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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.

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.

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.

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.

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.

To improve risk stratification in extracranial internal carotid artery disease (CAD), patients who would benefit maximally from revascularization must be identified. In cardiology, the fractional flow reserve (FFR) has become a reference standard for evaluating the functional severity of coronary artery stenosis, and noninvasive surrogates thereof relying on computational fluid dynamics (CFD) have been developed. Here, we present a CFD-based workflow using digital twins of patients’ carotid bifurcations derived from computed tomography angiography for the noninvasive functional assessment of CAD. We reconstructed patient-specific digital twins of 37 carotid bifurcations. We implemented a CFD model using common carotid artery peak systolic velocity (PSV) acquired with Doppler ultrasound (DUS) as inlet boundary condition and a two-element Windkessel model as oulet boundary condition. The agreement between CFD and DUS on the PSV in the internal carotid artery (ICA) was then compared. The relative error for the agreement between DUS and CFD was 9% ± 20% and the intraclass correlation coefficient was 0.88. Furthermore, hyperemic simulations in a physiological range were feasible and unmasked markedly different pressure drops along two ICA stenoses with similar degree of narrowing under comparable ICA blood flow. Hereby, we lay the foundation for prospective studies on noninvasive CFD-based derivation of metrics similar to the FFR for the assessment of CAD., NeuroImage: Clinical, 38, ISSN:2213-1582

We present an open-source software pipeline to create patient-specific left atrial (LA) models with fibre orientations and a fibrosis map, suitable for electrophysiology simulations. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter/intra-operator variability. Each output model consisted of (1) a labelled surface mesh open at the pulmonary veins (PV) and mitral valve (MV), (2) fibre orientations mapped from a diffusion tensor MRI human atlas, (3) fibrosis map from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. We evaluated reproducibility in our pipeline by comparing agreement in shape of the output meshes, fibrosis distribution in the LA body, and fibre orientations; simulations outputs were evaluated comparing total activation times of LAT maps, mean conduction velocity (CV), and structural similarity index measure (SSIM) of PS maps. Our workflow allows a single model to be created in 16.72 +/- 12.25 minutes. Results in this abstract are reported as inter/intra. Shape only differed noticeably with users' selection of the MV and the length of the PV from the ostia to the distal end; fibrosis agreement (0.91/0.99 ICC) and fibre orientation agreement (60.63/71.77 %) were high. LAT maps showed good agreement, the median of the absolute difference of the total activation times was 2.02ms/1.37ms. The average of the mean CV difference was -4.04mm/s / 2.1mm/s. PS maps showed a moderately good agreement with SSIM of 0.648/0.608. Although we found notable differences in the models due to user input, our tests show that operator variability was comparable to that of image resolution or fibre estimation., Comment: 17 pages, 7 figures, submitted for review at Journal of Computers in Biology and Medicine (in press)

Digital twins are gaining attention in healthcare, especially in fields like hospital management, simulating surgeries, or providing personalized health. As digital replicas based on users' data, digital twins can inform citizens in-depth about their lifestyle, medical data, and biomedical data. Hence, there is the assumption that digital twins could facilitate preventative healthcare at home, bringing healthcare closer to citizens, yet there are underexamined ethical concerns. In this paper, we explore the ethics of digital twins based on citizens' perspectives on digital twins in healthcare via recent literature and research. Although digital twins have great potential, citizens have concerns about surveillance, data ownership, data accuracy, and personal and collective agency.

Digital technologies play an increasingly important role in addressing environmental challenges, such as climate change and resource depletion. Yet, the characteristics and implications of digitalized environmental governance are still under-conceptualized. In this perspective, we distinguish three dimensions of governance: (1) seeing and knowing, (2) participation and engagement, and (3) interventions and actions. For each dimension, we provide a critical perspective on the shifts that digital technologies generate in governance. We argue against the assumption that the use of digital technologies automatically results in improved outcomes or in more democratic decision-making. Instead, attention needs to be paid to the wider political and normative context in which digital technologies are proposed, designed, and used as environmental governance tools. We conclude with key questions for academics and policymakers to broaden the debate on responsible design and use of digital technologies in environmental governance.

A workflow for editing and rendering 360° images (also called spherical or equirectangular images) is illustrated and discussed. The aim was to develop a novel procedure able to overcome limitations induced by deformations in images acquired with low-cost 360° cameras. Editing and rendering are carried on equirectangular projections using a dynamic procedure that turns images into traditional central perspectives, enabling the user to change viewing directions, modify digital content, and store the changes in the original files. Integration of the rendered projections with traditional outputs of digital recording operations as well as other deliverables of the architectural restoration project is also discussed. Finally, the development of a web-GIS application that stores and shares multiple deliverables (including the newly proposed renders) is presented.

