Search

This review paper aimed to draw the red line passing through almost 25 years of research on waterjet cutting carried out at WJ_Lab, the waterjet laboratory of the Department of Mechanical Engineering of Politecnico di Milano. The purpose was not to just historically analyse the obtained scientific results by themselves but to make them even more useful by introducing the concept of the waterjet digital twin passing through the accuracy improvements due to microAWJ. This strategy effectively creates synergy among the topics and gives the opportunity to researchers in this field to both have an example of how research in industrial manufacturing processes can be guided by scientific and industrial needs, at least from the author's point of view, and to appreciate how it can be made useful for further improvements by introducing a powerful concept as the digital twin. [ABSTRACT FROM AUTHOR]

A digital twin is recognized as a pivotal technology in a new type of power system monitoring as it provides an effective method for monitoring the vibration caused by ice shedding in overhead transmission lines. The digital twin model differs from traditional models in that it has the characteristics of precise mapping and real-time simulation. These emerging characteristics lead to urgent updating of the existing modeling approaches. Therefore, the current study proposes a dynamic digital twin modeling method for transmission line ice-shedding systems. In this approach, an analytical solution for conductor deicing oscillation is proposed to describe the span and tension unchanged in any time period and then segmented and iteratively corrected with measured time-varying parameters to implement real-time simulation functionality. A dynamic geometric model for transmission lines is proposed based on the Unity3D platform. In addition, a human-computer interaction visualization platform is proposed to display twin data, with the objective of realizing precise mapping of real transmission lines. Finally, an application of this systematic approach to continuous three-span wire demonstrates the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Improving power conversion efficiency (PCE) in photovoltaics has driven innovative approaches in solar cell design and technology. Silicon heterojunction (SHJ) solar cells exhibit advantages in PCE due to their effective passivating contact structures. SHJ–interdigitated back contact (SHJ–IBC) solar cells have the potential to surpass traditional SHJ cells, attributed to their advantage in short‐circuit current (JSC). Herein, Silvaco Atlas technology computer‐aided design is used to create digital twins of high‐efficiency SHJ solar cells with amorphous silicon and nanocrystalline silicon hole selective contact (HSC) layers. Using parameters from digital twins of SHJ solar cells, the practical efficiency limit of SHJ–IBC solar cells is assessed. The results show that SHJ–IBC cells can achieve potential efficiencies of 27.01% with amorphous HSC and 27.38% with nanocrystalline HSC. Further efficiency augmentation to 27.51% can be achieved by narrowing the gap from 80 to 20 μm. This study not only advances comprehension of SHJ–IBC solar cells but also provides insights into optimizing geometrical configurations for improved performance. The utilization of digital twins provides a valuable tool for predicting and evaluating the performance of SHJ–IBC solar cells, contributing substantively to the ongoing development of high‐efficiency photovoltaic technology. [ABSTRACT FROM AUTHOR]

The boom-type roadheader plays a crucial role in coal mining. However, conducting the real-time monitoring of the mechanical performance and comprehensive adaptive cutting in the dynamic cutting process are challenging. To address these issues, a digital twin system that integrates the elements of "shape, performance, and control" for roadheaders is presented in this paper. The system comprises three components: physical space, service space, and twin space. The service space forms the core of the entire system. Within this space, twin models and control models are created using numerical simulation, artificial intelligence and multi-source data fusion technology. These models serve the purpose of predicting the roadheader's mechanical performance and controlling the swing speed of the cutting arm. The physical space is built using technologies such as robot kinematics, electrical systems, hydraulic transmission, and other relevant techniques. This approach facilitates the transmission of multi-sensor data to twin models. The control model then manages the roadheader's function based on the output signals from the control model. The twin space is constructed utilizing physical rendering engines, databases, and 3D modelling tools. This space visualizes and stores the movement, performance, and control parameters of the roadheader. The results demonstrate that the average absolute error between the measured data from the test's three position strain gauges and the predicted data from the twin system is 10.38 MPa. Furthermore, the twin system achieves an average update interval of 0.34 s, allowing real-time stress monitoring of the structural components of the roadheader and preventing damage caused by overload. The proposed control model enables adaptive adjustment of the swing speed of the cutting arm in approximately 0.3 s. This improvement significantly enhances the adaptive cutting capabilities of roadheaders when dealing with complex coal and rock formations. [ABSTRACT FROM AUTHOR]

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation. [ABSTRACT FROM AUTHOR]

Performance in many racing sports depends on the ability of the athletes to produce and maintain the highest possible work i.e., the highest power for the duration of the race. To model this energy production in an individualized way, an adaptation and a reinterpretation (including a physiological meaning of parameters) of the three-component Margaria–Morton model were performed. The model is applied to the muscles involved in a given task. The introduction of physiological meanings was possible thanks to the measurement of physiological characteristics for a given athlete. A method for creating a digital twin was therefore proposed and applied for national-level cyclists. The twins thus created were validated by comparison with field performance, experimental observations, and literature data. Simulations of record times and 3-minute all-out tests were consistent with experimental data. Considering the literature, the model provided good estimates of the time course of muscle metabolite concentrations (e.g., lactate and phosphocreatine). It also simulated the behavior of oxygen kinetics at exercise onset and during recovery. This methodology has a wide range of applications, including prediction and optimization of the performance of individually modeled athletes. [ABSTRACT FROM AUTHOR]

This article reviews the development of digital twin and summarizes the evolution of the concept of digital twin. The key words and abstracts of 1187 relevant papers in recent 5 years are quantitatively analyzed, and 13 key nodes are obtained. Then it analyzes the content of 9 systematic review articles and summarizes 15 core contents. Then select 115 application examples from the articles. Through the above three steps, nine descriptive general characteristics of digital twin system are summarized: Entity object; Virtual object; Twinning degree; Communication; Data; Functionality; Human-computer interaction; systematicness; and Cost control. This paper will explain these features in detail and summarize the application examples of classifying each feature. This paper aims to summarize the development of digital twin in recent years and propose a description method of system characteristics according to the application scenario of digital twin and the increasingly complex situation of the system. [ABSTRACT FROM AUTHOR]

