Search

Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO2 per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current research in nephrology is increasingly focused on elucidating the complexity inherent in tightly interwoven molecular systems and their correlation with pathology and related therapeutics, including dialysis and renal transplantation. Rapid advances in the omics sciences, medical device sensorization, and networked digital medical devices have made such research increasingly data centered. Data-centric science requires the support of computationally powerful and sophisticated tools able to handle the overflow of novel biomarkers and therapeutic targets. This is a context in which artificial intelligence (AI) and, more specifically, machine learning (ML) can provide a clear analytical advantage, given the rapid advances in their ability to harness multimodal data, from genomic information to signal, image and even heterogeneous electronic health records (EHR). However, paradoxically, only a small fraction of ML-based medical decision support systems undergo validation and demonstrate clinical usefulness. To effectively translate all this new knowledge into clinical practice, the development of clinically compliant support systems based on interpretable and explainable ML-based methods and clear analytical strategies for personalized medicine are imperative. Intelligent nephrology, that is, the design and development of AI-based strategies for a data-centric approach to nephrology, is just taking its first steps and is by no means yet close to its coming of age. These first steps are not even homogeneously taken, as a digital divide in access to technology has become evident between developed and developing countries, also affecting underrepresented minorities. With all this in mind, this editorial aim to provide a selective overview of the current use of AI technologies in nephrology and heralds the "Artificial Intelligence in Nephrology" special issue launched by BMC Nephrology. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations. [ABSTRACT FROM AUTHOR]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology: Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results: The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions: Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors. [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 paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solving a challenging particle transport problem. DeepONet also exhibits generalizability and computational efficiency as an efficient surrogate tool for DT component. However, the application of DeepONet reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world DT implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, this study marks an important step towards harnessing the power of DeepONet surrogate modeling for real-time inference capability within the context of DT enabling technology for nuclear systems. [ABSTRACT FROM AUTHOR]

This work aims to conduct an analysis to find opportunities for the implementation of software incorporating the concept of digital twins for foundry work. Examples of implementations and their impact on the work of enterprises are presented, as is a definition and history of the concept of a digital twin. The outcome of this work is the implementation of software that involves a digital copy of the author's device, created by the "Łukasiewicz" Research Network at the Krakow Institute of Technology. The research problem of this scientific work is to reduce the number of necessary physical tests on real objects in order to find a solution that saves time and energy when testing the thermal expansion of known and new metal alloys. This will be achieved by predicting the behavior of the sample in a digital environment and avoiding causing it to break in reality. Until now, after an interruption, the device often continued to operate and collect data even though no current was flowing through the material, which could be described as inefficient testing. The expected result will be based on the information and decisions obtained by predicting values with the help of a recurrent neural network. Ultimately, it is intended to predict the condition of the sample after a set period of time. Thanks to this, a decision will be made, based on which the twin will know whether it should automatically end its work, disconnect the power or call the operator for the necessary interaction with the device. The described software will help the operator of a real machine, for example, to operate a larger number of workstations at the same time, without devoting all their attention to a process that may last even for hours. Additionally, it will be possible to start work on selecting the chemical composition of the next material sample and plan its testing in advance. The machine learning handles model learning and value prediction with the help of artificial neural networks that were created in Python. The application uses historical test data, additionally retrieves current information, presents it to the user in a clear modern form and runs the provided scripts. Based on these, it decides on the further operation of the actual device. [ABSTRACT FROM AUTHOR]

The current trend in renovating existing buildings is to perform retrofits on a case-by-case basis without a systematic assessment, using static tools with broad assumptions and generic inputs. As a result, only about 1% of the building stock undergoes energy renovations each year. To address this issue, new approaches and modern tools are necessary to enhance and expedite energy retrofits in Danish buildings. While there were a few initiatives and projects exploring the implementation of digital twins in building applications, the focus is primarily on newly constructed, highly energy-efficient buildings with integrated building information models (BIM). Conversely, existing and older buildings often lack any form of digital modeling, making it challenging to implement digital twins in those contexts. This paper presents an innovative digital twin solution, 'DanRETwin', which will provide decision-making support, retro-commissioning, and data-driven performance optimization for non-residential existing buildings. The proposed solution will utilize building operational data, employing machine learning and artificial intelligence techniques to develop scalable data-driven models of building energy. Additionally, clamp-on IoT sensors will be used for data collection, enabling a fully automated and flexible solution. By utilizing DanRETwin, building owners will enjoy higher energy efficiency and improved comfort in their retrofitted buildings; facility managers will have an advanced monitoring solution that enables systematic retro-commissioning of their newly retrofitted buildings, eliminating faults and reducing losses; consultants will have a potential solution to retrofit, enhance, and optimize their clients' building performance, allowing them to make informed, data-driven decisions and interventions; and city planners will have an effective, scalable, and adaptable tool to expand retrofit efforts and evaluate various scenarios. [ABSTRACT FROM AUTHOR]