Background: The European Commission (EC) has launched the European Green Deal communication, setting out the path for a fundamental transformation of Europe. Key element in this policy is a fully sustainable food system outlined in the farm-to-fork strategy. Such strategy requires a systems approach in which all aspects related to the production and consumption of sufficient and healthy food are considered, including economic, environmental (climate, ecosystems) and social aspects. Scope and approach: Here, we present the systems approach concept for food production, following the farm-to-fork principle as embraced by the EC, and elaborate on how digitalisation and Artificial Intelligence (AI) can solve the challenges that a sustainable food system imposes. Key findings and conclusions: We present a number of research and innovation challenges and illustrate these by some specific examples. It is concluded that AI and digitalisation show great potential to support the transition towards a sustainable food system. This development will impact the roles and interactions of the actors in the entire value chain from farmers to consumers. Policy recommendations are made for a successful future implementation of AI in sustainable food production.

In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA) – a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and data-driven modeling (DDM) to create generalizable, trustworthy, accurate, computationally efficient and self-evolving models. CoSTA achieves this objective by augmenting the governing equation of a PBM model with a corrective source term generated using a deep neural network. In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is found to outperform comparable DDM and PBM models in terms of accuracy – often reducing predictive errors by several orders of magnitude – while also generalizing better than pure DDM. Due to its flexible but solid theoretical foundation, CoSTA provides a modular framework for leveraging novel developments within both PBM and DDM. Its theoretical foundation also ensures that CoSTA can be used to model any system governed by (deterministic) partial differential equations. Moreover, CoSTA facilitates interpretation of the DNN-generated source term within the context of PBM, which results in improved explainability of the DNN. These factors make CoSTA a potential door-opener for data-driven techniques to enter high-stakes applications previously reserved for pure PBM.

The Covid-19 pandemic accelerated research and development not only in infectious diseases but also in digital technologies to improve monitoring, forecasting, and intervening on planetary and ecological risks. In the European Commission, the Destination Earth (DestinE) is a current major initiative to develop a digital model of the Earth (a "digital twin") with high precision. Moreover, omics systems science is undergoing digital transformation impacting nearly all dimensions of the field, including real-time phenotype capture to data analytics using machine learning and artificial intelligence, to name but a few emerging frontiers. We discuss the ways in which the current ongoing digital transformation in omics offers synergies with digital twins/DestinE. Importantly, we note here the rise of a new field of scholarship, planetary health genomics. We conclude that digital transformation in public and private sectors, digital twins/DestinE, and their convergence with omics systems science are poised to build robust capacities for pandemic preparedness and resilient societies in the 21st century.

Medical image segmentation, especially for biological soft tissues, is an issue of great interest. The aim of this study is to evaluate the segmentation performance of a commercial and an open-source software, to segment aortic root and coronary arteries. 3D printing stereolithography technology was used to generate ground truth models, which were then re-acquired by means of a micro-CT scanner. Measurements from the printed and reconstructed models with both the software were compared, in order to evaluate the level of agreement. In the second phase of this study, Computational Fluid Dynamics (CFD) simulations were conducted, to compare the outputs between the models segmented with the two software. The goal was to understand how differences in the segmentation process propagate in CFD results. Results showed that both software guarantee satisfactory segmentation performance, with average geometrical differences between reconstructed and physical models in the order of a few percentage points. However, when we consider thin details, as a sharp stenotic region, the commercial validated software seems to be more accurate in replicating the real anatomy. We also realized how apparently negligible geometrical differences, varying the employed software, can turn into enormous variations of hemodynamic parameters, such as velocity and wall shear stress, which place in the centre the delicate role the segmentation process holds. This evidence is crucial in the biomedical field and especially in a coronary arteries study, where CFD simulations can be exploited as a starting point for surgery considerations.

Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating a digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic state-of-the-art composition along with corresponding limitations, three dimensions of the future digital twins for the aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.

The transformation toward electromobility presents a significant challenge to the battery cell production processes. It is, therefore, necessary to establish efficient and robust operations and machines to meet the forecasted demand for manufactured cell capacity. Currently, the development and ramp-up phases of production machines, especially for cell stack assembly, are characterized by high material scrap rates and large personnel expenses. Aspects such as the web tension of electrodes or separator materials during separation and stack assembly have a significant influence on the subsequent intermediate product properties, and therefore present a key challenge. Herein, the optimal methodology for the web tension control of electrode materials is determined via a model-based approach by the means of a digital twin. The focus is hereby set on a production machine for the flexible cell stack assembly. Appropriate control methods are implemented and validated on the real system, thus reducing material scrap rates and overall costs. The control of the web tension by means of an unwinder and material storage was found to be optimal on the basis of the model and confirmed experimentally.

