DEVELOPMENT OF A DIGITAL TWIN FOR AVIATION RESEARCH

The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.

Technological advancements have led to the transition of manufacturing industries to Smart Manufacturing and Industry 4.0. Promising concepts such as Digital Twin and Digital Thread could help speed up the transition. One benefit of using digital twins is to enable continuity of lifecycle information. However, currently, most digital-twin implementations focus on modeling a particular lifecycle stage of a physical element in “silos”. This makes companies missing out on up to 65% of possible value of digital twin investments. This also results in it challenging to incorporate diverse data streams from different lifecycle stages. Digital thread has been used to represent information flow along the product lifecycle. Using information across product lifecycle stages will facilitate interoperability and reusability of digital twins. Because data from each lifecycle stage could be accessed and managed systematically, this will ensure the value and the credibility of the digital twin. A lot of confusion still remains in industry about what are digital twin and digital thread as well as their relationships. Using lifecycle data from a digital thread for digital twin implementation is complex because of the heterogeneity of standards and technologies involved. In this paper, we provide definitions of both digital twin and digital thread; we highlight the benefit of using them for interoperability and reusability of a digital twin for product lifecycle management and analysis; we propose a methodology for implementing digital twins using lifecycle data supported by a digital thread. Finally, we showcase the proposed methodology by providing an example of integrating digital twins with a digital thread.

<b><sc>Abstract.</sc></b> Cooling tomatoes after harvesting and keeping them refrigerated during their transportation is a key step toward preserving the quality attributes of tomatoes. This is a challenging task to accomplish in Nigeria, mainly because shipments of tomatoes from the tomato-producing states in northern Nigeria to the non-producing states in southern Nigeria were predominately not transported in a refrigerated ambient condition. To minimize postharvest losses of tomatoes during transportation, quantification of changes in quality would be extremely valuable. For this purpose, a digital tomato twin was developed based on mechanistic physics-based modeling. This digital twin simulated the actual thermal profile of tomatoes during transportation, based on sensed ambient environmental conditions. The impacts of the adoption of a low-cost, simple plastic packaging container compared with the traditional container (raffia baskets) on tomato's quality evolution during transportation were analyzed in-silico using the developed digital twin. Generally, the values of colour parameter (a*), firmness (N), and lycopene content (LC, mg/100ml) predicted by the digital twin model for tomatoes transported in raffia baskets were all higher than prediction for plastic container. For example, in shipment #2 the predicted colour parameter (a*) was 46.9 % more than the value predicted for the tomatoes in the plastic container at the end of the transportation. Similarly, lycopene content (LC) increased from 1.14 mg/100ml to 2.58 mg/100ml and 1.80 mg/100ml for raffia baskets and plastic container and firmness (N) decreased by 89% and 58 % in the raffia basket and plastic container respectively.

Digital twin technology has been increasingly applied in healthcare and patient well-being in recent years. This paper provides an overview of the current methods and applications of digital twins in the healthcare field. One such application is digital twins in precision healthcare, where digital twins are used to create patient-specific models to assist in diagnosis and treatment planning. Digital twins are also used in hospital/clinic management, where they help to optimize resource allocation and workflow processes. In response to the COVID-19 pandemic, digital twins have been utilized to detect outbreaks and predict disease spread. In addition, digital twins have been applied in bio-manufacturing and pharmaceutical industry to improve manufacturing processes. Another application area is machine learning and modeling, where digital twins are used in machine learning, data generation, and system modeling for applications in healthcare and disease prediction. Security and ethical issues related to digital twins are also discussed in this paper, as privacy concerns and data protection remain important considerations in the application of digital twin technology in healthcare. Finally, the paper concludes by discussing the future challenges and directions of future work in this field. These include the need to develop more accurate and sophisticated digital twin models, addressing interoperability and integration issues, and further exploring the potential of digital twin technology in emerging areas such as telemedicine and personalized medicine.

