Search

Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [ABSTRACT FROM AUTHOR]

Nowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

The adoption of simulation models as Digital Twins (DTs) has been standing out in recent years and represents a revolution in decision-making. In this context, we note increasingly faster and more efficient decisions by mirroring the behaviour of physical systems. On the other hand, we highlight the challenges to ensure the simulation models validity over time since traditional validation approaches have limitations when we consider the periodic update of the model. Thus, the present work proposes an approach based on the constant assessment of these models through Machine Learning and control charts. To this end, we suggest a monitoring tool using the K-Nearest Neighbors (K-NN) classifier, combined with a p-control chart, to periodically assess the validity of DT simulation models. The proposed approach was tested in several theoretical cases and also implemented in a real case study. The findings suggest that the proposed tool can monitor the DT functioning and identify possible special causes that could compromise its results. Finally, we highlight the wide applicability of the proposed tool, which can be used in different DT models, including near/real-time models with different characteristics regarding connection, integration, and complexity. [ABSTRACT FROM AUTHOR]

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. [ABSTRACT FROM AUTHOR]

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [ABSTRACT FROM AUTHOR]

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases. Due to dynamic problem-solving capabilities, DT technologies are widely used in performance improvement analysis in food processing companies (FPC) despite limited implication's for business strategies. Our study incorporates the DT technologies with an implementation case study on FPC and accomplishes the real-life problem of the food processing company (FPC). Moreover, the proposed DT research framework demonstrates the various DT implementation stages, such as strategic mapping and physical-virtual space replica, with rigorous analysis. The results show that DT enhances the existing system's machine availability, allocation efficiency, technical efficiency, worker efficiency, utilization rate, effectiveness, step ratio, and throughput rate. The proposed physical-virtual interface model is executed using AnyLogic software with JAVA-enabled programming. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on digital twins (DTs) and their applications. However, a common method for evaluating the performance of DTs is lacking. Digital twins performance assessment mechanism is crucial when improving or monitoring any process or system in a business, such a strategy could serve as a guide to help researchers and practitioners create more effective digital twins. Guided by these challenges, we propose a structured method for measuring the performance and flexibility of digital twins, and our study provides a procedure for quantitative calculation of the performance of DTs based on four key performance indicators (KPIs). Moreover, we propose a new KPI, DTflex, for determining the flexibility of digital twins. We evaluated the performance of DTflex as a KPI to demonstrate its use as a simple tool for designers and practitioners to quickly compare different DT methodologies, which might provide valuable input for the iterative improvement of DTs to increase their efficiency in future applications. The knowledge provided along with the method proposed in this paper is expected to help researchers and practitioners working in different fields to compare DT methodologies and DT performance which will lead to higher efficiency DT solutions and increased re-usage of existing DTs. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the literature concerning the implementation of digital twins (DTs) for zero-defect manufacturing (ZDM) following a systematic method and, guided by a preliminary finding that a structured and standardised approach to the development of the DT applications is lacking, we provide a standardised design methodology to guide researchers and practitioners in their efforts to develop DTs regardless of the domain. After examination and interpretation of the literature, we also present the results of our state-of-the-art analysis, discuss the current state and limitations of research and practice, and provide useful insights on this important and complex topic. The design methodology proposed in our study will benefit both practitioners and academicians by covering the essential elements to be considered when developing DTs for ZDM for any applications in this domain. The study also contributes to knowledge by presenting a structured overview of the specific research area with a comprehensive, systematic, and critical analysis of the literature and by providing answers to some fundamental questions in the context of DTs for ZDM. Finally, we provide suggestions for further developments in research and practice. [ABSTRACT FROM AUTHOR]

