Digital Twins for Biofluids.

Digital Twin is a new concept that consists of creating an up-to-date virtual asset in cyberspace which mimics the original physical asset in most of its aspects, ultimately to monitor, analyze, test, and optimize the physical asset. In this article, we investigate and discuss the use of the digital twin concept of the roads as a step toward realizing the dream of smart cities. To this end, we propose the deployment of a Digital Twin Box to the roads that is composed of a 360° camera and a set of IoT devices connected to a Single Onboard Computer. The Digital Twin Box creates a digital twin of the physical road asset by constantly sending real-time data to the edge/cloud, including the 360° live stream, GPS location, and measurements of the temperature and humidity. This data will be used for realtime monitoring and other purposes by displaying the live stream via head-mounted devices or using a 360° web-based player. Additionally, we perform an object detection process to extract all possible objects from the captured stream. For some specific objects (person and vehicle), an identification module and a tracking module are employed to identify the corresponding objects and keep track of all video frames where these objects appeared. The outcome of the latter step would be of utmost importance to many other services and domains such as national security. To show the viability of the proposed solution, we have implemented and conducted real-world experiments where we focus more on the detection and recognition processes. The achieved results show the effectiveness of the proposed solution in creating a digital twin of the roads, a step forward to enable self-driving vehicles as a crucial component of smart mobility, using the Digital Twin Box.

Reduced-Order Models and Data-Driven Digital Twin Mathematical Representations and Real-Time Optimization Abstract digital twins offer a way of real-time monitoring, control and optimization of a physical asset, hence transforming technologies in cyber-physical systems (CPS). By merging mathematical modelling, reduced-order, and data-driven modelling approaches, this work addresses how CPS digital twins might be more efficient and accurate. Complex physical systems are captured using ROMs, which also help to preserve significant dynamics by lowering computation. Using data gathered on real-world systems, machine-learning and other statistical methods are utilized to enhance these models to maintain the digital twin updated with the actual system. Combined with data-driven approaches, ROM makes real-time optimization conceivable in which parameters of the system are continuously modified to acquire optimal system performance under evolving conditions. For industries including manufacturing, energy, and transportation where real-time judgments are required, this is particularly helpful. The paper offers analysis of how such integrated models developed, their uses in CPS, some of the difficulties these models offer for the future and chances for improvement. These contributions comprise building data-driven physics-based digital twins using libraries of component-based RMIs and leveraging parallel reduced-order modelling for high-performance computing pipelines. Proceeding toward more accurate, scalable, and efficient digital twin to support industry and smart manufacturing.

This chapter presents a Digital Twin Pipeline Framework of the COGNITWIN project that supports Hybrid and Cognitive Digital Twins, through four Big Data and AI pipeline steps adapted for Digital Twins. The pipeline steps are Data Acquisition, Data Representation, AI/Machine learning, and Visualisation and Control. Big Data and AI Technology selections of the Digital Twin system are related to the different technology areas in the BDV Reference Model. A Hybrid Digital Twin is defined as a combination of a data-driven Digital Twin with First-order Physical models. The chapter illustrates the use of a Hybrid Digital Twin approach by describing an application example of Spiral Welded Steel Industrial Machinery maintenance, with a focus on the Digital Twin support for Predictive Maintenance. A further extension is in progress to support Cognitive Digital Twins includes support for learning, understanding, and planning, including the use of domain and human knowledge. By using digital, hybrid, and cognitive twins, the project’s presented pilot aims to reduce energy consumption and average duration of machine downtimes. Data-driven artificial intelligence methods and predictive analytics models that are deployed in the Digital Twin pipeline have been detailed with a focus on decreasing the machinery’s unplanned downtime. We conclude that the presented pipeline can be used for similar cases in the process industry.

Digital Twin technology is an emerging concept that has quickly gained traction in both industry and academia. A digital twin is a virtual representation of a real-world object or system. It is used to evaluate performance, and inefficiencies and design solutions to improve the efficiency of its physical counterpart. The Digital Twin is described as the integration of data between a physical and virtual asset. It is nothing but a replica of the physical object, known as a logical object, which reflects all the major characteristics and properties of the original product. To model a digital twin for a physical object or process, it uses artificial intelligence, the Internet of Things, and other supporting technologies like cloud and analytics. The review of digital twins in the manufacturing, health, and industrial sectors is mainly represented in this paper. It also discusses the significance, characteristics, and fundamentals of creating a digital twin for any physical asset or process.