In the era of digitalization and artificial intelligence (AI), the utilization of Metaverse technology has become increasingly crucial. As the world becomes more digitized, there is a pressing need to effectively transfer real-world assets into the digital realm and establish meaningful relationships between them. However, existing approaches have shown significant limitations in achieving this goal comprehensively. To address this, this research introduces an innovative methodology called the Meta-Metaverse, which aims to enhance the immersive experience and create realistic digital twins across various domains such as biology, genetics, economy, medicine, environment, gaming, digital twins, Internet of Things, artificial intelligence, machine learning, psychology, supply chain, social networking, smart manufacturing, and politics. The multi-layered structure of Metaverse platforms and digital twins allows for greater flexibility and scalability, offering valuable insights into the potential impact of advancing science, technology, and the internet. This article presents a detailed description of the proposed methodology and its applications, highlighting its potential to transform scientific research and inspire groundbreaking ideas in science, medicine, and technology. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

The rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions. [ABSTRACT FROM AUTHOR]

Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model's accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool's condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data. [ABSTRACT FROM AUTHOR]

This research paper delves into the exploration of computer vision technology and digital twins as a means to enhance security measures in educational institutions. The study primarily focuses on the creation of a virtual replica of the first floor of a school and the integration of a person detection algorithm with the existing surveillance cameras. By leveraging the capabilities of the digital twin and real-time monitoring, comprehensive surveillance of the premises becomes feasible, resulting in simplified security operations. The paper sheds light on the significant potential of training neural networks for specific security tasks, such as the identification of weapons or the detection of anomalies in human behavior. These trained neural networks can be seamlessly integrated into the digital twin, thus ensuring public safety within the educational environment. The findings of this study provide substantial evidence for the effectiveness of computer vision technology and digital twins in bolstering security measures. The ability to create a virtual representation of the school's first floor enables comprehensive monitoring and surveillance, aiding in the prevention and prompt response to security incidents. The integration of person detection algorithms further enhances the system's capabilities by automatically identifying and tracking individuals within the premises. Additionally, the deployment of neural networks for specialized security tasks adds an extra layer of protection, enabling the identification of potential threats and the detection of abnormal behavior patterns. By employing computer vision technology and digital twins, educational institutions can establish an advanced security infrastructure that optimizes monitoring, enhances situational awareness, and ensures a safer environment for students, staff, and visitors. The research presented in this paper highlights the tremendous potential and practical implications of these technologies in the realm of educational security. [ABSTRACT FROM AUTHOR]

As companies are trying to build more resilient supply chains using digital twins created by smart manufacturing technologies, it is imperative that senior executives and technology providers understand the crucial role of process simulation and AI in quantifying the uncertainties of these complex systems. The resulting digital twins enable users to replay history, gain predictive visibility into the future, and identify corrective actions to optimize future performance. In this article, we define process digital twins and their four foundational elements. We discuss how key digital twin functions and enabling AI and simulation technologies integrate to describe, predict, and optimize supply chains for Industry 4.0 implementations. [ABSTRACT FROM AUTHOR]