Bearing is a key part of rotating machinery. Accurate prediction of bearing life can avoid serious failures. To address the current problem of low accuracy and poor predictability of bearing life prediction, a bearing life prediction method based on digital twins is proposed. Firstly, the vibration signals of rolling bearings are collected, and the time-domain and frequency-domain features of the actual data set are extracted to construct the feature matrix. Then unsupervised classification and feature selection are carried out by improving the self-organizing feature mapping method. Using sensitive features to construct a twin dataset framework and using the integrated learning CatBoost method to supplement the missing data sets, a complete digital twin dataset is formed. Secondly, important information is extracted through macro and micro attention mechanisms to achieve weight amplification. The life prediction of rolling bearing is realized by using fusion features. Finally, the proposed method is verified by experiments. The experimental results show that this method can predict the bearing life with a limited amount of measured data, which is superior to other prediction methods and can provide a new idea for the health prediction and management of mechanical components.

Two key technologies that are at the heart of the Executable Digital Twins are Model Order Reduction and Smart Virtual Sensing (SVS). The former allows the high-fidelity models to be represented in compact forms and the latter is an on-line estimator framework which allows to extend the limits of physical sensors and provide full-field data using the simulation models. Previous work from the authors established a time-stable vibro-acoustic formulation and a Krylov-based reduction technique to create reduced order models (ROMs). The present work extends the methodology to high order FEM and to a new Infinite Element formulation called Flexible Infinite Elements (Flexible IFEM). With Flexible IFEM, it is possible to use general convex enclosures around the FEM. The paper will demonstrate reduction of the large models, the accuracy and performance gains as compared to conventional FEM and IFEM formulations and how these ROMs can be used in combination with a real-life SVS application case. No ISBN/ISSN ispartof: Proceedings of ISMA/USD 2022 ispartof: ISMA/USD 2022 location:Leuven, Belgium status: Published online

[Resumen] La globalización de los mercados y la exigencia cada vez mayor de los clientes ha hecho que la industria evolucione hacia lo que se conoce como industria 4.0. Las fábricas, conocidas como SmartFactories, tienen la posibilidad de tomar decisiones en tiempo real, teniendo para ello conocimiento de cómo se encuentran. Un concepto clave para conseguir dichos objetivos, son los Gemelos Digitales (DT). Un DT consiste en una representación virtual de la realidad a lo largo de las diferentes fases del ciclo de vida de un producto. Hoy en día existen en el mercado diferentes productos propietarios que combinan aplicaciones propietarias que rara vez pueden ser intercambiables o combinables. Además del coste de dichos productos se le añade la incompatibilidad de unos con otros por lo que se considera necesaria una plataforma abierta que permita la generación de Gemelos Digitales. Este trabajo, propone una plataforma de código abierto y las directrices para la generación de DT de estaciones que formarán parte de un proceso de producción. [Abstract] The globalisation of markets and the ever-increasing demands of customers have led industry to evolve towards what is known as Industry 4.0. SmartFactories have the ability to make decisions in real time, with the knowledge of how they are doing. A key concept to achieve these goals is the Digital Twin (DT). A DT is a virtual representation of reality throughout the different phases of a product's life cycle. Today there are several proprietary products on the market that combine proprietary applications that are rarely interchangeable or combinable. In addition to the cost of these products, the incompatibility of some with others is added to the cost of others, which is why an open platform that allows the generation of Digital Twins is considered necessary. This work proposes an open source platform and guidelines for the generation of DTs of stations that will be part of a production process. Los autores quieren agradecer la subvención parcial de este trabajo a través de los proyectos PID2019-110291RB-I00 y FEDER Andalucía con el código FEDER A1123060E00010 y la referencia 1380776 Universidad de Jaén; 1380776

[Resumen] Este artículo muestra la metodología desarrollada y los resultados obtenidos por el grupo 3DVC&R de la UCLM en la obtención de modelos térmicos de interiores de edificios con robots móviles. El documento revisa las etapas principales de planificación, adquisición de datos, procesamiento y creación del modelo geométrico-térmico semántico. El sistema y el método propuestos han sido experimentados con éxito en interiores de edificios a nivel de planta. Se presentan los resultados en un caso de estudio de 187 m2 de superficie, con 5 zonas comunicadas por varias puertas. Este trabajo se considera un primer paso hacia la consecución de gemelos digitales térmicos de edificaciones en el entorno de la ingeniería, arquitectura y construcción. [Abstract] This article shows the methodology developed and the results obtained by the UCLM 3DVC&R group regarding thermal models of building indoors using mobile robots. The document reviews the main four stages of the method: planning, data acquisition, data processing and creation of the semantic geometric thermal model. The proposed system and method have been successfully tested inside buildings at a floor level. The results are presented in a case study of 187 m2 of surface, with 5 areas connected by doors. This work is considered as a first step that leads to the creation of thermal digital twins of buildings in the engineering, architecture and construction fields. Esta investigación ha sido financiada por los proyectos SBPLY/19/180501/000094 (European Regional Development Fund) y PID2019-108271RB-C31 (Ministerio de Ciencia e Innovación).

