53.1%, and 74.8%.

The metaverse is an upcoming computing paradigm aiming towards blending reality seamlessly with the artificially generated 3D worlds of deep cyberspace. This giant interactive mesh of three-dimensional reconstructed realms has recently received tremendous attention from both an academic and commercial point of view owing to the curiosity instilled by its vast possible use cases. Every virtual world in the metaverse is controlled and maintained by a virtual service provider (VSP). Interconnected clusters of LiDAR sensors act as a feeder network to these VSPs which then process the data and reconstruct the best quality immersive environment possible. These data can then be leveraged to provide users with highly targeted virtual services by building upon the concept of digital twins (DTs) representing digital analogs of real-world items owned by parties that create and establish the communication channels connecting the DTs to their real-world counterparts. Logically, DTs represent data on servers where postprocessing can be shared easily across VSPs, giving rise to new marketplaces and economic frontiers. This paper presents a dynamic and distributed framework to enable high-quality reconstructions based on incoming data streams from sensors as well as to allow for the optimal allocation of VSPs to users. The optimal synchronization intensity control problem between the available VSPs and the feeder network is modeled using a simultaneous differential game, while the allocation of VSPs to users is modeled using a preference-based game-theoretic approach, where the users give strict preferences over the available VSPs.

The production of messenger ribonucleic acid (mRNA) and other biologics is performed primarily in batch mode. This results in larger equipment, cleaning/sterilization volumes, and dead times compared to any continuous approach. Consequently, production throughput is lower and capital costs are relatively high. Switching to continuous production thus reduces the production footprint and also lowers the cost of goods (COG). During process development, from the provision of clinical trial samples to the production plant, different plant sizes are usually required, operating at different operating parameters. To speed up this step, it would be optimal if only one plant with the same equipment and piping could be used for all sizes. In this study, an efficient solution to this old challenge in biologics manufacturing is demonstrated, namely the qualification and validation of a plant setup for clinical trial doses of about 1000 doses and a production scale-up of about 10 million doses. Using the current example of the Comirnaty BNT162b2 mRNA vaccine, the cost-intensive in vitro transcription was first optimized in batch so that a yield of 12 g/L mRNA was achieved, and then successfully transferred to continuous production in the segmented plug flow reactor with subsequent purification using ultra- and diafiltration, which enables the recycling of costly reactants. To realize automated process control as well as real-time product release, the use of appropriate process analytical technology is essential. This will also be used to efficiently capture the product slug so that no product loss occurs and contamination from the fill-up phase is

The article discusses the means and directions for improving the results of simulation modeling of suburban agriculture and, as a result, the creation of digital twins of farms. Most innovative technologies are still considered new areas for experimentation in agriculture. However, the digital twins being developed for agriculture implement many of the ideas that have already been tested in other industries. The article presents an optimization problem that allows the simulation of suburban agriculture to provide the city with fresh products. Particular attention is paid to modeling the sustainable development of suburban agriculture and the characterization of related data. At the same time, one of the biggest challenges is the need to constantly collect and update expanding data about the object in order to create digital twins. The result of the study is the construction of a simulation modeling system that forms digital twins of suburban crop and livestock production, and the determination of priorities for the selection of relevant data. In order to determine the conditions for realizing opportunities in the transition from suburban farming simulation to digital twins, a general modeling system is presented, consisting of simulation and optimization models, and a set of metrics is selected for the constant collection and updating of the digital twin. The created simulation model was previously worked out by running dozens of different options in the form of sets of initial data, and as a result of the model's operation, the article presents the best (optimal) responses. The necessary steps for the realization of this transition are defined. As a result of the activity of the proposed conceptual system, real-time information, and analytics allows to optimize the performance of the farm, У статті розглянуто засоби та напрямки вдосконалення результатів імітаційного моделювання приміського землеробства і, як наслідок, створення цифрових двійників фермерських господарств. Більшість інноваційних технологій все ще вважаються новими напрямками для експериментів у сільському господарстві. Однак цифрові двійники, які розробляються для сільського господарства, реалізують багато ідей, які вже були випробувані в інших галузях. У статті представлено оптимізаційну задачу, яка дозволяє змоделювати приміське сільське господарство для забезпечення міста свіжою продукцією. Особливу увагу приділено моделюванню сталого розвитку приміського сільського господарства та характеристиці відповідних даних. У той же час, однією з найбільших проблем є необхідність постійно збирати та оновлювати розширювані дані про об’єкт, щоб створити цифрових двійників. Результатом дослідження є побудова системи імітаційного моделювання, що формує цифрові двійники приміського рослинництва та тваринництва, та визначення пріоритетів відбору релевантних даних. Для визначення умов реалізації можливостей переходу від моделювання приміського землеробства до цифрових двійників представлено загальну систему моделювання, що складається з імітаційної та оптимізаційної моделей, а також обрано набір метрик для постійного збору та оновлення цифрових двійників. Створена імітаційна модель була попередньо відпрацьована шляхом запуску десятків різних варіантів у вигляді наборів вихідних даних, і в результаті роботи моделі в статті представлені найкращі (оптимальні) відповіді. Визначено необхідні кроки для реалізації цього переходу. В результаті діяльності запропонованої концептуальної системи інформація в режимі реального часу та аналітика дозволяє оптимізувати роботу господарства