Monitoring SARS-CoV-2 spread is challenging due to asymptomatic infections, numerous variants, and population behavior changes from non-pharmaceutical interventions. We developed a Digital Twin model to simulate SARS-CoV-2 evolution in Catalonia. Continuous validation ensures our model's accuracy. Our system uses Catalonia Health Service data to quantify cases, hospitalizations, and healthcare impact. These data may be under-reported due to screening policy changes. To improve our model's reliability, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). This paper shows how we use sewage data in the Digital Twin validation process to identify discrepancies between model predictions and real-time data. This continuous validation approach enables us to generate long-term forecasts, gain insights into SARS-CoV-2 spread, reassess assumptions, and enhance our understanding of the pandemic's behavior in Catalonia. [ABSTRACT FROM AUTHOR]

To ensure Energy Efficiency (EE) and better Quality of service (QoS), it is necessary to analyze the energy saving possibilities for low resource utilization in the current networks caused by rigorous QoS requirements and implementing EE approach in the planned model for performance improvement. Distributed Denial of Service (DDoS) attacks aim to exhaust the network's processing and communication capacity by saturating it with packets and generating malicious traffic. There are numerous advantages that make Digital Twin (DT) and Intrusion Detection technique (ID) an effective remedy for a range of (fifth generation) 5G problems. A DDoS attack must be immediately detected and stopped before a legitimate user can access the target of the attacker for the 5G network to provide an efficient energy service. Although they clearly show promise in assisting with the creation and implementation of the challenging 5G environment, Digital Twins is still a relatively new technology for 5G networks but will increase EE. In this research, a thorough examination of the materials was carried out to identify the most cutting-edge DT and ID methods. The purpose of this study was to comprehend the problems with Energy Efficiency, the need for DT, and the methods for dealing with large-scale attack by DDoS on Energy Efficient networks. Only 94 of the 1555 articles produced by the procedure were determined to be relevant using inclusion and exclusion criteria. The outcome demonstrates that in 5G networks, DT, and its fundamental approaches, like QoS and DDoS attack mitigation, can be used to regulate the network's Energy Efficiency. Numerous practical applications focusing on 5G Systems use their own principles. The effectiveness of these strategies was evaluated using several assessment criteria, including DT, Intrusion Detection, QoS, Energy Efficiency, and 5G Systems. Each study issue is thoroughly explained, along with typical methods, advantages, disadvantages, and performance metrics. Energy economy, network reliability, privacy, and cost reduction are all considerably increased by the implementation of intrusion detection technology in 5G systems. The decision is supported by the technology's demonstrated efficacy, scalability, real-time detection capacities, low error level, and personalized learning attributes, all of which contribute to the long-term viability of 5G networks as an entire system. [ABSTRACT FROM AUTHOR]

Advancing digitalization is reaching the realm of lightweight construction and structural–mechanical components. Through the synergistic combination of distributed sensors and intelligent evaluation algorithms, traditional structures evolve into smart sensing systems. In this context, Structural Health Monitoring (SHM) plays a key role in managing potential risks to human safety and environmental integrity due to structural failures by providing analysis, localization, and records of the structure's loading and damaging conditions. The establishment of networks between sensors and data-processing units via Internet of Things (IoT) technologies is an elementary prerequisite for the integration of SHM into smart sensing systems. However, this integrating of SHM faces significant restrictions due to scalability challenges of smart sensing systems and IoT-specific issues, including communication security and interoperability. To address the issue, this paper presents a comprehensive methodological framework aimed at facilitating the scalable integration of objects ranging from components via systems to clusters into SHM systems. Furthermore, we detail a prototypical implementation of the conceptually developed framework, demonstrating a structural component and its corresponding Digital Twin. Here, real-time capable deformation and strain-based monitoring of the structure are achieved, showcasing the practical applicability of the proposed framework. [ABSTRACT FROM AUTHOR]

It is essential to establish a digital twin scene, which helps to depict the dynamically changing geographical environment accurately. Digital twins could improve the refined management level of intelligent tunnel construction; however, research on geographical twin models primarily focuses on modeling and visual description, which has low analysis efficiency. This paper proposes a knowledge-guided intelligent analysis method for the geometric deformation of tunnel excavation profile twins. Firstly, a dynamic data-driven knowledge graph of tunnel excavation twin scenes was constructed to describe tunnel excavation profile twin scenes accurately. Secondly, an intelligent diagnosis algorithm for geometric deformation of tunnel excavation contour twins was designed by knowledge guidance. Thirdly, multiple visual variables were jointly used to support scene fusion visualization of tunnel excavation profile twin scenes. Finally, a case was selected to implement the experimental analysis. The experimental results demonstrate that the method in this article can achieve an accurate description of objects and their relationships in tunnel excavation twin scenes, which supports rapid geometric deformation analysis of the tunnel excavation profile twin. The speed of geometric deformation diagnosis is increased by more than 90% and the cognitive efficiency is improved by 70%. The complexity and difficulty of the deformation analysis operation are reduced, and the diagnostic analysis ability and standardization of the geographic digital twin model are effectively improved. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) product model has become a tool that has transitioned from a legacy instrument, used in design, to an emerging technology applied to production and assembly processes. As this evolution has occurred, the need has developed to understand the value of deploying the 3D product model beyond the design phase. This research answers the question and solves the problem, does electronic documentation inclusive of the 3D product model add to the production workers' ability to complete the production task? To answer this question, the methods used were that the research team tested how accurately and quickly a production and assembly team could build the product using interactive, electronic documentation, including the 3D product model, as a means to understand the design intent as opposed to printed bills of materials (BOMs) and two-dimensional (2D) paper drawings. The conclusions that can be drawn from this research are that the research found statistically significant improvements in the production throughput time (~10%), reductions in the direct labor hours per unit (~14%), and retained quality levels, when deploying electronic documentation, including the 3D product model, into the production and assembly processes. Through the deployment of the interactive 3D product model electronic documentation to the production floor, the organization also took a step towards creating a digital twin of the produced product and laid a foundation for the further adoption of Industry 4.0 practices. The novelty of the work and the areas where it goes beyond previous efforts in the literature concerns the current body of knowledge that does not demonstrate a repeatable methodology through which industry and other researchers can replicate the experiment on demonstrating economic value when deploying the 3D product model to production and assembly processes. In this paper, the authors aim to build on prior work to demonstrate a repeatable methodology for determining the economic value of 3D product model deployment in production and assembly processes through applied research. [ABSTRACT FROM AUTHOR]