The purpose of this work is to explore the role of the safe and optimal scheduling of thermal energy storage systems in intelligent buildings in promoting sustainable economic development under Digital Twins (DTs) technology. Phase Change Material (PCM) has high energy density, constant temperature storage, small footprint, and long service life. Here, PCM is first placed in the indoor building structure, and the DTs technology is introduced. In the development of intelligent buildings, the data generated by the energy storage system of intelligent buildings in the real space can be mapped to the virtual space in real time for simultaneous analysis. In addition, the PCM wall structure and thermal network DTs model are designed for the intelligent building. In addition, the PCW structure is used to build a thermal energy storage and dispatch model of the smart thermoelectric building based on DTs. Finally, the model is evaluated and analyzed experimentally. The analysis of system optimization power under different schemes indicates that the scheduling operation strategy of thermal energy storage of building walls can avoid overcharging or over-discharging batteries in the microgrid and reduce battery power consumption. Besides, the building wall energy storage capacity is always in the range of 0.2 ∼ 0.8 on the all-weather scale. Moreover, the model constructed here achieves significantly lower economic costs, environmental costs, and energy costs and a better energy-saving effect than the existing model. The model built here can serve as experimental reference for further digital energy storage in intelligent buildings and comprehensive energy utilization because of its superior safety performance and lower consumption.

The ocean holds abundant resources, but the utilization of those resources for the marine economy presents a complex and dynamic industrial situation. Exploring sustainable development in this industry is of practical value, as it involves the rational use of marine resources while protecting the environment. This study provides an innovative review of the current application status of Digital Twins Technology (DTT) in various sectors of the marine industry, including the ship-building industry (SBI), Offshore Oil and Gas Industry, marine fishery, and marine energy industry. The findings reveal that DTT offers robust support for full life cycle management (LCM) in SBI, including digital design, intelligent processing, operation, and error management. Furthermore, this work delves into the challenges and prospects of DTT application in the marine industry, aiming to provide reference and direction for intelligent systems in the industry and guide the rational development and utilization of marine resources in the future.

Building Information Modelling (BIM) is used in construction projects to improve efficiency. In general, it improves project management and monitoring, reduces time and costs, strengthens collaboration among stakeholders, and reduces risks and injuries. Sensor technologies with various functionalities are being implemented in the construction site. Technologies such as tracking workers and transmitting real-time site inspections to support the BIM tools in various fields are being used. To evaluate sensor technologies’ usability and effectiveness in Architecture, Engineering, Construction and Operations (AECO) sector, a literature review adapting PRISMA Statement is proposed. Consequently, this document represents a complementary manuscript as a PRISMA Protocol (PRISMA-P). Relevant articles are collected from top electronic databases in the construction field. This review aims to investigate the sensor connected BIM models and the technologies being utilised in the AECO sector, investigating the targeted groups, construction field, and the system architectures being used. Furthermore, the study will examine the case studies of the implemented sensors to evaluate each technology’s effectiveness. Finally, the reliability of the tools and future proposals is projected.

Autonomous Fifth Generation (5G) and Beyond 5G (B5G) networks require modelling tools to predict the impact on the performance when new configurations and features are applied in the network. Modeling modern networks through traditional mathematical analysis can lead to low accuracy, while the execution time and resource usage are high in network simulators. Machine Learning (ML) algorithms, and specifically Graph Neural Networks (GNNs), are suggested as a promising alternative since they can capture complex relationships from graph-like data, predicting properties with high accuracy and low resource requirements. However, they cannot generalize to larger networks, as their prediction accuracy decreases when input data (e.g., network topologies) is significantly different (e.g., larger) than the training data. This paper addresses the GNNs scalability issue by following a step-by-step approach, exploiting networking concepts to improve a baseline model. This work is framed in the 2021 International Telecommunication Union (ITU) and Barcelona Neural Networking Center - Universitat Politècnica de Catalunya (BNN-UPC) challenge. Results show that by following the suggested steps, applied on the RouteNet baseline developed by the BNN-UPC, can lower the Mean Average Percentage Error (MAPE) from 187.28% to 1.838%, improving the generalization significantly over larger graphs. Our approach is more simple than other solutions that participated in the challenge, but obtained similar results.