During drilling operations, stuck pipe occurs from time to time due to various reasons such as continuous changes of the formation lithology, failure to return the drill cuttings in time, shrinkage or collapse caused by soaking the formation with drilling fluid, and steps in the well wall caused by the drill-down. After the stuck pipe, the identification of the stuck pipe type can only be guessed by manual experience due to the jamming of the drill stem downhole, which lacks a scientific basis. Moreover, there is a lack of studies on the stuck pipe type. Therefore, scientific and accurate identification of the stuck pipe type is of great significance for timely unsticking and resuming drilling. In this paper, based on the friction torque rigid rod model of a3D well track, we obtained the degree of deviation of measured parameters from the normal trend, which can scientifically evaluate the degree of stuck pipe. Based on the SAX morphological symbolic aggregation approximation method, we obtained the changing trend of measured parameters during the stuck pipe, which can accurately describe the change laws of characteristic parameters during the stuck pipe. Based on the statistical characterization laws of different stuck pipe types in Sichuan and Chongqing, we established the knowledge graph of stuck pipe types, which can correlate with the complex knowledge of different stuck pipe types. The stuck pipe type can be identified according to the degree of stuck pipe, the changing trend of the characteristic parameters of stuck pipe, and the knowledge graph of stuck pipe types. The results show that the method can combine digital twins and the knowledge graph to accurately identify the stuck pipe type and provide a basis for taking targeted deconstruction measures.

Bridge infrastructure has great economic, social and cultural value. Nevertheless, many of the infrastructural assets are in poor condition as has been recently evidenced by the collapse of several bridges. The objective of this systematic review is to collect and synthesise state-of-the-art knowledge and information about how bridge information modelling, finite elements, and bridge health monitoring are combined and used in the creation of digital twins (DT) of bridges, and how these models could generate damage scenarios to be used by anomaly detection algorithms for damage detection on bridges, especially in those bridges with cultural heritage. A total of 76 relevant studies from 2017 up to 2022 are included in this review. The synthesis results show a general consensus towards the future adoption of DT for bridge design, management and operation among the scientific community and bridge practitioners. The main gaps identified are related to the lack of software interoperability, the required improvement of the performance of anomaly-detection algorithms and the approach definition to be adopted for the integration of DT at the macro scale. Other potential developments are related to the implementation of Industry 5.0 concepts and ideas within DT frameworks., This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101066739.

© 2023 by the authors. Licensee MDPI, Basel, Switzerland The notions of a collaborative virtual power plant ecosystem (CVPP-E) and a cognitive household digital twin (CHDT) have been proposed as contributions to the efficient organization and management of households within renewable energy communities (RECs). CHDTs can be modeled as software agents that are designed to possess some cognitive capabilities, enabling them to make autonomous decisions on behalf of their human owners based on the value system of their physical twin. Due to their cognitive and decision-making capabilities, these agents can exhibit some behavioral attributes, such as engaging in diverse collaborative actions aimed at achieving some common goals. These behavioral attributes can be directed to the promotion of sustainable energy consumption in the ecosystem. Along this line, this work demonstrates various collaborative practices that include: (1) collaborative roles played by the CVPP manager such as (a) opportunity seeking and goal formulation, (b) goal proposition/invitation to form a coalition or virtual organization, and (c) formation and dissolution of coalitions; and (2) collaborative roles played by CHDTs which include (a) acceptance or decline of an invitation based on (i) delegation/non-delegation and (ii) value system compatibility/non-compatibility, and (b) the sharing of common resources. This study adopts a simulation technique that involves the integration of multiple simulation methods such as system dynamics, agent-based, and discrete event simulation techniques in a single simulation environment. The outcome of this study confirms the potential of adding cognitive capabilities to CHDTs and further shows that these agents could exhibit certain collaborative attributes, enabling them to become suitable as rational decision-making agents in households. publishersversion published