The rise of mechanical automation in orchards has sparked research interest in developing robots capable of autonomous tree pruning operations. To achieve accurate pruning outcomes, these robots require robust perception systems that can reconstruct three-dimensional tree characteristics and execute appropriate pruning strategies. Three-dimensional modeling plays a crucial role in enabling accurate pruning outcomes. This paper introduces a specialized tree modeling approach using the space colonization algorithm (SCA) tailored for pruning. The proposed method extends SCA to operate in three-dimensional space, generating comprehensive cherry tree models. The resulting models are exported as normalized point cloud data, serving as the input dataset. Multiple criteria decision analysis is utilized to guide pruning decisions, incorporating various factors such as tree species, tree life cycle stages, and pruning strategies during real-world implementation. The pruning task is transformed into a point cloud neural network segmentation task, identifying the trunks and branches to be pruned. This approach reduces the data acquisition time and labor costs during development. Meanwhile, pruning training in a virtual environment is an application of digital twin technology, which makes it possible to combine the meta-universe with the automated pruning of fruit trees. Experimental results demonstrate superior performance compared to other pruning systems. The overall accuracy is 85%, with mean accuracy and mean Intersection over Union (IoU) values of 0.83 and 0.75. Trunks and branches are successfully segmented with class accuracies of 0.89 and 0.81, respectively, and Intersection over Union (IoU) metrics of 0.79 and 0.72. Compared to using the open-source synthetic tree dataset, this dataset yields 80% of the overall accuracy under the same conditions, which is an improvement of 6%. [ABSTRACT FROM AUTHOR]

Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change's impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R 2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R 2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed. [ABSTRACT FROM AUTHOR]

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid–liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited, because fixed recipes are usually applied to run the processes. In order to take the varying properties of biological feed materials into account, we aim to develop a digital twin of an industrial brownfield DCF plant, allowing to optimize setpoint decisions in almost real time. The core of the digital twin is a mechanistic–empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation in the selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach, and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. We considered a total of 24 industrial production batches from the filtration of grape juice from the years 2022 and 2023 in the study. After implementation of the digital twin on site, the campaign mean productivity increased by 15% over the course of the year 2023. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes. [ABSTRACT FROM AUTHOR]

On the basis of the characteristics of low-density polyethylene determined experimentally from the geometric dimensions of the extruder parts and COMSOL MULTIPHYSICS software a digital model of the apparatus was created, and the flow rate of the polymer melt from the extruder nozzle was calculated hydrodynamically. The agreement between the experimentally measured polymer flow rate and the value obtained from the model lies within the permissible error. This establishes the adequacy of the digital twin model, which will be used to predict the viscosity characteristics of composite materials based on low-density polyethylene and the technological regimes of their production by blending in the extruder. [ABSTRACT FROM AUTHOR]

Data assimilation plays a dual role in advancing the "scientific" understanding and serving as an "engineering tool" for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies. Plain Language Summary: Land Data Assimilation (LDA) integrates numerical models with observation data to enhance predictions of key variables related to land surface processes, including soil moisture, snow, evapotranspiration, and groundwater. Consequently, LDA effectively tackles two of the fundamental scientific challenges in the Earth system: how to enhance the usability of exponentially increasing land observations and how to improve the predictive capability and accuracy of land models. Significant advancements have been made in the theory, methods, and applications of LDA since the 21st century. Breakthroughs have been attained in characterizing uncertainties, estimating multiple variables and parameters simultaneously, and incorporating big data analytics. LDA systems at global, regional, and catchment‐scales, along with widely used software platforms, have been developed. However, future challenges facing LDA comprise improving meteorological forcing data, creating long‐term land reanalysis, devising operational applications, and broadening its range to cover the critical zone. In the era of big data, LDA will evolve further by assimilating big Earth data and incorporating machine learning to develop digital twins from pure geophysical systems to coupled natural and human systems. Key Points: Land data assimilation (LDA) advances scientific understanding and serves as an engineering tool for Earth system sciencesLDA reflects the trend of harmonizing theory and data in the era of big data and artificial intelligenceFuture LDA research should expand the applications from pure geophysical systems to coupled natural and human systems [ABSTRACT FROM AUTHOR]

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications. Plain Language Summary: Lake thermal dynamics are fundamental in controlling mixing processes and have significant implications for biological and geochemical processes. Consequently, the impacts of climate change on these dynamics can have severe consequences for the health of lakes and their aquatic ecosystems. In this context, mathematical models are essential for understanding the potential effects of future climate change on lake thermal dynamics and related processes. This manuscript offers a comprehensive review of lake water temperature modeling. It covers the fundamental physical concepts that govern thermal dynamics in lakes and provides an overview of various sources of observational water temperature data, including in situ monitoring and satellite data used in these models. The study evaluates different types of lake water temperature models, including statistical, process‐based, and hybrid models. It explores emerging modeling approaches such as forecasting, digital twins, combining process‐based modeling with deep learning, ensemble modeling, and climate‐lake models coupling. Model performance is also discussed, highlighting suggested evaluation metrics and providing a comprehensive analysis of the state‐of‐the‐art optimization methods to assess model accuracy. This review targets researchers in limnology, hydrology, ecology, biology, physics, engineering, and remote sensing from the private and public sectors interested in lake water temperature modeling and its applications. Key Points: Lake thermal dynamics are central in shaping mixing processes and the health of aquatic ecosystems, and climate change alters these dynamicsMathematical models are essential to understand past and project future climate change impacts on lake thermal dynamicsThis study reviews lake water temperature modeling, covering concepts, data sources, and model evaluation for applications across disciplines [ABSTRACT FROM AUTHOR]