ispartof: Proceedings of hEART 2022 ispartof: 10th Symposium of the European Association for Research in Transportation (hEART 2022) location:Leuven date:1 Jun - 3 Jun 2022 status: Published online

At present, the integration of green building, the intelligent building industry and high-quality development are facing a series of new opportunities and challenges. This review aims to analyze the digital development of smart green buildings to make it easier to create contiguous ecological development areas in green ecological cities. It sorts out the main contents of Intelligent Green Buildings (IGB) and summarizes the application and role of Digital Twins (DTs) in intelligent buildings. Firstly, the basic connotations and development direction of IGB are deeply discussed, and the current realization and applications of IGB are analyzed. Then, the advantages of DTs are further investigated in the context of IGB for DT smart cities. Finally, the development trends and challenges of IGB are analyzed. After a review and research, it is found that the realization and application of IGB have been implemented, but the application of DTs remains not quite integrated into the design of IGB. Therefore, a forward-looking design is required when designing the IGBs, such as prioritizing sustainable development, people’s livelihoods and green structures. At the same time, an IGB can only show its significance after the basic process of building the application layer is performed correctly. Therefore, this review contributes to the proper integration of IGB and urban development strategies, which are crucial to encouraging the long-term development of cities, thus providing a theoretical basis and practical experience for promoting the development of smart cities.

This Powerpoint is part of the project Twomes. This deliverable contains a presentation about the heat balance model and inverse-grey-box analysis tools that support automated estimation of crucial parameters of buildings, installations and comfort needs in individual homes and utility buildings based on time-series monitoring data. The presentation also includes results and conclusions of the analysis of data collected in the Assendorp neighbourhood in Zwolle, the Netherlands. Finally, this deliverable also contains a link to a GitHub repository with source code for the Twomes digital twin. This project is supported by TechForFuture (https://techforfuture.nl), the Centre of Expertise HTSM Oost, an initiative of Saxion and Windesheim.

The development of Digital Twins has enabled them to be widely applied to various fields represented by intelligent manufacturing. A Metaverse, which is parallel to the physical world, needs mature and secure Digital Twins technology in addition to Parallel Intelligence to enable it to evolve autonomously. We propose that Blockchain combined with other areas does not simultaneously require all of the basic elements. We extract the immutable characteristics of Blockchain and propose a secure multidimensional data storage solution called BlockNet that can ensure the security of the digital mapping process of the Internet of Things, thereby improving the data reliability of Digital Twins. Additionally, to address some of the challenges faced by multiscale spatial data processing, we propose a nonmutagenic multidimensional Hash Geocoding method, allowing unique indexing of multidimensional information and avoiding information loss due to data dimensionality reduction while improving the efficiency of information retrieval and facilitating the implementation of the Metaverse through spatial Digital Twins based on these two studies. © 2022 The Author(s) Export Date: 31 May 2022; Review; 通讯地址: Lv, Z.; Department of Game Design, Sweden; 电子邮件: lvzhihan@gmail.com; 基金资助详情: National Natural Science Foundation of China, NSFC, 61902203; 基金资助详情: Fundo para o Desenvolvimento das Ciências e da Tecnologia, FDCT, 0050/2020/A1; 基金资助文本 1: This work was supported in part by the National Natural Science Foundation of China under grant no. 61902203 and the Science and Technology Development Fund , Macau SAR (file no. 0050/2020/A1 ).; 参考文献: Dionisio, J.D.N., Burns, W.G., III, Gilbert, R., 3d virtual worlds and the metaverse: current status and future possibilities (2013) ACM Comput. 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System identification plays an important role in system control and controller tuning. Data- driven identification provides a behavioristic model of an identified system or process that can be useful for remote controller tuning by simulating the response of an identified system or process. The identification can be performed using various methods, therefore, it is necessary to examine available methods for the case study. The aim of this paper is to describe and perform the identification procedure for the case study of flow control process using the Matlab’s System Identification Toolbox. As a result, a continuous-time transfer function model is formed, which is to be used for PID controller tuning and a digital twin development purposes in further work.

Due to its capacity to evolve in a large solution space, the Simulated Annealing (SA) algorithm has shown very promising results for the Reverse Engineering of editable CAD geometries including parametric 2D sketches, 3D CAD parts and assem blies. However, parameter setting is a key factor for its performance, but it is also awkward work. This paper addresses the way a SA-based Reverse Engineering technique can be enhanced by identifying its optimal default setting parameters for the ftting of CAD geometries to point clouds of digitized parts. The method integrates a sensitivity analysis to characterize the impact of the variations in the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud to be ftted. The principles underpinning the adopted ftting algorithm are briefy recalled. A framework that uses design of experiments (DOEs) is introduced to identify and save in a database the best setting parameter values for given CAD models. This database is then exploited when considering the ftting of a new CAD model. Using similar ity assessment, it is then possible to reuse the best setting parameter values of the most similar CAD model found in the database. The applied sensitivity analysis is described together with the comparison of the resulting sensitivity evolution curves with the changes in the CAD model parameters imposed by the SA algorithm. Possible improvements suggested by the analysis are implemented to enhance the efciency of SA-based ftting. The overall approach is illustrated on the ftting of single mechanical parts but it can be directly extended to the ftting of parts’ assemblies. It is particularly interesting in the context of the Industry 4.0 to update and maintain the coherence of the digital twins with respect to the evolution of the associated physical products and systems.