The Spherical Tokamak for Energy Production (STEP) programme is an ambitious but challenging endeavour to design and deliver a prototype fusion power plant. It is a rapid, fast-moving programme, designing a first of a kind device in a Volatile, Uncertain, Complex and Ambiguous (VUCA) environment, and digital tools play a pivotal role in managing and navigating this space. Digital helps manage the complexity and sheer volume of information. Advanced modelling and simulation techniques provide a platform for designers to explore various scenarios and iteratively refine designs, providing insights into the intricate interplay of requirements, constraints and design factors across physics, technology and engineering domains and aiding informed decision-making amidst uncertainties. It also provides a means of building confidence in the new scientific, technological and engineering solutions, given that a full-scale-integrated precursor test is not feasible, almost by definition. The digital strategy for STEP is built around a vision of a digital twin of the whole plant. This will evolve from the current digital shadow formed by system architecting codes and complex workflows and will be underpinned by developing capabilities in plasma, materials and engineering simulation, data management, advanced control, industrial cybersecurity, regulation, digital technologies and related digital disciplines. These capabilities will help address the key challenges of managing the complexity and quantity of information, improving the reliability and robustness of the current digital shadow and developing an understanding of its validity and performance. This article is part of the theme issue 'Delivering Fusion Energy – The Spherical Tokamak for Energy Production (STEP)'. [ABSTRACT FROM AUTHOR]

Green hydrogen is a potential contributor to a more sustainable transportation system. In this paper, we present our experiences and insights gained from an industrial-scale green hydrogen refilling station, marked by the innovative hybrid Edge–Cloud electrolyser system (EMS) employed in this context. Hybrid EMS can improve system performance, adapt to dynamic conditions, and continuously improve efficiency, even under unpredictable load and low storage situations. It also allows the centralized monitoring of future hydrogen production sites to facilitate the operation and maintenance and, in the long-term vision, to enable the joint coordination of the plants flexibilities in the service market as a virtual plant, thus increasing the cost efficiency. Through a real-time digital-model based assessment of the EMS, this study offers valuable lessons learned and findings, contributing to a deeper understanding of EMS in green hydrogen infrastructure. • Description of a commercial Energy Management System (EMS) developed by the company ABB. • Demonstration of the benefits of the hybrid edge–cloud EMS architecture. • Application of an EMS to a hydrogen fueling station with on-site electrolysis. • Real-time digital-model assessment, offering valuable lessons learned contributing to a deeper understanding of EMS for H2. [ABSTRACT FROM AUTHOR]

Thermal error significantly influences the accuracy of precision computer numerical control (CNC) machine tools. The key to compensating thermal error lies in selecting appropriate temperature measurement points and establishing an accurate error prediction model. Traditional methods separate measurement points selection and prediction modeling, that is, selecting temperature measurement points first and then establishing the prediction model using these points. These methods are difficult to achieve optimal matching between measurement points and the prediction model, resulting in shortcomings in modeling accuracy. To address these challenges, an integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools is proposed. A dual-stage attention-based long short-term memory combined with convolutional neural network (DA-CLSTM) error modeling method is proposed to accurately predict the thermal error for different numbers of temperature measurement points. Then, an integrated method of virtual-real temperature measurement points layout and error modeling is proposed, which ensures the temperature measurement points and the error prediction model are closely matched, offering rational sensors layout and high-accuracy prediction. Finally, experiments are conducted on the spindle system test bench to validate the method proposed in this research. Through integrated optimization of measurement points layout and the prediction model, the proposed method achieves high-accuracy thermal error prediction across various sensor counts and maintains reliable prediction ability when the number of sensors is reduced. This method is applied to a digital twin system of MKL7150 grinding machine, and effectively improves the accuracy of actual machining. • An integrated optimization method for measurement points layout and error modeling of CNC machine tools is proposed. • A dual-stage attention-based long short-term memory combined with convolutional neural network model is designed. • Genetic algorithm is used to optimize the layout of measurement points to achieve the optimal prediction effect. • The proposed method achieves high prediction accuracy and maintains accuracy well with fewer sensors. • The method is validated on the test bench and embedded into the digital twin system to achieve thermal error prediction. [ABSTRACT FROM AUTHOR]