The time needed to eliminate complications and accidents accounts for 20–25% of total well construction time, according to a 2020 SPE paper (SPE 200740). The same paper notes that digital twins have proven to be a key enabler in improving sustainability during well construction, shrinking the carbon footprint by reducing overall drilling time and encouraging and bringing confidence to contactless advisory and collaboration. The paper also points out the potential application of digital twins to activities such as geothermal drilling. Advanced data analytics and machine learning (ML) potentially can reduce engineering hours up to 70% during field development, according to Boston Consulting Group. Increased field automation, remote operations, sensor costs, digital twins, machine learning, and improved computational speed are responsible. It is no surprise, then, that digital twins are taking on a greater sense of urgency for operators, service companies, and drilling contractors working to improve asset and enterprise safety, productivity, and performance management. For 2021, digital twins appear among the oil and gas industry’s top 10 digital spending priorities. DNV GL said in its Technology Outlook 2030 that this could be the decade when cloud computing and advanced simulation see virtual system testing, virtual/augmented reality, and machine learning progressively merge into full digital twins that combine data analytics, real-time, and near-real-time data for installations, subsurface geology, and reservoirs to bring about significant advancements in upstream asset performance, safety, and profitability. The biggest challenges to these advancements, according to the firm, will be establishing confidence in the data and computational models that a digital twin uses and user organizations’ readiness to work with and evolve alongside the digital twin. JPT looked at publications from inside and outside the upstream industry and at several recent SPE papers to get a snapshot of where the industry stands regarding uptake of digital twins in well construction and how the technology is affecting operations and outcomes. Why Digital Twins Gartner Information defines a digital twin as a digital representation of a real-world entity or system. “The implementation of a digital twin,” Gartner writes, “is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.” Data from multiple digital twins can be aggregated for a composite view across several real-world entities and their related processes. In upstream oil and gas, digital twins focus on the well—and, ultimately, the field—and its lifecycle. Unlike a digital simulation, which produces scenarios based on what could happen in the physical world but whose scenarios may not be actionable, a digital twin represents actual events from the physical world, making it possible to visualize and understand real-life scenarios to make better decisions. Digital well construction twins can pertain to single assets or processes and to the reservoir/subsurface or the surface. Ultimately, when process and asset sub-twins are connected, the result is an integrated digital twin of the entire asset or well. Massive sensor technology and the ability to store and handle huge amounts of data from the asset will enable the full digital twin to age throughout the life-cycle of the asset, along with the asset itself (Fig. 1).

Energy digital Twins in smart manufacturing Systems: A literature review

Digital twins, which are virtual representations of physical systems, have become integral to various industries, offering unprecedented capabilities in real-time monitoring, simulation, and optimization. The rapid advancements in digital twin technology have transformed how industries manage and optimize their physical assets [1]. Digital twins provide a virtual representation of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. However, the widespread adoption of digital twins has introduced new cybersecurity challenges that must be addressed to ensure the integrity and security of these digital counterparts. As digital twin technology becomes more prevalent, it is crucial to proactively address the potential vulnerabilities and security risks associated with these digital representations of physical systems. To fully harness the benefits of digital twins, industries must develop robust cybersecurity strategies to protect against unauthorized access, data breaches, and other malicious attacks that could compromise the integrity of the digital twin and the physical assets they represent.[2]This paper aims to explore the multifaceted cybersecurity risks associated with digital twins, including data integrity and confidentiality issues, expanded attack surfaces, and vulnerabilities in communication protocols. Additionally, it will propose comprehensive solutions and best practices to enhance the cybersecurity of digital twins, encompassing secure data management, access control, and resilient system design. Through a comprehensive review of current cybersecurity solutions, such as zero-trust architectures, blockchain technology, and AI-driven threat detection systems, this paper highlights the effectiveness and limitations of existing approaches. Furthermore, it identifies key challenges in implementing robust cybersecurity measures, such as scalability, integration with existing frameworks, and regulatory compliance.[3]The paper concludes by proposing future research directions to enhance the security of digital twins, emphasizing the need for standardized security protocols, advanced real-time threat detection capabilities, and collaborative efforts among stakeholders. By addressing these challenges, this research aims to contribute to the development of more secure and resilient digital twin environments, ultimately ensuring their safe and effective deployment across various sectors.