Engineering cyber-physical systems inhabiting contemporary urban spatial environments demands software engineering facilities to support design and operation. Tools and approaches in civil engineering and architectural informatics produce artifacts that are geometrical or geographical representations describing physical spaces. The models we consider conform to the CityGML standard; although relying on international standards and accessible in machine-readable formats, such physical space descriptions often lack semantic information that can be used to support analyses. In our context, analysis as commonly understood in software engineering refers to reasoning on properties of an abstracted model—in this case a city design. We support model-based development, firstly by providing a way to derive analyzable models from CityGML descriptions, and secondly, we ensure that changes performed are propagated correctly. Essentially, a digital twin of a city is kept synchronized, in both directions, with the information from the actual city. Specifically, our formal programming technique and accompanying technical framework assure that relevant information added, or changes applied to the domain (resp. analyzable) model are reflected back in the analyzable (resp. domain) model automatically and coherently. The technique developed is rooted in the theory of bidirectional transformations, which guarantees that synchronization between models is consistent and well behaved. Produced models can bootstrap graph-theoretic, spatial or dynamic analyses. We demonstrate that bidirectional transformations can be achieved in practice on real city models.

This paper addresses the way a simulated annealing-based fitting strategy can be enhanced by leveraging a sensitivity analysis able to characterize the impact of the variations in the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud of the digitized part to be fitted. The principles underpinning the adopted fitting algorithm are briefly recalled. The applied sensitivity analysis is described together with the comparison of the resulting sensitivity evolution curves with the changes in the CAD model parameters imposed by the simulated annealing algorithm. This analysis suggests several possible improvements that are discussed. The overall approach is illustrated on the fitting of single mechanical parts but it can be directly extended to the fitting of parts’ assemblies. It is particularly interesting in the context of the Industry 4.0 to update digital twins of physical products and systems.

Digital twins are expected to form a network, a “Digital Twin Web,” in the future. Digital Twin Web follows a similar structure to the World Wide Web and consists of meta-level digital twins that are described as digital twin description documents and distributed via Digital Twin Web servers. Standards must be established before the Digital Twin Web can be used efficiently, and having an easily accessible server implementation can foster the development of those standards. Twinbase is an open-source, Git-based Digital Twin Web server developed with user-friendliness in mind. Twinbase stores digital twin documents in a Git repository, modifies them with Git workflows, and distributes them to users via a static web server, from which the documents can be accessed via a client library or a regular web browser. A demo server is available at https://dtw.twinbase.org and new server instances can be initialized free-of-charge at GitHub via its browser interface. Twinbase is built with GitHub repository, Pages, and Actions but can be extended to support other providers or self-hosting. We describe the underlying architecture of Twinbase to support the creation of derivative and alternative server implementations. The Digital Twin Web requires permanent, globally accessible, and transferable identifiers to function properly, and to address this issue, we introduce the concept of digital twin identifier registry. Performance measurements showed that the median response times for fetching a digital twin document from Twinbase varied between 0.4 and 1.2 seconds depending on the identifier registry.

The recent industrial scenario was defined by the emergence of digital twins and cyber physical systems as key elements for manufacturers leadership. Digital models can perform good in terms of production planning and control decisions if they are correctly representing their physical counterparts at anytime. Discrete event simulation can be considered as established digital models of manufacturing system, thanks to the proven capabilities of correctly estimating the system performances. Automated simulation model generation techniques can significantly reduce model development phases and allow for using simulation models for short term decisions in production. Application studies and test cases are scarce in the literature. In this paper, we present the application of a digital model generation method. The test case is done exploiting a lab-scale model of a manufacturing system composed by six stations. We investigate how the model generation works online, during the transient phase of a manufacturing system. Results confirm the real-time applicability of the approach provided that sufficient data points are available from the production event logs.