Digital transformation for the water sector has gained momentum in recent years, and many water resource recovery facilities modelers have already started transitioning from developing traditional models to digital twin (DT) applications. DTs simulate the operation of treatment plants in near real time and provide a powerful tool to the operators and process engineers for real‐time scenario analysis and calamity mitigation, online process optimization, predictive maintenance, model‐based control, and so forth. So far, only a few mature examples of full‐scale DT implementations can be found in the literature, which only address some of the key requirements of a DT. This paper presents the development of a full‐scale operational DT for the Eindhoven water resource recovery facility in The Netherlands, which includes a fully automated data‐pipeline combined with a detailed mechanistic full‐plant process model and a user interface co‐created with the plant's operators. The automated data preprocessing pipeline provides continuous access to validated data, an influent generator provides dynamic predictions of influent composition data and allows forecasting 48 h into the future, and an advanced compartmental model of the aeration and anoxic bioreactors ensures high predictive power. The DT runs near real‐time simulations every 2 h. Visualization and interaction with the DT is facilitated by the cloud‐based TwinPlant technology, which was developed in close interaction with the plant's operators. A set of predefined handles are made available, allowing users to simulate hypothetical scenarios such as process and equipment failures and changes in controller settings. The combination of the advanced data pipeline and process model development used in the Eindhoven DT and the active involvement of the operators/process engineers/managers in the development process makes the twin a valuable asset for decision making with long‐term reliability. Practitioner Points: A full‐scale digital twin (DT) has been developed for the Eindhoven WRRF.The Eindhoven DT includes an automated continuous data preprocessing and reconciliation pipeline.A full‐plant mechanistic compartmental process model of the plant has been developed based on hydrodynamic studies.The interactive user interface of the Eindhoven DT allows operators to perform what‐if scenarios on various operational settings and process inputs.Plant operators were actively involved in the DT development process to make a reliable and relevant tool with the expected added value. [ABSTRACT FROM AUTHOR]

• Cultural heritage is a multi-disciplinary research domain; however data interoperability is really achieved. • Cultural heritage research relies on the integration of data deriving from social sciences and humanities and natural and exact sciences. • A semantic-aware digital twin is offering research opportunities towards the development of large-scale knowledge-bases in cultural heritage, essential for the implementation of the European Commission vision for a European Collaborative Cloud for Cultural Heritage (ECCCH). • Human and machines readable systems, based on a dedicated ontology provide the technological frame for the implementation of the ECCCH.2. The methodologies of Cultural Heritage research and in particular those related to conservation and the analytical study of heritage assets and works of art, recently grouped under the term Heritage Sciences, engage a broad range of disciplines, each with its own characteristic workflows for generating data. Consequently, an emerging challenge is the need to define a digital framework for multi-source data integration, associated with a single heritage asset, but generated by various tools and methods which are often pursued by different research groups and at different times. This digital framework is discussed in this essay as the digital twin of a heritage asset, comprising of the documentation data associated with a heritage asset and its virtual representation. To best describe and define a Heritage Digital Twin ontology and its associated knowledge graph, we use a specific example drawing from art historical and analytical investigation of a 13th century Italian painting masterpiece, the Crucifixion of Christ, by Giovanni Baronzio, one of the leaders of the so-called Rimini School of painting which was greatly influenced by the work of the famous Renaissance master Giotto di Bondone. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) are transforming business operations across industries through accurate replication of physical entities using the Internet of Things and big data analytics. Despite booming progress in the manufacturing, aerospace and buildings sectors, the adoption of DTs in the minerals industry has been slow, and integration with efficient visualisation and user interactions has not been fully optimised to achieve maximum fidelity and usability. One promising avenue for enhancing DT capabilities is the utilisation of extended reality (XR) technologies, which also hold great potential for realising an industrial metaverse where real-world business activities can be conducted in a virtual space. This article proposes a cost-effective and scalable approach to developing a DT with real-time monitoring and control capabilities for a ball mill operation, a widely used processing equipment in the minerals industry. The case study showcases two approaches with different levels of system integration by leveraging serious game development platforms, toolkits and workflows. [ABSTRACT FROM AUTHOR]

Digital twins efficiency lies in fast and representative solutions of inverse problems to accomodate models with physical observations. The quality of the solution of an inverse problem is conditioned by inherent features of the latter, in particular (i) the richness of available data, (ii) the a priori experimental and modeling knowledge that allows to regularize the ill-posedness nature of the problem, and (iii) the complexity of the space in which updated parameters are sought. We present in this contribution a fully automated physics-guided model updating framework dedicated to the correction of finite element models from low-frequency dynamics measurements. The proposed methodology is based on the minimization of a modified Constitutive Relation Error (mCRE) functional, whose basic idea is to construct mechanical fields and identify material parameters that are a trade-off between all available information (and associated confidence) but without any further assumption. The dependency into some expert-user's judgment is thus avoided. Dedicated rules are provided to automatically calibrate all mCRE internal tuning parameters as well as a strategy to optimize the space in which parameters are sought, leading to a fully autonomous algorithm. The performance and robustness of the proposed model updating methodology are illustrated using synthetic ground motion tests on a bending plate in which defects of various shapes are identified from noisy acceleration datasets, with inherent limitations due to richness of input loading, sensors sparsity and defect identifiability. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as "Smart Parking", "Environmental Behavior Analysis on Traffic Incidents", and "Emergency Management", we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity. [ABSTRACT FROM AUTHOR]