We present a structural digital twin system based on component-based parametric reduced order modeling using Reduced Basis Finite Element Analysis (RB-FEA) framework. This enables fast, detailed, and holistic modeling of large-scale structures, such as floating and fixed production and drilling platforms, and crucially, the digital twin's parametric nature and fast solve time enables the system to be connected seamlessly with inspection and sensor data. We demonstrate RB-FEA based structural digital twins, and capabilities for connecting them to sensor data and data science methods to increase predictive power and engineering insight. We show how the digital twin is a key enabler for a Digital Thread – a data-driven architecture that links together information generated from across the product lifecycle. Inspection data, such as thickness measurements, can be directly mapped into the structural digital twin. Sensor data, such as motion and strain can be transformed into loading data, e.g. to estimate sea-states and calculate stress and fatigue accumulation based on actual conditions, or to "tune" model parameters to match data-based measurements, such as mode shapes and frequencies. This enables continuous tracking of the asset and assessment of its current state, based on quantitative and detailed structural analysis, at any time. Ultimately the structural digital twin provides an interpretation layer for integration of monitoring and inspection data, leading to a fully automated digital thread. We present a framework, based on a cloud-based simulation platform and Python-based scripting, to automate the workflows listed above. Digital twin provides a single source of truth about asset condition and allows data driven communication and decision taking between multiple stakeholders – operators, contractors, regulators, etc. We will show examples of real-world applications of RB-FEA digital twin for FPSO and Jacket platforms and development work for digital thread with emphasis on data driven value creation along the entire life cycle of the asset. The Reduced Basis FEA based structural digital twin is a key enabler of data driven digital thread in addition to sensor frameworks and data science systems. RB-FEA provides fast, parametric, and physics-based analysis providing a transparent interpretation layer for data obtained from other systems. It provides a key element allowing knowledge management along asset lifecycle – from concept to decommissioning. High fidelity, physics based global simulations, such as RB-FEA digital twins, are a key enabler for effective digital transformation of industrial operations. They enable real-time insights into asset performance that are otherwise unattainable, providing tools for Asset Integrity Management, Risk Based Inspection, Automation and Optimization of Operations as well as enabling full digital thread enhancing Safety and CAPEX and OPEX.

The construction industry in Sri Lanka is grappling with challenges such as cost, time, and quality issues, which impact project delivery, productivity, and overall performance. The current Quality Management (QM) systems are primarily paper-based, labourintensive, and inefficient, hindering the ability to identify and manage defects. To improve Quality Management performance, fully automated and intelligent systems are required. The application of Digital Twin (DT) can offer real-time insights to enhance Quality Management procedures. However, the Sri Lankan construction industry is lagging in adopting Digital Twin technology due to implementation barriers. This study aimed to explore the applicability of Digital Twin for quality management in the Sri Lankan construction industry. A critical literature review was conducted, involving 15 experts interviewed using semi-structured interviews. The study found that Digital Twin improves the efficiency and effectiveness of the Quality Management process by integrating with quality standards and regulations, improving communication, material quality control, automatic quality checks, transparency, improved quality documentation, stakeholder participation in quality decisions, and prompt resolution of problems. This study identifies barriers to implementing Digital Twin in construction and suggests overcoming them. The results of this study indicate that Digital Twin has a higher potential than the traditional method currently used for Quality Management in the Sri Lankan construction industry.

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

Geometric parameter updating in digital twin of built assets: A systematic literature review