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

Digital twins (DT) describe the integration of the physical and digital worlds with the aim of optimizing real world operations and functions. The digital twin concept has gained increasing attention across industries in the past decade including the building sector. However digital twins remain ambiguous with various existing definitions and characteristics. While DTs include all life cycle phases, ultimately their goal is optimization of operations during the use phase. Of the building life cycle phases, building facilities management (FM) is responsible for considerable costs and energy consumption and has potential for improvement through DT implementation. Along with increased building information modeling (BIM) implementation, recent advances in data driven technologies have encouraged the exploration of DT in the building sector. BIM has been coupled with technologies such as internet of things (IoT), data analytics, and cloud computing to optimize various FM functions often resembling DT. This study has reviewed existing literature on digital twins in facilities management using a structured literature review and characterized similar characteristics and definitions by different authors. Additionally, DT implementation in different FM application areas was quantified and analyzed. Results show that DT implementation in FM is still at nascent stages with major challenges surrounding standardization and data integration. Master of Science Digital twins (DT) describe the integration of the physical and digital worlds with the aim of optimizing real world operations and functions. The digital twin concept has gained increasing attention across industries in the past decade including the building sector. However digital twins remain ambiguous with various existing definitions and characteristics. DTs include all building life cycle phases from design, construction, to operation and maintenance. Ultimately their goal is optimization of operations also referred to as facilities management during the use phase. Of the building life cycle phases, building facilities management (FM) is responsible for considerable costs and energy consumption and has potential for improvement through DT implementation. Building information modeling (BIM) describes geometric and semantic information of physical assets and has been used to optimize operations in FM. Along with increased BIM implementation, recent advances in data driven technologies have encouraged the exploration of DT in the building sector. BIM has been coupled with technologies such as internet of things (IoT), data analytics, and cloud computing to optimize various FM functions often resembling DT. This study has reviewed existing literature on digital twins in facilities management using a structured literature review and characterized similar characteristics and definitions by different authors. Additionally, DT implementation in different FM application areas was quantified and analyzed. Results show that DT implementation in FM is still at nascent stages with major challenges surrounding standardization and data integration.

Intertwining smart manufacturing and the Internet of Things (IoT) is known as the Industrial Internet of Things (IIoT). IIoT improves product quality and reliability and requires intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. Recently, it has been increasingly deployed; however, multi-party collaborative information processing is often required in heterogeneous IIoT. The security and efficiency requirements of each party interacting with other partners have become a significant challenge in information security. This paper proposes an automated smart manufacturing framework based on Digital Twin (DT) and Blockchain. The data used in the DT are all from the cluster generated after blockchain authentication. The processed data in the DT will only be accessed and visualized in the cloud when necessary. Therefore, all the data transmitted in the process are result reports, avoiding the frequent transmission of sensitive data. Simulation results show that the proposed authentication mode takes less time than the standard protocol. In addition, our DT framework for a smart factory deploys the PDQN DRL model, proving to have higher accuracy, stability, and reliability.

This document presents the protocol adopted to perform a systematic review for the DTADD project following the checklist provided by PRISMA and guidelines of the PRISMA-P Explanation and Elaboration.

This document presents the search strategy adopted to perform the systematic review of the DTADD project and it is based on the PRISMA-S checklist.

Digital twins (DTs) and eXtended Reality (XR) are two core technological enablers for engineering in the Metaverse that can accelerate the human-centric Industry 5.0 transformation. The digital twin technology provides a digital representation of a physical asset with data linkages for inspection, monitoring, and prediction of complex processes or systems, while eXtended reality offers real-and-virtual combined environments for human users to interact with machines. However, the synergies between digital twins and eXtended reality remain understudied. This work addresses this research gap by introducing a novel method “TwinXR” that leverages ontology-based descriptions of Digital twins, i.e., digital twin documents, in industrial eXtended reality applications. To ease the use of the TwinXR method, we publish a Unity package that allows data flow and conversion between eXtended reality applications and digital twin documents on the server. Finally, the work applies the TwinXR method in two industrial eXtended reality applications involving overhead cranes and a robot arm to demonstrate the use and indicate the validity of the method. We conclude that the TwinXR method is a promising way to advance the synergies between digital twins and eXtended reality: For eXtended reality, TwinXR enables efficient and scalable eXtended reality development; For digital twins, TwinXR unlocks and demonstrates the potential of digital twins for data interchange and system interoperation. Future work includes introducing more detailed principles of Semantic Web and Knowledge Graph, as well as developing factory-level TwinXR-compatible applications.