• Evaluating the impact of a cyber-vulnerability on device-level resilience of solar-based networks. • Designing a structure for assessing device resiliency using controller code verification in digital twin environment. • Determining the effect on microgrid resilience applying a new machine learning based cyber-physical resiliency metric (CPRM). • Adjusting control actions to improved situational awareness of solar-based networks. Cyber-attacks (CAs) on electrical networks in presents of renewable energies, particularly solar energy, have become more complex and sophisticated over the past few years by making severe system outages. In light of increased automation in solar-based energy industries, a holistic cyber-physical infrastructure must be considered to predict the effect of CAs on electrical networks and the ways to enhance its resilience. The present study examines the resilience characteristics at the equipment area of the diverse control methods and their effect on the outage severity of the smart grid using digital twins (DT) simulation technology. The paper presents a machine learning based metric for measuring the resilience of cyber-physical features that considers device-level characteristics, vulnerabilities, and system models. Resiliency refers to a system's capability of providing energy even during severe contingencies and relates to the ability to resist, forecast, and recover. An example based on the newest CA against Ukraine has been provided and simulated on DT environment. A case study is proposed to illustrate how cyber-physical resilience metrics can be applied to improve operators' situational awareness and provide more proactive or corrective control measures for improving resilience. [ABSTRACT FROM AUTHOR]

The paper is devoted to the digital transformation of the core engineering maintenance processes involved in medium-voltage overhead distribution network infrastructure inspection. The study presents an analysis of digital twin utilization for inspection of the infrastructure of medium-voltage overhead distribution networks. At present, the infrastructure monitoring process is still manual, and its automatization is a challenging task due to the large distances between and small dimensions of the elements involved. The proposed digital twin is employed for 3D infrastructure modeling and complex analysis based on photogrammetry and aerial scanning data processing methods. The paper describes practical use cases for these data-driven methods for specific infrastructure management processes, e.g., scheduled inspection processes, including geometrical parameter measurements and visual infrastructure element defect identification, unplanned inspection processes (state of emergency, post-storm, etc.), and vegetation management processes. The proposed method allows operations to be performed remotely without physical presence in the field. At the same time, data-driven solutions provide objective results and potential automation via machine learning algorithms, which are more profitable economically. As the proposed method allows processes to be performed remotely, these data-driven solutions offer cost-effective results for automation. The digital twin concept is applied to the Latvian medium-voltage overhead distribution network with the support of the Latvian distribution system operator "Sadales tīkls" JSC. [ABSTRACT FROM AUTHOR]

This review discusses the links between the newly introduced concepts of digital twins and more classical finite element modeling, reduced order models, parametric modeling, inverse analysis, machine learning, and parameter identification. The purpose of this article is to demonstrate that development, as almost always is the case, is based on previously developed tools that are currently exploited since the technological tools for their implementation are available and the needs of their usage appear. This fact has rarely been declared clearly in the available literature. The need for digital twins in infrastructures arises due to the extreme loadings applied on energy-related infrastructure and to the higher importance that fatigue effects have. Digital twins promise to provide reliable and affordable models that accompany the structure throughout its whole lifetime, make fatigue and degradation prediction more reliable, and support effective predictive maintenance schemes. [ABSTRACT FROM AUTHOR]

The personal digital twin extends to individual persons, a concept that originated in engineering to twin complex machines with a digital simulation containing a model of its functions to monitor its past and present behaviour, and repair, correct, improve or otherwise ensure its optimal operation. Several independent trends in technological developments are seen to converge towards the elaboration of the digital replication of individual human data and life history, notably in health industries. Among the main ones, we consider the ubiquitous distribution of digital assistants, the rapid progress of machine learning concurrent with the exponential growth of 'personal' Big Data and the incipient interest in developing lifelogs. The core hypothesis here is that among the psychological effects of the digital transformation, the externalization of cognitive faculties such as memory, planning and judgement, the decision-making processes located within the human person are also emigrating to digital functions, perhaps as a prelude to a later re-integration within the person via brain–computer interfaces. The paper concludes with ethical considerations about these ongoing developments. This article is part of the theme issue 'Towards symbiotic autonomous systems'. [ABSTRACT FROM AUTHOR]

While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data. This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing. Moreover, it offers further insights into the adoption of innovative machine learning methods and novel trends in the development of new digital biopharma solutions. • Provide a review of applications of ML in antibody design and manufacturing. • Identifying challenges related to bioprocesses and process data. • Offering a valuable reference with a thorough discussion and public datasets. • Proposing machine learning (ML) research directions in antibody manufacturing. • Identifying advanced ML solutions for digital twins within biopharma 4.0. [ABSTRACT FROM AUTHOR]