Digital Twins can be considered as a new phase in smart and data-driven greenhouse horticulture. A Digital Twin is a digital equivalent to a real-life object of which it mirrors its behaviour and states over its lifetime in a virtual space. Research indicates that they can substantially enhance productivity and sustainability, and are able to deal with the increasing scarcity of green labour in greenhouse horticulture. This paper presents the results of a systematic literature review on Digital Twin applications in greenhouse horticulture. The review identifies 8 articles that explicitly address Digital Twins in greenhouse horticulture and 115 studies that implicitly apply the Digital Twin concept in smart IoT-based systems. Findings indicate that the concept of the Digital Twin is in a seminal phase in greenhouse horticulture, but there are existing applications that are not yet framed as Digital Twins. In the reviewed papers, there is a dominant focus on the cultivation process at the greenhouse level, among others for climate control, energy management and lighting. About 9% of the articles are virtualizing plants themselves, which indicates that the granularity level addressed is still rather limited. Only 7 % of the articles look beyond plants or single greenhouses. None of the reviewed articles consider the company level. Furthermore, most applications address monitoring and control of the state and behaviour of real-life objects. More advanced applications, including predictive and prescriptive capabilities across the complete lifecycle, are still in an early stage of development, although predictive Digital Twins are gaining prominence.

The article demonstrates the applications, essence and possibilities of digital twin technology and explains the advantages of digital twin technology and potential challenges. A digital twin is a digital copy that can be used to simulate the status of a physical object or system. The integration of digital twin technologies with the Internet of things, Big Data and Artificial intelligence offers innovative solutions for quicker detection of arisen problems (or problems to arise) related to changes in a real physical object and for making relevant decisions. The development of such solutions in the healthcare is crucial for preserving human health and providing people with higher-quality medical care. It is possible to get an early diagnosis of the disease and the selection of a more effective treatment method on the produced digital twin by transferring the patient’s physical characteristics and changes in his/her body to the digital environment. The article analyzes medical digital twins, categorizing their benefits into patient health, cost reduction, self-management, and other benefits. In order to ensure adaptability and effectiveness in disease therapy and healthcare management decision-making, the patientoriented ontology of healthcare is examined, and a four-level ontological model is suggested to create its digital twin. The creation of a patient-oriented digital twin of healthcare requires the creation of a digital twin of existing physical objects at each level of its ontological model. The creation of digital twin in healthcare opens wide opportunities for making decisions on provision of safe and high-quality medical care to patients.

Digital twin modelling for optimizing the material consumption: A case study on sustainability improvement of thermoforming process

Web-based human-robot collaboration digital twin management and control system

Recently, more and more digital twin technologies are getting attention in managing and operating urban metros. The fact that integrating time-monitor data like a metro digital twin right now has chances to improve its operation and dependability. This essay is mostly about the applications of digital twins for the metro system: infrastructure monitoring and operation management. Digital twin, at the infrastructure level, can be utilized in observing the structure inside the metro tunnel to see if there is a dangerous situation. For example, a swelling on Earth. After applying our collected data to a predictive analysis, this will offer us an early warning and allow preventive maintenance. In terms of operation, a digital twin system would help people to monitor the operation of metro network, which means smoother passenger flow. Although the metro system's digital twin technology has great benefits, problems like data integration, reliability of the whole system and its security still need to be solved. More studies and practices need to take place to make sure that digital twin tech is employed safely and correctly on future metros.

ABSTRACTThe digital age has brought about a need for organizations to utilize Digital Twins to improve operational efficiency and decision‐making. However, it is difficult for companies to identify and prioritize Digital Twin initiatives that meet the needs of their stakeholders and align with the capabilities of the company and its strategic plans. This paper proposes a methodology for the systematic identification and prioritization of Digital Twin applications in complex industrial settings. The methodology begins by documenting business requirements, current processes, and challenges, and subsequently identifying areas with potential benefits from Digital Twins through the use of an opportunity scoring system. To refine the portfolio of Digital Twin applications to include only those that are impactful and viable, the feasibility of Digital Twin is quantified by evaluating technological (technical capacity and digital skills), organizational, and project risk factors. To validate the proposed methodology, a case study was conducted in collaboration with an industrial partner specializing in injection molding. This real‐world application demonstrates the effectiveness of our approach in identifying and prioritizing Digital Twin applications in a complex industrial context. This research contributes to the growing body of knowledge surrounding Digital Twins, providing organizations with a structured approach to leverage the potential of this transformative technology.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.