Data centres are becoming an increasingly important part of our society’s infrastructure. The number of data centres is growing constantly, making growing the gross level of electrical energy consumption. At the same time, the rapid spread of sophisticated electrical devices as well as other automation systems, in general, produces an opportunity for making data centres an attractive player in the constantly designing energy market. But for this, new advanced technologies must be applied to solve the problems of complexity and heterogeneity in various types of data centre design. A new concept, which is based on the automated generation of a digital twin (DT) system, is directly from its schematic representations presented in this paper. A DT is a virtual version of an object or system, designed to aid decision-making and virtual commissioning through simulation, machine learning, and reasoning. In the scope of current work, the IEC 61850 standard is chosen as a starting point for a multi-step generation of the DT combining simulation model and decentralized control logic. As a result, the designed DT “clone” of an electrical system consists of the SIMULINK model of the electrical system plus the automatically generated control application (based on the IEC 61499 standard). Validerad;2023;Nivå 2;2023-02-09 (joosat);Funder: ERA-Net SES 2018 (RegSys)Licens fulltext: CC BY License

Herbal remedies are in most cases still manufactured with traditional equipment installations and processes. Innovative chemical process engineering methods such as modeling and process intensification with green technology could contribute to the economic and ecologic future of those botanicals. The integration of modern unit operations such as water-based pressurized hot water extraction and inline measurement devices for process analytical technology approaches in traditional extraction processes is exemplified. The regulatory concept is based on the quality-by-design demand for autonomous feed-based recipe operation with the aid of digital twins within advanced process control. This may include real-time release testing to the automatic cleaning of validation issues. Digitalization and Industry 4.0 methods, including machine learning and artificial intelligence, are capable of keeping natural product extraction manufacturing and can contribute significantly to the future of human health.

[Abstract:] Data science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DTw) able to mirror or predict the performance of its corresponding physical twins. Therefore, the main target of the Topical Collection is exploring how the development of DTw can benefit the development of safe, efficient solutions for the pre-disposal and disposal of radioactive waste. A particular challenge for DTw in radioactive waste management is the combination of concepts from geological modelling and underground construction which will be addressed by linking structural and multi-physics/chemistry process models to building or tunnel information models. As for technical systems, engineered structures a variety of DTw approaches already exist, the development of DTw concepts for geological systems poses a particular challenge when taking the complexities (structures and processes) and uncertainties at extremely varying time and spatial scales of subsurface environments into account. This work has been financed within the framework of EURAD, the European Joint Programme on Radioactive Waste Management (Grant Agreement No 847593) and PREDIS (Pre-disposal management of radioactive waste, Euratom research and training programme, grant agreement No 945098). The contribution of Javier Samper (UDC) was partly funded by Project PID2019-109544RB-I00). These supports are gratefully acknowledged. Open Access funding enabled and organized by Projekt DEAL.

Despite increased interest in the development of smart cities and urban spaces that cater to the needs of their inhabitants, there is a significant lack of information and experience when it comes to working with older people. URBANAGE is a European H2020 project focused on supporting urban planners and policymakers in the decision-making process for age-friendly cities by developing new technologies for evidence-based decision-making. Older adults over 60 years of age, public servants and other relevant stakeholders were invited to co-create and test the solution to ensure that their needs and challenges were being addressed by the project. Decision-makers are facing major challenges in terms of understanding and addressing the needs of vulnerable population groups, such as older people, because of the lack of large enough datasets of disaggregated anonymised data. In this article, we report on the main challenges encountered during the implementation of the URBANAGE project, and the development of the components for big data analytics, visualisations, predictive algorithms and simulation. Using examples from three European locations - Helsinki, Flanders and Santander - we describe and discuss how we can gather personal data related to the daily lives of older people in terms of the existing privacy and data protection laws in the EU. The use of new technologies, such as location-based information devices, can provide up-to-date and precise information regarding problems that older people face while moving around the city, but they pose privacy concerns at the same time.

Digital twins are becoming an integral part of mechanical systems in industries due to the several advantages that they can provide such as performance analysis, predictive maintenance, and real-time monitoring. However, to allow their usage by non-experts and integration with digital twins of other interacting systems, the model behind the digital twin should be simple, physics based, and yet accurate enough to serve its purpose. This article presents such a model for counterbalance valves (CBVs) which are commonly found in a variety of hydraulic systems. The model consists of parameters that account for the internal geometry, inertia, and friction characteristics of the CBV. To illustrate the parameter identification strategy for this model, a test set up is designed, and four sets of experiments are conducted. A subset of the measured data is used in a systematic identification of the parameters. The rest of the data is used to validate the CBV model and the parameter identification strategy. The results indicate that the model is successfully able to predict the behavior of the CBV across different operating conditions, thereby, validating the model. The proposed CBV model satisfies key requirements of digital twins and thus, is a suitable candidate for the digital twin of CBVs and can easily be integrated in the digital twins of larger systems where the CBVs are installed. Version of record