• Digital twin solution is demonstrated for a CHS X-joint under fatigue loading. • Digital twin is capable of accurately identifying, locating and quantifying cracks. • Relationship between crack size and nearby strains is revealed via substructuring. • Crack diagnosis is performed with affordable strain sensors. • Physics-informed model is validated through strain-interfaced physical twins. This paper aims to introduce a strain-interfaced local digital twin solution for a welded circular hollow section (CHS) X-joint subjected to brace axial loading. The solution comprises a series of machine learning algorithms to (1) identify the presence of cracks, (2) locate the cracks and (3) quantify the extent of cracking. These algorithms make use of strain readings in the vicinity of the crack to perform the diagnosis, representing a remote sensing methodology, thereby eliminating physical inspections. The validation of the proposed methodology includes two experiments – one each in the high and low cycle fatigue regime – demonstrating its wide scale applicability. The success of these experiments highlights the strong potential of affordable strain sensors in crack diagnosis assessments for CHS joints. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) have attracted widespread attention in academia and industry in recent years. It can accurately reflect the physical world in real-time, enabling online monitoring, control, and prediction operations. Their foundation is super-real-time computing and high data representation capabilities. However, current DTs do not achieve 3D super-real-time computing. This study proposes a novel 3D computational method for solving fluid–solid coupling problems in a super-real-time. The method is based on a mixed solution framework that combines traditional numerical methods with deep learning operators. Specifically, the method employs multi-core CPU parallel acceleration to solve the solid equations while leveraging the computing power of GPU to solve the fluid equations. The fluid–solid coupling is achieved through information exchange between the GPU and the multi-core CPU. In addition, the proposed method introduces a new deep learning operator framework based on the DeepONET. The framework is accompanied by a database structure that facilitates model training and validation and a loss function that guides the training. The space nuclear reactor, an improved TOPAZ-II system, was selected to demonstrate its feasibility. Four non-training transient conditions were simulated to test the generalization performance. The results show that the proposed method achieves an average error between the calculated results and reference values below 2.5%, with the average error of thermodynamic parameters below 1.5%. The average deviation between system parameter peak values during the transient process and the reference value was less than 5 s. The result meets the acceptable error level and satisfies the super-real-time requirements with a time acceleration ratio of approximately 1.17, which is 60 times faster than traditional numerical methods. The results demonstrate the accuracy and efficiency of the proposed method for DT. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [ABSTRACT FROM AUTHOR]

A fully autonomous ship should be monitored in a digital environment to track its real-time response to the changes in its surroundings. It is commanded by a controller algorithm whose optimum parameters is subject to change with respect to the changes in the environment. The ship's response with respect to these variations in the controller parameters need to be tracked to develop a full understanding of the ship's autonomy. As such, it is believed that the digital twin of a ship is an essential component of the path to fully autonomous ships. Despite maritime institutions not giving the credit to it yet, the ship digital twin concept is expected to be one of the leading topics in marine engineering soon. In this study, we try to establish the ship digital twin concept in terms of navigation autonomy. The paper starts by introducing the path to full autonomy in seas and the need for the digital twin by defining this concept for ships. A maneuvering mathematical model is used to represent the physical ship. The details of the model and the controller algorithms are given next. The propeller and rudder models are first validated by free-running self-propulsion and turning circle tests. The ship considered in this study has a twisted rudder and does not possess course-keeping ability; therefore, it requires autopilot to move straight ahead. Three types of simulation cases are identified with one being the autopilot mode. In the other two cases, the ship is forced to conduct two hard maneuvers while accelerating/decelerating. Investigation of the generated results reveals the effectiveness of the digital twin architecture used in this study. It has also allowed us to conduct a controller stability analysis, which has shown the behavior of the controller gains to detect the most optimum values for the considered model ship. • Ship digital twin (SDT) is a misunderstood concept in marine engineering: it is not a digital model. • SDT is an essential component of the path to fully autonomous ships. • It can be used to enhance navigation autonomy and this study presents a digital twin architecture. • SDT offers an environment to optimize controller gains before implementation. • The environment is open for improvement when coupled with machine learning algorithms. [ABSTRACT FROM AUTHOR]