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant's systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making. [ABSTRACT FROM AUTHOR]

Digital twin technology is a highly valued asset in the manufacturing sector, with its unique capability to bridge the gap between the physical and virtual parts. The impact of the rapid increase in this technology is based on the collection of real-world data, its standardization, and its widespread deployment on an existing manufacturing system. This encompasses sensor values, PLC internal states, and IoT, as well as how the means of linking these data with their digital counterparts. It is challenging to implement digital twins on a large scale due to the heterogeneity of protocols and data structuring of subsystems. To facilitate the integration of the digital twin into existing manufacturing architectures, we propose in this paper a framework that enables the deployment of scalable digital twins from sensors to services of digital twins in an iterative manner. [ABSTRACT FROM AUTHOR]

The concept of digital twins is one of the fundamental pillars of Industry 4.0. Digital twin allows the realization of a virtual model of a real system, enhancing the relevant performance (e.g., in terms of production rate, risk prevention, energy saving, and maintenance operation). Current literature presents many contributions pointing out the advantages that may be achieved by the definition of a digital twin of a water supply system. The Reference Architecture Model for Industry 4.0 introduces the concept of the Asset Administration Shell for the digital representation of components within the Industry 4.0 ecosystem. Several proposals are currently available in the literature considering the Asset Administration Shell for the realization of a digital twin of real systems. To the best of the authors' knowledge, at the moment, the adoption of Asset Administration Shell for the digital representation of a water supply system is not present in the current literature. For this reason, the aim of this paper is to present a methodological approach for developing a digital twin of a water supply system using the Asset Administration Shell metamodel. The paper will describe the approach proposed by the author and the relevant model based on Asset Administration Shell, pointing out that its implementation is freely available on the GitHub platform. [ABSTRACT FROM AUTHOR]

Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the Digital Twin (DT) concept and the interest of its application in photovoltaic installations. It compares how other authors use the DT approach in photovoltaic installations to improve the efficiency of the renewable energy generated and consumed, energy prediction and the reduction of the operation and maintenance costs of the photovoltaic installation. It reviews how, by providing real-time data and analysis, DTs enable more informed decision-making in the solar energy sector. The objectives of the review are to study digital twin technology and to analyse its application and implementation in PV systems. [ABSTRACT FROM AUTHOR]

This study is a numerical study to predict the temperature of the heat exchange tubes inside the pulverized coal-fired boiler through the conjugate heat transfer analysis. Due to the aspect ratio and number of tubes inside the pulverized coal-fired boiler, actual tube modeling analysis has rarely been conducted. Most of the research has been conducted through the porous media method, resulting in limited information on the temperature distribution of each tube. However, for the development of a digital twin model for improving the performance of the boiler and reducing maintenance costs, information on the local temperature of the tubes is required. In this study, all the tubes inside the boiler were modeled, and conjugate heat transfer analysis was performed to confirm the local temperature distribution. For this purpose, the analysis was conducted using Fluent 2020 r2, and the analysis model was constructed using more than 300 million structured grids. The calculation was performed considering conjugate heat transfer in the pulverized coal-fired boiler, heat exchange by steam inside the tubes, and conductive heat transfer of the tubes. As a result, it was confirmed that there is a significant deviation in the local temperature for each tube position. Furthermore, the maximum temperature of the PrSH tube ranges widely, between 492 and 532 degrees, depending on the tube's position. It was observed that the point of the highest temperature inside the tubes also varies for each tube due to the flow of external combustion gas. Based on these results, it is expected that strategic approaches to boiler design and maintenance can be achieved. Furthermore, it is anticipated to contribute to the high efficiency of power facilities by being utilized as basic data for the development of a digital twin model for the boiler. [ABSTRACT FROM AUTHOR]

Human-robot collaboration, which strives to combine the best skills of humans and robots, has shown board application prospects in meeting safe-effective-flexible requirements in various fields. The ideation of much closer interaction between humans and robots has greatly developed the exploration of digital twin to enhance collaboration. By offering high-fidelity models and real-time physical-virtual interaction, the digital twin enables to achieve an accurate reflection of the physical scenario, including not only human-robot conditions but also environmental changes. However, the appearance of unpredictable events may cause an inconsistency between the established schedule and actual execution. To cope with this issue, an environment-adaptive assignment method based on digital twin for human-robot collaboration is formed in this study. The proposed approach consists of a factor-event-act mechanism that analyzes the dynamic events and their impacts from both internal and external perspectives of digital twin and a genetic algorithm-based assignment algorithm to respond to them. Experiments are carried out in the last part, aiming to show the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Concept of a digital twin for bridges in the operational phase The concept of the digital twin is also finding its way into bridge construction. In future, the aggregated data collected during the operating phase from inspection, structural health monitoring and diagnostics will form the basis for transforming the current reactive maintenance into predictive maintenance. This article presents a concept for an object twin with the degree of connectivity of a digital shadow at maturity level 2. It lists criteria for a precise definition of the term digital twin, defines BIM use cases for the operational phase of bridges and derives the necessary models. With regard to the IDA‐KI research project, requirements for the digital twin are established and a software concept is developed to fulfill them. Particular focus is put on the provision of the IFC models in a CDE as well as on the refinement process of monitoring data. [ABSTRACT FROM AUTHOR]