Improvements are necessary for the performance improvements of the digital twin technology developed for the virtual energy market on the Metaverse platform. However, more important factors need to be improved first to avoid excessive increases in costs. Thus, a priority analysis needs to be carried out to determine the variables that most affect the performance of technology investments. Accordingly, the purpose of this study is to evaluate the investments of digital twin technologies for virtual energy market in the Metaverse. A novel artificial intelligence-based fuzzy decision-making model is constructed to reach this objective. Firstly, the expert choices are prioritized with artificial intelligence-based decision-making method. Secondly, the investment priorities are analyzed for digital twin technologies with quantum picture fuzzy rough sets (QPFRS)-based Multi Stepwise Weight Assessment Ratio Analysis (M-SWARA). Finally, the alternatives for virtual energy market in the metaverse are ranked by VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje). There are limited studies in the literature that computes the weights of the experts while generating a decision-making model. Therefore, the main contribution of this study is integrating the artificial intelligence approach and fuzzy multi-criteria decision-making methodology. Within this scope, an artificial intelligence-based application is performed when creating the decision matrix. Owing to this issue, the importance weights of experts are determined according to the qualifications of these people. This situation contributes to the results obtained being more realistic. The findings demonstrate that operational performance is the most important indicator for the improvements of the digital twin technology investments for virtual energy markets in metaverse platform because it has the greatest weight (0.267). Furthermore, integrated data production is another critical factor for the performance increase of these projects with the weight of 0.257. It is also concluded that optimization of energy consumption with smart grids has the best ranking performance among the alternatives. [ABSTRACT FROM AUTHOR]

Due to the inherent unpredictability of marine trial conditions, safety issues in the launching and recovery process are commonplace. For these issues, digital twin (DT) maintenance offers a novel idea. In this paper, a blended-wing-body underwater glider skeleton is selected as a research object. And a simple demo is created to investigate the major technologies that may be involved in DT maintenance. According to this specimen, the necessary development environments are developed to obtain physical information and produce a DT model in cyberspace respectively. An attitude sensor is used as the physical data collector, while real-time structural field prediction and virtual reality visualisation are employed. By using the attitude angles and structural data, it is possible to achieve real-time monitoring of the skeleton strength. Through the means of this straightforward case study, the key technologies are supported and can be applied to far more complex inquiries. [ABSTRACT FROM AUTHOR]

Approximately 45% of ship delivery delays are due to welding quality. To solve the problematic control of production tempo and process sequence optimization in the welding process, it is urgent to combine the characteristics of the digital twin for dynamic simulation and optimization. Therefore, the capacity evaluation and scheduling optimization for the ship welding production line (WPL) based on the digital twin is proposed. Firstly, to describe the construction method of the digital twin model and digital twin data, a strategy is proposed for the construction of a digital twin ship component WPL model. Based on the fusion mapping of model and data, the construction of the digital twin model for WPL (DTM-WPL) is achieved. Secondly, by using equipment failure rate, processing time and buffer capacity as evaluation indicators, an WPL optimization model based on digital twins is constructed to solve the WPL production sequencing problem. Thirdly, to illustrate the welding quality traceability and prediction process, a DTM-WPL synchronous mapping for quality prediction and adjustment method is proposed. Finally, taking small and medium-sized WPL as the research object, the capacity evaluation and scheduling optimization system of ship components is developed to evaluate the production capacity of ship components. The validation results indicate that the optimized process scheme has increased production efficiency by 7.27%. [ABSTRACT FROM AUTHOR]

Accurate defect detection is crucial for ensuring the trustworthiness of intelligent railway systems. Current approaches either rely on static ensembling of Convolutional Neural Networks (CNNs) or on single deep-learning CNN models. Traditional methods use a large volume of data to identify underlying patterns. However, training a new defect classifier with limited samples often results in overfitting and poor performance on unseen images. To overcome these challenges, we introduce a dynamic weight adaptation mechanism within an ensemble learning framework, enhanced by state-of-the-art transfer learning models (VGG-19, MobileNetV3, and ResNet50). We obtained a good test and validation accuracy of 99%, accompanied by a precision of 0.99, and recall and F1-score of 0.98. Unlike previous methods, our approach demonstrates consistency. Particularly, techniques that involve selecting predictions based on minimum loss previously required a significant number of epochs to stabilize. Data fusion techniques are even more demanding, requiring a substantially higher number of epochs for stabilization. Conversely, our proposed method achieves stable performance and rapid convergence within just 10 epochs, and it shows minimal fluctuation in the training curve (0–5 epochs). This visibly contrasts with earlier methods, which, despite reaching similar levels of accuracy, required significantly more epochs and exhibited greater fluctuations in their training curves. Through these findings, we anticipate that the proposed dynamic weight adaptation-based ensemble approach will further research and development of reusable AI-enabled solutions for rail defect classification. [ABSTRACT FROM AUTHOR]