With the advancement of information technologies such as the Internet of Things and artificial intelligence, cyber-physical systems are being introduced into society. At the core of these systems is the digital twin, a computer model (twin) of a physical entity built in cyberspace for simulation-based prediction. Currently, the digital twin mainly targets artificial objects, such as aircraft engines and factories. However, if it can be extended to humans, it could lead to the realization of human-machine cooperative systems and health promotion services, thereby solving social issues stemming from the aging of society. However, humans are the weakest link in the system, and many technical problems remain to be solved, such as realizing the measurement, modeling, and prediction of human behavior, if the human digital twin is to become reality. This special issue contains 9 papers on developing essential technologies for the human digital twin and constructing human-machine systems for specific applications. The topics covered include learning algorithms, motion measurement and analysis techniques, human perception, system development, and platform software for system development. These clearly show that cross-disciplinary efforts are essential to the realization of the human digital twin. We thank the authors of the papers submitted for this special issue. We are confident that the information provided by the authors is suggestive and informative for both specialists and non-specialists alike. We also sincerely appreciate the efforts of the reviewers. Their contributions helped to make this special issue possible. We hope that this special issue will catalyze sharing across the boundaries of research fields along the path to realizing a human digital twin. [ABSTRACT FROM AUTHOR]

• Digital twin with AI to develop a predictive monitoring system that estimates CO2-eq and TVOC emissions from existing buildings. • The predictive monitoring system of CO2-eq emissions was developed using the design science research paradigm. • Real-life case study was used to validate the created solution. • This solution introduces interactive dashboard to predict Co2-eq based on varying humidity percentages and temperature degrees. The revolution of the industry 4.0 presents a new era of digital transformation for the construction industry, advancing towards the concept of digital twins, while on the other hand it faces the global challenge of reducing carbon emissions from operational assets. The current research gap in the application of digital twins for achieving net zero were reviewed in this study, highlighting its potentials for enhancement in the built environment, and emphasizing the need for demonstration of a use-case analysis for its adoption by the industry. This research presents a digital twin solution to automate the monitoring and controlling of equivalent carbon dioxide (eCO 2) emissions from existing assets through the integration of IoT, BIM, and artificial intelligence across a comprehensive solution, further validating its workability through a real-life use case analysis. The study revealed the significance of BIM and IoT, as essential components of a digital twin to visualise critical spatial information for enhanced facility management specifically for monitoring of indoor air quality of spaces, while also coalescing an AI-supported system to predict carbon emissions from the collected data through integration of machine learning features across the digital twin. The output of the entire solution is displayed as an interactive dashboard for observing trends and patterns, enabling stakeholders to implement effective data-driven retrofitting strategies. This research is a fundamental initiation for implementing digital twins to monitor emissions from existing assets, a step towards achieving the net zero targets. [ABSTRACT FROM AUTHOR]

Characterization, digital twins, machine learning, simulation Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Keywords: characterization; digital twins; machine learning; simulation EN characterization digital twins machine learning simulation 1 1 1 09/05/22 20220901 NES 220901 B Lithium Batteries b In article number 2200889, Kai Yang, Shichun Yang and co-workers demonstrate the potential of cyber hierarchy and interactional network framework (CHAIN) to bridge multi-scale characterization and modeling technologies to evaluate lithium battery overall lifecycle. [Extracted from the article]

The increasing digitalization and advancement in information communication technologies has greatly changed how humans interact with digital information. Nowadays, it is not sufficient to only display relevant data in production activities, as the enormous amount of data generated from smart devices can overwhelm operators without being fully utilized. Operators often require extensive knowledge of the machines in use to make informed decisions during processes such as maintenance and production. To enable novice operators to access such knowledge, it is important to reinvent the way of interacting with digitally enhanced smart devices. In this research, a mobile augmented reality remote monitoring system is proposed to help operators with low knowledge and experience level comprehend digital twin data of a device and interact with the device. It analyses both historic logs as well as real-time data through a cloud server and enriches 2D data with 3D models and animations in the 3D physical space. A cloud-based machine learning algorithm is applied to transform learned knowledge into live presentations on a mobile device for users to interact with. A scaled-down case study is conducted using a tower crane model to demonstrate the potential benefits as well as implications when the system is deployed in industrial environments. This user study verifies that the proposed solution yields consistent measurable improvements for novice users in human-device interaction that is statistically significant. [ABSTRACT FROM AUTHOR]