In this paper, we propose an open source-based digital twin broker interface that can realize interaction through data and event exchange between real and virtual assets. Online simulation of interactions between existing and new assets through digital twins is very useful for introducing new assets or processes into a smart factory environment where existing assets are already built. The proposed digital twin-based interaction broker is implemented based on the Eclipse Ditto open-source platform as an interface for exchanging status data and control events between real and virtual assets. In order to verify the performance of the broker interface, we built a digital twin for an indoor cooperative logistic testbed system consisting of real assets such as two AMRs, a charging station, and a conveyor. And to interact with them, we implemented a digital twin simulation module consisting of a virtual crane and forklift. Through the proposed broker interface, an experiment was conducted to create a situation in which real AMRs recognize and avoid when routes were interrupted by virtual crane and forklift within a common work area. As a result, we confirmed that the proposed broker interface supports smooth interaction between two digital twins.

The Iliad Digital Twins of the Ocean project [1] is a large (55 partners) European Green Deal Project which aims at the development of an architecture and set of components, tools and services for the creation of digital twins of the ocean. The approach aims to support the emerging European Digital Twins of the Ocean (EU DTO) initative including interoperability with associated projects like EDITO Infra and EDITO Model lab and the overall Destination Earth (DestinE) initiative and also taking advantage of the evolving European Common Data Spaces including the Green Deal Data Space, the Copernicus Data Space and the EOSC cross domain Data Space. The approach of Iliad digital twin interoperability architecture based on four steps of a digital twin pipeline.The four digital twin pipeline steps are: Digital Twin Data Acquisition/Collection, Digital Twin Data Representation, Digital Twin Hybrid and Cognitive/AI Analytics Models and Digital Twin Visualisation and Control. The Iliad project has idenified these four steps as main architectural pipeline areas from an interoperability perspective, as described in the following. The architecture is system of systems based and the figure also shows the existence of potential multiple digital twins interactions.The first Digital Twin step focuses on Data acquisition and collection from various sources including collection of realtime see\nsor data, for input to the Digital Twin. This is supported by various Data Spaces and also through a direct Stream Handler. This includes both streaming data and data extraction from relevant external data sources and sensors. It includes support for handling all relevant data types and also relevant data protection handling for this step. In the Digital Twin sensor context this includes the full Observation Pyramid from remote sensing through airborne sensors to surface and subsea sensors and in-situ and IoT sensors.The second Digital Twin step focuses on Digital Twin Data Representation. The data availability for the digital twins is supported by various Digital Twin Data Lakes – connected to Data Spaces and also potentially directly to streaming observations from the previous step.The third Digital Twin step focuses on Digital Twin Hybrid and Cognitive/AI Analytics Models. The processing execution for the models is supported by various Digital Twin Engines. The fourth Digital Twin step focuses on Digital Twin Visualisation and Control. This is being supported by various types of 2D/3D/4D visualisations, and immersive visualisations and further evolutions towards the GeoVerse perspective on MetaVerse.The Iliad project is providing a framework with tools and services for these four digital twin pipeline steps aiming at technical and semantic interoperability with, and portability to, the EU DTO ecosystem of digital twins of the ocean.[1] Iliad – Digital Twins of the Ocean project, https://ocean-twin.eu/