In recent years, the integration of digital technologies has grown rapidly in the field of thermal comfort and energy efficiency for buildings. The concept of a digital twin, incorporating multiple digital technologies, has gained increasing attention. The literature lacks a review of the digital twin concept in thermal comfort and energy consumption for existing buildings. This paper conducts a review of the current state-of-the-art in digital twin (DT) technology for thermal comfort/ energy consumption in buildings. The review employs a scientometric approach and examines various technologies used in creating DTs and a systematic analysis of the methods, technologies, algorithms, and approaches used in digital twin experiments. The results show a growing number of studies in this area, with a focus on thermal comfort monitoring, visualization, tracking, energy management, prediction, and optimization for existing buildings. Furthermore, the prediction of energy consumption using algorithms such as Artificial Neural Networks (ANN), Artificial Intelligence (AI), deep neural networks, and YOLOv4 have been used in buildings. However, the wider adoption of a DT that can facilitate occupants, and thermal sensations, enhance human-centered solutions, and improve energy prediction levels are necessary. There is a need for further international collaboration to expand the studies on digital twins for thermal comfort and energy efficiency. The review highlights the limitations and areas of improvement, such as the limited adoption of sensors for environmental measures, the need for more focus on the subjective perception of occupants, and the need for more comparative studies of algorithms for predicting energy consumption. Further studies can be conducted in areas such as understanding occupant psychological responses/behaviors to comfort in the digital world. This will enhance a more consolidated and robust validation for building performance. [ABSTRACT FROM AUTHOR]

Decentralized periodic event‐triggered control can reduce the working time and data transmissions in the sensor nodes for feedback control loops, making it suitable for wireless networked control systems, especially those with large physical scales, such as smart water distribution systems. However, the design of this control approach requires accurate system model, which is normally difficult to obtain. In this work, we assume a digital twin of the original system can be obtained by some modeling and simulation tools, for example, Simscape in MATLAB/Simulink for water distribution systems, and thus present an event‐triggered controller design approach. We first present the design of such an event‐triggered controller for the digital twin; and then analyze the condition under which if both the original system and its digital twin can be satisfied, the same controller can work for the original system and some type of stability can be guaranteed at the same time. The proposed approach is applied in a smart water distribution system to show the feasibility. [ABSTRACT FROM AUTHOR]

During service, civil turbofans experience environmentally induced deterioration. Predicting this in a digital service twin model is computationally challenging due to the need to model both deterioration mechanisms and environmental conditions. For compressor erosion, a key challenge is to model particle ingestion throughout a flight mission (FM). During ground operations, these particles may be airborne or deposited on runways and taxiways. This work assesses the impact of the latter on turbofan core compressor deterioration during a mission. The airflow field in front of the engine intake is approximated using potential flow theory. Comparisons with measurements show that the predicted air velocity near the engine is underestimated since the inlet ground vortices generated from viscous effects are neglected. The forces acting on the particles are derived from the flow field. It turns out that most particles are lifted from the ground during take-off (TO). Yet only smaller particles below ≈ 50 µm are ingested into the engine intake. A deterioration model based on flat plate erosion experiments is used to compute mission severity, assuming all particles are similar to medium Arizona Road Dust. The results indicate that the engine's distance from the ground, power setting, and the number of particles on the ground are key parameters influencing the impact of runway and taxiway particles. Considering the underestimation of the airflow field and thus the number of particles ingested, it is concluded that runway and taxiway particles play a major role in turbofan compressor deterioration. [ABSTRACT FROM AUTHOR]

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers. [ABSTRACT FROM AUTHOR]

The economic growth of developed or emerging countries through globalization has prompted them to increase their supply chain performance. A large number of concepts, tools, and methodologies have been proposed in support of this performance improvement. They are mainly based on the use of classical optimization or enterprise modeling methods. However, environmental and social issues, not to mention digital transformation, are often ignored or not sufficiently integrated. Indeed, the world geopolitical situation, the increase in oil prices, and the commitment to protect our earth require the integration of sustainability aspects and Industry 4.0 concepts like digital twin and artificial intelligence in transforming the supply chain. This paper focuses on defining a conceptual framework to support sustainable supply chain management and digital transformation. It aims to exploit the sustainability and digital maturity of companies to transform their supply chains and enhance their performance to meet the challenges of Industry 5.0. Several practices related to sustainability, as well as two use cases on optimization and digital twin, are presented to illustrate this framework. Finally, based on the previous practices and use cases, an adapted framework for the supply chain manager to support the transition from Industry 4.0 to Industry 5.0 has been developed, as well as a performance dashboard. [ABSTRACT FROM AUTHOR]