The present paper presents the first-of-its-kind digital twin for a furnace operating in flameless combustion conditions. A methodology combining data compression, by means of Proper Orthogonal Decomposition (POD), and interpolation, using Kriging, was developed to design physics-based, reduced-order models (ROMs) for the prediction of combustion data at unexplored operating conditions. Three-dimensional simulations with detailed chemistry were carried out, spanning a wide range of operating conditions in terms of fuel composition (methane-hydrogen mixtures from pure methane to pure hydrogen), equivalence ratio (from 0.7 to 1) and air injector diameter (to adjust the air jet entrainment). Based on the available simulations, a ROM was developed, to predict both spatial fields, local and integral values of thermochemical variables at working conditions not included in the ROM development. Results showed that the developed ROM could reliably predict the temperature and main chemical species distribution in the furnace with an overall error below 10%, proving the effectiveness of the approach for the development of digital twins of combustion systems. A remarkable accuracy was observed for the prediction of specific quantities, including wall temperatures, OH decay length, OH peak value and location and exhaust gas composition, including pollutants, with prediction errors always below 5%, showing the potential of the approach to develop soft sensors., SCOPUS: ar.j, info:eu-repo/semantics/published

Roll-to-roll gravure (R2Rg) has become highly affiliated with printed electronics in the past few years due to its high yield of printed thin-film transistor (TFT) in active matrix devices, and to its low cost. For printing TFTs with multilayer structures, achieving a high-precision in overlay printing registration accuracy (OPRA) is a key challenge to attain the high degree of TFT integration through R2Rg. To address this challenge efficiently, a digital twin paradigm was first introduced in the R2Rg system with an aim to optimize the OPRA by developing a predictive model based on typical input variables such as web tension, nip force, and printing speed in the R2Rg system. In our introductory-level digital twin, errors in the OPRA were collected with the variable parameters of web tensions, nip forces, and printing speeds from several R2Rg printing processes. Subsequently, statistical features were extracted from the input data followed by the training of a deep learning long-short term memory (LSTM) model for predicting machine directional error (MD) in the OPRA. As a result of training the LSTM model in our digital twin, its attained accuracy of prediction was 77%. Based on this result, we studied the relationship between the nip forces and printing speeds to predict the MD error in the OPRA. The results indicated a correlation between the MD error in the OPRA and the printing speed, as the MD error amplitude in the OPRA tended to decline at the higher printing speed.

An industrial linear stage is a device that is commonly used in robotics. To be precise, an industrial linear stage is an electro-mechanical system that includes a motor, electronics, flexible coupling, gear, ball screw, and precision linear bearing. A tight fit can provide better precision but also generates a difficult-to-model friction that is highly nonlinear and asymmetrical. Herein, this paper proposes an advanced trajectory controller based on a digital twin framework incorporated with artificial intelligence (AI), which can effectively control a precision linear stage. This framework offers several advantages: detection of abnormalities, estimation of performance, and selective control over any situation. The digital twin is developed via Matlab’s Simscape and runs concurrently having a real-time controller.

The use of digital twins in smart power systems at the stages of the life cycle is promising. The dynamics of such systems (smart energy renewable sources, smart energy hydrogen systems, etc.), are determined mainly by the physical and chemical processes occurring inside the systems. The basis for developing digital twins is reliable mathematical models of the systems. In the present paper, the authors present a method of energy processes mathematical prototyping—an overall approach to modeling processes of various physical and chemical natures based on modern non-equilibrium thermodynamics, mechanics, and electrodynamics. Controlled parameters are connected with measured ones by developing a theoretically correct system of process dynamics equations with accuracy up to the experimentally studied properties of substances and processes. Subsequent transformation into particular mathematical models of a specific class of systems makes this approach widely applicable. The properties of substances and processes are given in the form of functional dependencies on the state of the system up to experimentally determined constant coefficients. The authors consider algorithms for identifying the constant coefficients of the functions of substances and processes properties, which complement the proposed unified approach of designing models of various physical and chemical nature systems.

Liquid–liquid extraction has proven to be an aid in biologics manufacturing for cell and component separation. Because distribution coefficients and separation factors can be appropriately adjusted via phase screening, especially in aqueous two-phase systems, one stage is frequently feasible. For biologics separation, aqueous two-phase systems have proven to be feasible and efficient. The simple mixer–settler equipment type is still not standard in biologics manufacturing operations. Therefore, a scalable digital twin would be of aid for operator training, process design under the regulatory demanded quality by design approach for risk analysis, design and control space definition, and predictive maintenance. Autonomous operation is achieved with the aid of process analytical technology to update the digital twin to real time events and to allow process control near any optimal operation point. Autonomous operation is first demonstrated with an experimental feasibility study based on an industrial type example of pDNA manufacturing via lysis from E. coli with and without cell separation performance.