The integration of Digital Twin (DT) technology with Artificial Intelligence (AI) has shown significant promise in enhancing the security and operational efficiency of Internet of Things (IoT) networks. This paper proposes a Hybrid Digital Twin solution designed for real-time threat prevention in AI-driven IoT networks. By leveraging AI-driven decision-making processes, coupled with the real-time simulation and monitoring capabilities of Digital Twins, the proposed framework continuously analyzes IoT network behaviors and predicts potential security threats. The hybrid approach combines machine learning (ML) models with Digital Twin simulations to predict network vulnerabilities and detect anomalous behaviors at an early stage, thereby preventing security breaches before they impact the system. The architecture includes a real-time monitoring system for both physical and virtual assets, providing insights into the IoT network's current state and enabling proactive threat mitigation. Experimental results demonstrate a 15% reduction in false-positive threat detection, a 20% improvement in response time to potential threats, and a 17% increase in overall network efficiency when compared to conventional threat prevention methods. The proposed framework integrates the following components, Real-time data acquisition from IoT devices and systems, AI-based anomaly detection algorithms for threat identification, Digital Twin simulation models for continuous network status monitoring and predictive analytics. Automated response mechanisms based on AI predictions and Digital Twin assessments. The effectiveness of the proposed solution is validated through case studies and performance evaluations, highlighting its ability to enhance the security, reliability, and efficiency of AI-driven IoT networks. Future work will focus on improving the scalability of the solution, optimizing resource allocation, and extending its application to more diverse IoT environments.

Integration of digital twins and physical AI in cyber-physical systems

Using NFTs for ownership management of digital twins and for proof of delivery of their physical assets

Sustained continuous monitoring and replication of organizational development in digital organizational twins is of particular importance for labour-intensive enterprises and also those in which reciprocal relations between social, corporate, normative and performative aspects assume the leading role. The main purpose of the research is the developing of a digital representation of organizational processes, which focuses on the performance, working activities, organizational issues, behaviour and interactions between of the organizational members. Consequently, the objectives of research include the monitoring of current research state, concept and design of a digital twin. The implementation of digital organizational twin should improve considering timely optimization of proactive and reactive organizational development measures in the company in relation to the core variables of the 7S model. The created digital twin should map the dynamics of organizational development, as well as concomitant and deviating processes. Systematization literary sources and approaches for the digital replication of organizational development issues indicates the lack of publications on research and diffuse distribution of scientific interest. The initial design of organizational development in the digital twin is based on four main objects and limited to a certain number of investigated parameters. This paper compare the conventional and digitalized organizational development process, explain the data flow in digital organizational twin, the design of organizational development in the digital organizational twin, provide an overview of the individual facets of organizational development, list the parameterization models and exemplarily illustrate the visualization of selected parameters. The results of the research can be useful for the expansion of the tension bridge between organisational development and technologies and the development of new potentials for the study of socio-technical effects in companies. This can be extended to include the other facets of business management and supplemented by the connection of other technological resources.

Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

Future internet of things (IoT) services necessitate the integration of sensing and communication functions within the same system, utilizing digital twin (DT) technology. Integrated sensing and communication (ISAC) can address the need for widespread communication and high-precision sensing by leveraging the benefits of spectrum and hardware resource sharing. The DT represents a promising technology for achieving low latency and high energy efficiency, leveraging real-time monitoring, optimization, and predictive maintenance capabilities. Recent advancements in DT and ISAC for IoT systems research necessitate the establishment of effective communication technology methods between the physical entity and its digital representation. Most prior research reviews have not sufficiently explored the critical enabling technologies for DT and ISAC applications in IoT systems that facilitate communication between a physical entity and its digital counterparts. Previous research has primarily focused on the application of DT technology in IoT systems; however, little emphasis has been placed on the integration of ISAC technology with DT in IoT systems, as well as the important integrated sensing and communication technologies between physical entities and their digital counterparts. This paper presents a systematic literature review that focuses on the analysis of key communication technologies that facilitate connections in DT and ISAC for IoT systems. The implications of communication methods derived from the study are presented, focusing on technologies such as unmanned aerial vehicles (UAVs), reconfigurable intelligent surfaces (RISs), millimeter wave (mmWave), and massive MIMO (mMIMO) technologies. The emphasis on these technologies is due to their significance as essential enablers of integrated sensing and communication between physical entities and their digital counterparts. Furthermore, these technologies are critical for integrating DT and ISAC into the IoT systems to efficiently meet the needs of IoT services. Future discussions are finally addressing open research and the challenges that demand attention.