Digital twins for medium span bridges – pilot project bridge Schwindegg – part 2: Asset Administration Shell The term ′digital twin′ is becoming increasingly popular in German‐speaking countries for the digital management and condition monitoring of bridges. Although standardised developments for the use of digital twins have already been initiated in industry and medicine, the first prototypes are currently being implemented in the construction industry. The digital images are typically operated in online environments that are proprietary to the project and have limited reusability. The article investigates the integration of scaling into Building Information Modelling models, which is reaching its limits due to the real‐time capability. It places existing definitions of the digital twin of the industry in the context of the construction industry. The implementation of the Isenbridge in Schwindegg as an Industry 4.0 Asset Administration Shell (AAS) is presented, building on Part 1 of this contribution. We show that an industrial, data‐driven digital twin can also be implemented technically and semantically for engineering structures. The article also presents the future challenges for evaluation and control mechanisms still to be developed for the structure. We also show a possible visualisation. The goal is to develop a syntax for the AAS' communication. [ABSTRACT FROM AUTHOR]

openLAB – A research bridge for developing a digital twin of bridges Within the IDA‐KI project, a research bridge (openLAB) is being built and loaded up to the level of severe damage. This will be accompanied by extensive measurements and investigations, thus creating a unique database for the validation of damage detection and evaluation methods under real environmental conditions using a large‐scale model. This article presents the project idea, the research bridge currently under construction, and the monitoring system installed at the bridge. The methods implemented for automated data analysis and fault detection in the monitoring data are presented and validated through load tests in the openLAB. First measurements recorded during the production process of the precast girders of the research bridge underline the potential of distributed fiber optic sensors for a monitoring from day one. The information gained from the construction and operation of the bridge is provided in a digital model. The conceptualization of the so‐called "as maintained" model and the data management will be presented. [ABSTRACT FROM AUTHOR]

The current research addresses a unified and integrated Euler–Lagrange simulation platform based on the extended discrete element method that advances significantly the digital twin technology applied to blast furnaces. It includes a particulate phase characterized by its dynamic and thermodynamic state that is coupled to multiphase computational fluid dynamics (CFD) by heat, mass, and momentum transfer. Thus, the framework deals with transient and reacting 3D granular and multiphase flows. The latter includes all relevant processes taking place in a blast furnace: formation of a reducing agent, e.g., carbon monoxide, reduction of iron‐bearing material, and softening and melting with the subsequent transfer into the corresponding phase of the multiphase CFD solver and thus, identifying unambiguously the cohesive zone. Validation is carried out on different length scales from a particle level to the global dimensions of a blast furnace and shows a good agreement between predictions and experimental data. Applying fast and innovative algorithms allows for moderate computational times, i.e., 1000 s real‐time takes ≈2.3 d on an Intel(R) Xenon(R) Silver 4114 CPU @ 2.2 GHz processor with 40 cores and the entire domain divides into eight partitions. [ABSTRACT FROM AUTHOR]

This paper proposes a digital twin enhanced approach for optimizing collaborative production scheduling in multi-enterprise manufacturing systems. A multi-objective model is developed incorporating practical constraints such as limited time windows, different production capacities, and transportation considerations. To solve the model, an Improved Non-dominated Sorting Genetic Algorithm (INSGA-II) is designed with specialized operators and strategies. The digital twin simulation is enriched with Multi-objective Decision Making based on Interaction Structures (MDIS) to obtain higher-quality solutions. Experiments demonstrate that the MDIS digital twin approach reduces manufacturing lead times and costs while improving utilization and quality compared to standard methods. This research provides an effective optimization framework to leverage cloud manufacturing resources across organizations. [ABSTRACT FROM AUTHOR]

The digital twin concept proves to be highly advantageous for smart ships as it allows the digital model to adapt in real time with changes in the physical ship. The mean value engine model (MVEM) is investigated with three different compressor mass flow rate models: a 2D interpolation model (2D), an experience-based M-JENSEN (MJ) model, and a physics-based Kang Song (KS) model. Each model is studied on a MAN 7S80ME engine with ABB A270 turbochargers. The root-mean-square errors (RMSE) for the test data are approximately 2.5 m 3 / s for the 2D model, 2.8 m 3 / s for the KS model, and 0.56 m 3 / s for the MJ model. For the training data, the RMSEs are around 0.0, 3.0, and 0.69 m 3 / s , respectively. When coupling each model to the MVEM, it is observed that the calculated values differ only in the turbocharger speed. The RMREs under steady-state processes are 0.54% for MVEM with the 2D model, 1.63% for MVEM with the KS model, and 0.48% for MVEM with the MJ model. Under dynamic processes where the engine load is changed from 100% to 80%, the RMREs are 1.59%, 2.40%, and 1.58%, respectively. Among the studied models, the 2D model exhibits instability, the MJ model shows the highest accuracy, while the KS model performs the worst. However, all models demonstrate comparable results when used in the MVEM. Moreover, the physics-based KS model is a favorable choice for digital twin applications due to its excellent extrapolation ability and low dependence on measured data. [ABSTRACT FROM AUTHOR]

The global drive toward renewable energy is imposing challenging operating requirements on power turbines. Flexible load-leveling applications must accept more frequent and demanding start-stop cycles. Full transient analyses are too computationally expensive for real-time simulation across all operating regimes so monitoring relies on sparse physical measurements. Alone, these sparse data lack the fidelity for real-time prediction of a complex thermal field. A novel hybrid methodology is proposed, coupling data across a range of fidelities to bridge the limitations in the individual analyses. Combining several fidelity methods in parallel; low-order models, corrected by real-time physical measurements, are calibrated with high-fidelity simulations. A newly developed low-order thermal network code is used to predict the thermal field in real-time. High-fidelity flow characteristics are routinely transferred to the decoupled low-order solution. A critical enabling feature of this hybrid approach is the fast data interpolation between differing fidelity numerical simulations. This paper evaluates a spatial Kriging method for robust data transfer between two different fidelity mesh, tested in the case of thermal profile prediction of a power turbine. Additionally, a novel coordinate-based hash mapping process is demonstrated for the fast high-to-low fidelity data transfer. Localized hashing allows independent, parallel, nearest neighbor search at significantly reduced computational cost. The demonstrated method facilitates fast mesh pairing, necessary to support the real-time hybrid method for thermal field prediction during turbine transient operation. [ABSTRACT FROM AUTHOR]