Aiming at the real-time robust optimization problem of perishable supply-chain systems in complex environments, a real-time robust optimization scheme based on supply-chain digital twins is proposed. Firstly, based on the quantitative logical relationship between production and sales of single-chain series supply-chain system products, the state space equation of the supply-chain system with logical characteristics, structural characteristics, and quantitative characteristics was constructed, and twin data were introduced to construct the digital twins of supply chains based on the state-space equation. Secondly, the perishable supply-chain system in complex environments was regarded as an uncertain closed-loop system from the perspective of the state space equation, and then a robust H∞ controller design strategy was proposed, and the supply-chain digital twins was used to update and correct the relevant parameters of the supply-chain system in real-time, to implement the real-time robust optimization based on the supply-chain digital twins. Finally, the simulation experiment was carried out with a cake supply-chain production as an example. The experimental results show that the real-time updating of relevant parameters through the digital twins can help enterprise managers to formulate reasonable management plans, effectively avoid the shortage problem of enterprises in the cake supply-chain system, and reduce the maximum inventory movement standard deviation of each link by 12.65%, 6.50%, and 14.87%, and the maximum production movement standard deviation by 70.21%, 56.84%, and 45.19%.

This paper presents a set of computational tools specially developed for supporting the operation and management of water distribution systems towards digital transformation of water services. These tools were developed in the scope of two R&D projects carried out in Portugal, DECIdE and WISDom, during 2018–2022. The DECIdE project focused on the development of tools for importing cadastral and operational data, as well as on the three operational tools for supporting the performance assessment: the first allows the calculation of different key performance indicators, both at a global and sectorial level, which is an annual requirement of the water regulator, and the other two allow the calculation of the water and the energy balances and a set of complementary indices. The WISDom project aimed at the implementation of applications that directly address specific water utility needs, namely, the flow rate data processing, the optimal location of pressure sensors, the identification of critical areas in the distribution network for pipe burst location, and the prioritization of pipes for rehabilitation. Implemented tools are useful to support water utilities in the daily operation and management of their systems, being a step forward towards digital transformation of the water sector.

Digitization offers new perspectives for educational research to identify the effects of visualizations regarding cognitive processing. In addition, new types of data can be generated, expanding the possibilities for visualizing cognitive processes and understanding human learning. Digital twins are already used in Industry 4.0, as an additional visualization to a real object, for data mining and data analysis for process optimization. The increasing integration of digital twins in the industrial sector requires the formulation of corresponding educational goals to ensure high-quality and future-oriented education. Therefore, future generations must be introduced to technologies from industry during their education. In this paper, an intelligent photometric measurement system called SmaEPho with a digital twin for science, technology, engineering, and mathematics (STEM) learning is presented. In addition to its function as a photometric measurement device, an intelligent sensor technology allows for data generation on the user’s usage behavior. The digital twin reflects and visualizes these data in real-time. This enables a variety of new didactic and methodological approaches in teaching. A first study evaluating the hardware and tracking components of SmaEPho shows that the deviation accuracy of the measurement system is sufficient for experimental applications in schools. Another study with n=52 students confirmed the excellent usability of the SmaEPho hardware platform. These research results lay the foundation for a variety of future research questions on data analysis and machine learning algorithms with the aim of increasing the quality of education. The use of intelligent digital twins as an element of digitization in educational contexts offers the extended possibility of identifying cognitive processing steps using this technology.

The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of network resources in the next decades. Among different B5G use cases, the Digital Twin (DT) has been identified as a key high bandwidth-demanding use case. The creation and operation of a DT require the continuous collection of an enormous and widely distributed amount of sensor telemetry data which can overwhelm the transport layer. Therefore, the reduction in such transported telemetry data is an essential objective of smart use case operation. Moreover, deep telemetry data analysis, i.e., anomaly detection, can be executed in a hierarchical way to reduce the processing needed to perform such analysis in a centralized way. In this paper, we propose a smart management system consisting of a hierarchical architecture for telemetry sensor data analysis using deep autoencoders (AEs). The system contains AE-based methods for the adaptive compression of telemetry time series data using pools of AEs (called AAC), as well as for anomaly detection in single (called SS-AD) and multiple (called MS-AGD) sensor streams. Numerical results using experimental telemetry data show compression ratios of up to 64% with reconstruction errors of less than 1%, clearly improving upon the benchmark state-of-the-art methods. In addition, fast and accurate anomaly detection is demonstrated for both single and multiple-sensor scenarios. Finally, a great reduction in transport network capacity resources of 50% and more is obtained by smart use case operation for distributed DT scenarios. This research was funded by the European Commission Horizon Europe SNS JU DESIRE6G project (G.A. 101096466), by the AEI through the IBON project (PID2020-114135RB-I00), and by the ICREA institution.

This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context. info:eu-repo/semantics/publishedVersion

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.