Human-robot collaboration (HRC) in assembly is becoming increasingly popular in the field of intelligent manufacturing. However, safety concerns remain the most critical issue to be addressed. The complex assembly environment and the individual differences of workers make it challenging to ensure safety effectively. Furthermore, the concept of human-centric manufacturing also places higher requirements on safety and efficiency of HRC assembly. The application of digital twin (DT) technology has bolstered the cognitive and decision-making capabilities in the context of HRC assembly. This technological advancement provides essential support for addressing safety concerns in HRC assembly processes guided by a human-centric approach. A personalised safety detection method for HRC assembly based on DT technology is proposed. This approach involves personalised modelling and simulation of real objects within the HRC process, establishing a collision detection model based on skeleton points. Subsequently, human motion prediction methods drive safety monitoring between humans and robots. Ultimately, personalised safety warning strategies are applied to ensure the safety of the assembly process. [ABSTRACT FROM AUTHOR]

While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) is considered the most modern simulation method when it comes to tightly connect the real and virtual worlds to produce accurate simulation models of constant use to aid stakeholders in decision making. This method has been used more intensively in the manufacturing sector, but its use has spread to other sectors of great relevance in the economy, such as the retail industry. The contribution of this article to existing research is to present a Systematic Literature Review (SLR) addressing the state-of-the-art of the potential use of Digital Twins (DTs) to support decision making in the retail field. The main findings illustrate a considerable appearance of case studies applied directly in the sector, a strong investment in research focused on supply chains, an extensive use of simulation models and sensors, however, that mostly make use of secondary data and are not completely autonomous. Summary tables of the main benefits, opportunities and challenges in applying DTs in the retail sector are also presented. [ABSTRACT FROM AUTHOR]

Automation is the guiding principle of this new era, and despite the problems that humanity faces as a result of automation, technology has greatly benefitted people by streamlining challenging jobs across many industries. The mining business, where there are frequently unforeseen mishaps, is one such industry that requires complete automation. In this work, a new simulative processing environment termed DTFL‐DF—Digital twin federated learning decision forest a digital twin environment that is tailored to handle unforeseen fire incidents—is offered as a means of avoiding these unplanned catastrophes in the mining industry. Although the design presented here is intended for usage in the mining sector, it can also be applied to other sectors. The overall technological contribution of this study is to guarantee the processing of real‐time data in order to successfully handle mission‐critical operations without relying on past data. This is accomplished by adapting the digital twin's original design and distributing the processing environment within the edge‐fog layer. Results analysis in the form of robustness analysis, performance evaluation of the classification model, etc. provides strong support for the suggested methodology. For handling the decentralized training procedure, a brand‐new algorithm termed FL‐DF is put forth in order to speed up classification and prevent any sort of catastrophe. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things and artificial intelligence in the era of Industry 4.0 has transformed decision-making within production systems. In particular, many decisions that previously required significant human activity are now made automatically with minimal human intervention via so-called digital twins (DTs). In the context of maintenance and reliability modeling, this naturally calls for new paradigms that can be seamlessly integrated within DTs for decision-making. The input data for time to failure needed in reliability computations are directly collected from the work center in a digital setting and often do not satisfy a known distribution. A neural network (NN) is proposed here to bypass this difficulty within the DT. Further, an algorithm inspired from machine learning is employed to solve the underlying semi-Markov decision process, whose transition model is captured via the NN. Numerical studies are carried out to demonstrate the usefulness of the approach. Finally, convergence properties of the algorithm are analyzed mathematically. [ABSTRACT FROM AUTHOR]