The global drive toward renewable energy is imposing challenging operating requirements on power turbines. Flexible load-leveling applications must accept more frequent and demanding start-stop cycles. Full transient analyses are too computationally expensive for real-time simulation across all operating regimes so monitoring relies on sparse physical measurements. Alone, these sparse data lack the fidelity for real-time prediction of a complex thermal field. A new hybrid methodology is proposed, coupling data across a range of fidelities to bridge the limitations in the individual analyses. Combining several fidelity methods in parallel, low-order models, corrected by real-time physical measurements, is calibrated with high-fidelity simulations. The multifaceted hybrid approach enables the real-time speed of low-order analysis at high resolution. This paper series develops the critical enabling features of the hybrid method. Real-time cross-fidelity data transition is fundamental to the hybrid methodology. A novel neural network auto-encoder method is presented, facilitating complex thermal profile reconstruction. Uncovering a compressed latent space, auto-encoders leverage underlying data features for fast simulation. Coupled with a dynamic mask and top-k selection, thermal probe placement can be automatically optimized. The auto-encoder method is demonstrated on a turbine casing, reconstructing over 500 h of transient operation in real-time, whilst reducing the required number of measurements by half. [ABSTRACT FROM AUTHOR]

It is becoming increasingly important to provide a solution for managing the present mission and status of autonomous and semi-autonomous vehicles operating on site in large and complex port locations. More ports are implementing digital twin systems, which provide a complete 3D reconstruction of the port as well as real-time data on all objects and actions in progress. It is challenging to provide all relevant data in a way that improves situational awareness and decision-making, resulting in improved management and a faster, more effective response during an emergency. This is due to greater port areas and drones operating in the air, on the water's surface, and underneath. To solve this issue, the PASSport initiative, a project financed by the European Space Agency (EUSPA), is developing an innovative solution based on Mixed Reality (MR) technology. The solution combines real-time geo-tagged and Earth Observation data to provide end users with enhanced 3D representation of the port area via a dedicated Head Mounted Display (HMD) that records user location and movement. [ABSTRACT FROM AUTHOR]

Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground. [ABSTRACT FROM AUTHOR]

Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process. [ABSTRACT FROM AUTHOR]

Uncertainties, such as road restrictions at shipyards and the irregular shape of blocks, pose challenges for transporter scheduling. Efficient scheduling of multiple transporters is critical to improving transportation efficiency. The digital twin (DT) technology offers numerous benefits, enabling interactions between the virtual and real worlds, real-time mapping, and dynamic performance evaluation. Based on DT technology, this study proposes a dynamic scheduling approach for cooperative transportation utilizing multiple transporters. The scheduling problem for multiple transporters is addressed and modeled in this study, considering factors such as block size and transporter loading. To solve this problem, a framework of DT-based multiple transporters system is established in a virtual environment. By inputting block information into this system, a solution is generated using transporter scheduling rules and interference detection methods. Experimental comparisons are conducted in this paper, exploring various scenarios with different number of tasks and the application of DT. The results demonstrate that the proposed approach effectively enhances transportation efficiency and improves ship construction efficiency. Hence, this study expands the application of DT technology in dynamic scheduling of transportation in shipyards and provides new ideas for shipbuilding company managers. [ABSTRACT FROM AUTHOR]

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs. [ABSTRACT FROM AUTHOR]

Pressure swing adsorption(PSA) based gas separation technology is widely applied in chemical industries, especially in hydrogen purification. Its complex mechanism and non-steady state feature restrict the separation performance adjustment and application on efficient gas utilization. Process simulation, machine learning and modelling combined innovating gas management methodology is proposed in this paper to perform accurate gas separation for natural gas based syngas mixture. VB script program is employed to drive Aspen adsorption simulation, and then classical and regression tree(CART), polynomial regression(PNR) and surrogate model are comprehensively used to perform machine learning. Consequently, the coefficient matrix is obtained to quantify the separation performance. This method holds the potential for broader application in refineries and SPCs hybrid hydrogen network, facilitating the comprehensive utilization of hydrogen containing gases. Case study results show this methodology can conserve natural gas and gas productivity by 21.3 %, as well as carbon emission by 8211.6 Nm3/h. This work highlights the promising application of digital twin technology within the chemical industry, showcasing how process simulation, machine learning, and modeling can collectively revolutionize gas management and resource conservation. [Display omitted] • Novel PSA based gas allocation instead of gas separation technology. • Simulation, modelling and machine learning combined gas accommodating methodology. • An insight technology for integration of refinery and SPC. • A novel gas allocation method for zero waste of hydrogen. • An insight application of digital twin technology for gas efficient utilization. [ABSTRACT FROM AUTHOR]

This study analyzes a hydrogen-methane thermal power plant installed in a high school. In this plant, photovoltaic energy is used to produce hydrogen through two alkaline electrolyzers. The hydrogen is stored under pressure and utilized as fuel, mixed with methane in a commercial boiler for heating purposes. A digital twin of the hybrid system was developed, and simulations were conducted using Matlab/Simulink software. The study compares two possible system configurations: in the first one, the energy produced by the photovoltaic array is sufficient to meet the required amount of hydrogen throughout the simulation year; in the second one, the electrolyzers come into operation only when green electricity is available. For each configuration, several simulations were performed, considering variable sizes of the photovoltaic array, hydrogen storage capacity, and hydrogen-to-methane ratio to determine the optimal plant configuration from an economic perspective. Results indicate that, with current operating expense and capital expenditure costs, the system can achieve a payback time of 16 years with 100 % hydrogen combustion, provided the photovoltaic array and hydrogen storage subsystems are sized appropriately. In terms of environmental benefits, the optimized system configuration enables an annual CO 2 savings of 155 tons. [Display omitted] • A green H 2 - NG mixture for a 35 kW th boiler was analyzed. • H 2 is produced with a 15 kW el electrolyzer supplied by a mix of PV and grid energy. • Matlab Simulink and Autodesk Revit softwares were used for system simulation. • Sub-systems size optimization was performed to increase system economic feasibility. • Economical and environmental analysis on the general system were done. [ABSTRACT FROM AUTHOR]

A clean solar hydrail, a locomotive operated by solar and hydrogen energy, has received significant attention as an alternative to a current fossil fuel-based locomotive. Nevertheless, the feasibility test of solar hydrail is still challenging since it has not yet been practically developed. Herein, we employed digital twin technology to design and operate solar hydrail and diesel locomotive. 30 years of operation simulation starting from 2021 was conducted based on the data of Gangwon-do, South Korea. It was confirmed that replacing the single diesel locomotive with solar hydrail can provide about 59 % lower net present cost and annual harmful emission reduction of approximately 300 tons. Sensitivity analyses were also conducted to mitigate future uncertainty, and the solar hydrail was a better option than the diesel locomotive in every scenario. This study demonstrates that solar hydrail is the successful substitute for the diesel locomotive currently operated in South Korea. • 30 years of operation was simulated for solar hydrail and diesel locomotive. • Solar hydrail can have $2,026,279 lower NPC than diesel locomotive. • Solar hydrail yearly saves about 300 tons of emissions compared to diesel locomotive. [ABSTRACT FROM AUTHOR]

Reprogrammable metasurface (RM) has been demonstrated as a key enabling technology for resolving the difficulties arising in conventional electromagnetic (EM) sensing strategies, and the RM‐enabled intelligent EM sensing has emerged as a new paradigm with many advanced functionalities that are thought to be impossible. However, the RM‐enabled sensing strategies developed so far are usually limited to the single‐scale sensing task and the cooperative target, since they are designed to operate at a single frequency band and have the relatively small view angle. To address the formidable challenges, here the concept of multi‐task multi‐scale intelligent sensing is proposed with algorithm‐driven distributed multi‐frequency RMs. For principle illustration, a proof‐of‐principle system working at S‐, C‐, and X‐bands is designed, and it is experimentally demonstrated that the system is capable of monitoring continuously the action behavior and healthy status of the target acting freely in real‐world indoor settings. It is faithfully expected that the presented strategy could open a new avenue in the areas of internet‐of‐things (IoTs), digital twins, and so on. [ABSTRACT FROM AUTHOR]

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure. [ABSTRACT FROM AUTHOR]

Among various applications of digital twins, measuring particulate matter in the context of air pollution has become increasingly important due to growing interest in atmospheric environments. By obtaining particle count values from analogue‐to‐digital converter raw data through photodiode and calibrating them, particulate matter values can be acquired. The singular‐value–decomposition is efficient transfer function learning method. However, due to the linear nature of the transfer function of singular‐value–decomposition, it also conveys noise information, necessitating post‐processing to suppress noise. This study proposes a method that utilizes long short‐term memory neural networks to effectively stabilize noise based on the output of continuous dust sensor particulate matter distributions. Using the singular value decomposition based particulate matter shows relative root‐mean‐square error (4.4761, 100%) as a reference, the proposed long short‐term memory post‐processing demonstrates an improved result (2.9328, 65.52%) compared to other post‐processing method: the mean filter (3.6704, 82.00%), low‐pass filter (3.7719, 84.27%) and Kalman filter (3.5550, 79.42%). Furthermore, to address the limitation of initial delay in long short‐term memory to achieve stable data output, a method of iteratively training the initial input data sample is proposed. For the initial input data, the non‐initialization approach exhibited a relative error of 87.57% and it took around 5 samples to predict stable output. In contrast, the proposed iterative training method, applied five times to the initial data sample, achieved a relative error of 9.51% and immediately obtained stable data. [ABSTRACT FROM AUTHOR]

The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly based on geometric transformation and rendering optimization, focusing more on the data compression and visual quality of the model, which essentially does not change the data structure of the BIM model, and it is difficult to establish the mapping relationship between spatial location and spatial data, information, and resources. In addition, current coding methods proposed for BIM models are mostly based on the line classification method, which realizes the identification of components based on the classification of their attributes, and the location information is stored according to the attributes or natural language descriptions, which need to be parsed and translated when they are used, and this procedure ignores the importance of spatial location in daily management and emergency management. The importance of spatial location in daily management and emergency management is also ignored. Based on this kind of identification code, it is impossible to directly analyze and apply spatial location data. Therefore, this paper takes the combination of GIS technology and BIM technology as the starting point and proposes a BIM data modeling method based on the BeiDou grid code, based on the efficiency of its underlying data organization and the accuracy of its real geographic location expression on the one hand and the completeness of the information expression by BIM and fine three-dimensional visualization on the other hand. Finally, a series of experiments are carried out based on the method. Through visualization modeling and efficiency experiments, different feature models are meshed to verify the feasibility and efficiency of the model. Through coding and information query experiments, the model′s data organization capability, data dynamic carrying capability, and efficient spatial computation capability and practical application capability are verified. [ABSTRACT FROM AUTHOR]

Hydrogen is a promising energy carrier in all fields of transportation, including unmanned aerial vehicles (UAVs) and manned vehicles for urban air mobility (UAM). In these applications, one of the biggest challenges is to overcome the limitations of lithium battery technologies, while keeping the advantage of clean energy, at least in terms of direct emissions. For these reasons, there is an ever-increasing interest in the development, simulation, and testing of propulsion systems adopting air-cooled proton exchange membrane fuel cells (PEMFCs). Fuel cells for aerospace must be designed for power-to-weight maximization. For this reason, auxiliary systems are simplified, and the adoption of air-cooling and passive cooling techniques is favored. However, the performance and dynamic behavior of PEMFCs are affected by the operating conditions, which, in applications like UAVs and UAM, are continuously changing due to the variation of speed and altitude during the flight. This investigation analyzes semi-empirical and control-oriented models of fuel cell systems proposed in the scientific literature. The review addresses the whole fuel cell system, inclusive of the balance of the plant, and introduces the transition from dynamic models to digital twins. [ABSTRACT FROM AUTHOR]