The rapid booming of information and communication technologies (ICT) and artificial intelligence has promoted the development of maintenance practices in the building industry towards a data-driven paradigm, of which the digital twin (DT) serves as the fundamental framework to strengthen data transit ability and interoperability. Among the state-of-the-art approaches in the maintenance industry, predictive maintenance (PdM) is a prominent approach by anticipating unexpected failures and unscheduled maintenance tasks. However, most current PdM frameworks are facility-specific, lacking generality and scalability. Besides, existing solutions mainly concentrate on condition monitoring and fault identification rather than failure prediction. Moreover, good prediction results rely heavily on sufficient labelled data sets, which are costly and labour-intensive to collect. To address these issues, the author developed a unified PdM framework for the building industry from the DT perspective. Next, a novel failure prediction method utilising the Semi-supervised Generative Adversarial Network (GAN) has been proposed in this article, which makes effective utilisation of both labelled and unlabelled data. Finally, an online platform has been developed to present the monitoring and prediction information. Experimental findings show the effectiveness and superiority of the proposed method for failure prediction through public data sets of building facilities. [ABSTRACT FROM AUTHOR]

Digital twin represents the core technology to realize the dynamic monitoring of complex industrial systems. However, the human body, as the most complex system in the physical world, digital twin is rarely applied in it. In this study, we successfully demonstrated a digital twin in the human biomedical application by proposing a dynamic monitoring system of the upper limb force. In this system, the real upper limb drives the motion of the virtual one in real-time and dynamically updates the force. Meanwhile, the virtual upper limb feeds back the monitoring-results of the force to the controller of the real upper limb via immersive virtual reality interaction. Experimental results of the typical motions of the upper limb revealed that the proposed system functioned interactively in real-time in a non-invasive manner, while ensuring the accurate solving of the muscle force. In conclusion, our digital twin-driven system is of great importance for rehabilitation medicine, biomechanical scientific research and physical training, promoting the application of the digital twin in the human biomedical field. [ABSTRACT FROM AUTHOR]

In the realm of Digital Twins (DTs), industry experts have emphasised the pivotal concept of the Federation of Twins, envisioning seamless collaboration across sectors driven by shared semantics. In response to this challenge, the Cognitive Digital Twin (CDT) integrates the DT framework with formal semantics, specifically ontologies. This paper introduces a comprehensive five-step methodology for CDT development. Furthermore, it becomes possible to incorporate human expertise into the DT ecosystem by adopting an ontological approach. The CDT enhances DT services with advanced reasoning capabilities, leading to a profound semantic enrichment of the data. The presented methodology has been validated using a use case where the CDT is employed to detect malfunctions, significantly reducing manual intervention. This paper advocates for the adoption of CDTs, which represent a harmonious fusion of formal semantics and human expertise, enhancing system efficiency and operational performance. • A comprehensive 5-step methodology leveraging ontologies enriches Digital Twin (DT) development and semantics of the data. • Cognitive Digital Twins (CDTs) use formal semantics and facilitate the formalisation of human expertise. • Practical validation through real-world use cases demonstrates CDT's potential for reducing manual intervention. [ABSTRACT FROM AUTHOR]

The production industry is challenged to become more flexible and efficient while coping with a variety of disruptive events, such as natural disasters, infrastructure blockages, or economic crises. From the individual station on a production line to the global supply chain, everything is connected, making regulation and control a complex task. Biological molecular processes, such as the metabolism of living organisms or the cell cycle, are also extremely complex processes that can be compared to industrial production processes, both of which involve a series of intermediate steps and products. Thanks to (self‐)regulatory mechanisms that have evolved over time, these biological mechanisms are very efficient and robust in the face of perturbations. This article proposes an explanatory representation of these complex processes, considering both biological and technical aspects. The aim is to facilitate biomimetic transfer of biological regulation mechanisms into the technical domain. It presents concepts for biomimetic regulation of production lines and sourcing strategies and introduces a workflow for generating digital twins. This workflow is inspired by the cell cycle checkpoints, which ensure that only perfect copies of DNA are passed on during cell replication. By leveraging this understanding, the production industry can potentially improve its own processes and efficiency. [ABSTRACT FROM AUTHOR]

This paper provides the results and the analysis of a study conducted on the processes and technologies currently used by European Union shipyards, and their potential interest in new advanced composite construction technologies, including Adaptive Moulds, Automated Tape Laying, Automated Fibre Placement, Curved Pultruded Profiles, Additive Manufacturing, Hot Stamping, Modular and Serialised Shipbuilding and Digitisation of Production. A new set of indexes are also introduced with the objective of facilitating the evaluation of the technological level of the shipyard, thereby allowing the understanding of the shipyard's interest in new developments. The analysis of the survey conducted shows that almost the 95% of shipyards either currently use or plan to use composite materials, being the composites made of fibreglass and polyester resin the most used. These composites are mainly manufactured by manual lamination and vacuum infusion techniques. The survey has also shown that there is a high interest in the shipyard digitisation and the use of new technologies in the shipbuilding industry, especially for new construction shipyards. The study shows that despite shipyards want to adopt digitisation in engineering and design processes, implementation of new technologies and concepts is being held back by financial cost and uncertainty regarding outcomes such as improved operational efficiency of vessels. The shipyards that do not use composites are less technologically advanced than those that use them, according to the analysis of the technological indexes. This analysis has also shown that the shipyards with a high technological index have a more varied potential market offer. [ABSTRACT FROM AUTHOR]

A digital twin is a new trend in the development of the current smart water conservancy industry. The main research content of intelligent water conservancy is clarified. This paper first summarizes and combs the relevant system architecture of smart water conservancy, and puts forward a smart water conservancy framework based on digital twins, highlighting the characteristics of virtual and real interaction, and symbiosis of the water conservancy twin platform. Secondly, the status quo of intelligent water conservancy "sky, air, ground and water" integrated monitoring technology, big data and artificial intelligence, model platform technology, knowledge graph and security technology is analyzed. From the perspective of application, the research progress of each technology in water security, water resources and hydraulic engineering is reviewed. Although the construction of smart water conservancy has made remarkable progress, it still faces many challenges such as data governance, technology integration and innovation, and standardization. In view of these challenges, this paper puts forward a series of countermeasures, and looks forward to the future development direction of intelligent water conservancy. [ABSTRACT FROM AUTHOR]

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]

Digital twins (DTs) provide accurate, data-driven, real-time modeling to create a digital representation of the physical world. The integration of new technologies, such as virtual/mixed reality, artificial intelligence, and DTs, enables modeling and research into ways to achieve better sustainability, greater efficiency, and improved safety in Industry 4.0/5.0 technologies. This paper discusses concepts, limitations, future trends, and potential research directions to provide the infrastructure and underlying intelligence for large-scale semi-automated DT building environments. Grouping these technologies along these lines allows for a better consideration of their individual risk factors and use of available data, resulting in an approach to generate holistic virtual representations (DTs) to facilitate predictive analyses in industrial practices. Artificial intelligence-based DTs are becoming a new tool for monitoring, simulating, and optimizing systems, and the widespread implementation and mastery of this technology will lead to significant improvements in performance, reliability, and profitability. Despite advances, the aforementioned technology still requires research, improvement, and investment. This article's contribution is a concept that, if adopted instead of the traditional approach, can become standard practice rather than an advanced operation and can accelerate this development. [ABSTRACT FROM AUTHOR]

This article presents the methodology and process of the modeling, designing, and testing of a research station enabling the identification, tuning, and verification of Digital Twin (DT) in control systems in power hydraulics. The concept of the station is presented, the main part of which is a subsystem forcing a dynamic, variable load using a hydraulic actuator controlled by a proportional valve, together with data acquisition and processing systems and software. The load application subsystem can subject the tested power hydraulic system, usually consisting of an actuator with its own control system, to controlled dynamic loads, which allows for the determination of its characteristics and tuning of the DT. This article describes the electro-hydraulic elements used to adjust the position of the actuators and the designed hydraulic and electrical diagrams of the station. The process of identifying, modeling, and selecting a controller for a subsystem simulating an external load is presented. The test station is built and tested. The identification, modeling, and tuning of DTs, for example, actuators and controllers, are described. A satisfactory convergence of the simulation and modeling results with the operation of the real system is achieved, which allows the obtaining of a reliable DT for actuator control systems operating under variable load. [ABSTRACT FROM AUTHOR]

The integration of high-penetration renewable energy sources is playing an increasingly important role in the frequency regulation of the power system. However, due to the varying output characteristics and response speeds of different renewable distributed energy resources (DERs), the overall response from the collective output of these distributed energy resources may not meet expected requirements and could have adverse effects on the grid. One drawback of traditional distributed coordinated control is its high communication requirements. This paper proposes using digital twin (DT) technology for the coordinated control of distributed energy resources, which can minimize the communication needs between various distributed energy resources while achieving coordinated control. Verification of the frequency support coordinated control strategy based on digital twin technology shows that it can effectively enhance the individual output characteristics and overall response of each distributed energy resource, providing effective support for grid frequency. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik 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.)

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.)

Purpose: This paper aims to investigate the theoretical and practical links between digital twin (DT) application in heritage facilities management (HFM) from a life cycle management perspective and to signpost the future development directions of DT in HFM. Design/methodology/approach: This state-of-the-art review was conducted using a systematic literature review method. Inclusive and exclusive criteria were identified and used to retrieve relevant literature from renowned literature databases. Shortlisted publications were analysed using the VOSviewer software and then critically reviewed to reveal the status quo of research in the subject area. Findings: The review results show that DT has been mainly adopted to support decision-making on conservation approach and method selection, performance monitoring and prediction, maintenance strategies design and development, and energy evaluation and management. Although many researchers attempted to develop DT models for part of a heritage building at component or system level and test the models using real-life cases, their works were constrained by availability of empirical data. Furthermore, data capture approaches, data acquisition methods and modelling with multi-source data are found to be the existing challenges of DT application in HFM. Originality/value: In a broader sense, this study contributes to the field of engineering, construction and architectural management by providing an overview of how DT has been applied to support management activities throughout the building life cycle. For the HFM practice, a DT-cum-heritage building information modelling (HBIM) framework was developed to illustrate how DT can be integrated with HBIM to facilitate future DT application in HFM. The overall implication of this study is that it reveals the potential of heritage DT in facilitating HFM in the urban development context. [ABSTRACT FROM AUTHOR]

This research identifies the urgent need for adaptive and real-time urban weather visualization models to support city planning and decision-making. The proposed solution involves the use of digital twin technology integrated with the Fuzzy Sugeno method to handle dynamic and complex environmental data. The novelty of this approach lies in its ability to provide real-time, adaptive visualizations through a combination of IoT (Internet of Things) data and advanced fuzzy logic processing, enhancing the accuracy and relevance of urban weather monitoring. This method enables the system to process real-time weather data collected from IoT sensors distributed throughout the city, resulting in accurate and relevant visualizations. The weather data, including temperature, humidity, and wind speed, is sent to a data centre where the Fuzzy Sugeno method is used to transform this data into visualizable weather information. The weather visualizations include conditions such as heavy rain, regular rain, drizzle, overcast, cloudy, sunny cloudy, somewhat cloudy, and sunny. The results of this study demonstrate that the system can display weather conditions adaptively and in real-time, significantly contributing to the understanding and management of urban heat islands. In ten experiments with temperature ranges from 21°C to 34°C, humidity from 55% to 87%, and wind speeds from 12 km/h to 32 km/h, the dominant defuzzification value was 0.6, corresponding to sunny cloudy weather conditions. The implementation of this system also shows improvements in the quality and accuracy of weather information presented, thereby aiding in more responsive and sustainable urban planning. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0. [ABSTRACT FROM AUTHOR]

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations. [ABSTRACT FROM AUTHOR]

AbstractVibration analysis remains the most used technique for the maintenance of rotating machines, despite requiring their availability. However, with the emergence of Industry 4.0, digital twins provide the opportunity to generate signals of several operating modes. These signals can be used to diagnose or predict the state of the machine being monitored. The digital twins rely on working on hypotheses in the conception of the numerical model. Rolling bearings are one of the most critical components in rotating machines, due to their high requirements for machine dynamics. In this study, the flexibility of the bearings are integrated into the numerical model of a rolling bearings test bench. To achieve this, a discrete element model and a finite element model is created, which communicate with each other to translate the structure’s dynamics. To convert the numerical model into a digital, it is recalibrated on the basis of the signals measured on the test bench, by minimizing an objective function. Then, to assess the reliability of the data generated by the digital twin, a diagnostic by classification is performed. The data generated by the digital twin are used to train a MSVM classification algorithm. Predictions are made on the experimental data initially acquired, describing the same operating modes as the generated data. The results show an improvement in the classification accuracy with the model integrating flexibility compared to the model existing in the literature. [ABSTRACT FROM AUTHOR]

Introduction: Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensorderived patient data, digital twins offer significant potential for applications in the healthcare sector. Methods: This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. Results: A short case study is presented to demonstrate the viability of the proposed simulation architecture. Discussion: The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making. [ABSTRACT FROM AUTHOR]