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The digital twin paradigm for future NASA and U.S. air force vehicles
Abstract
Future generations of NASA and U.S. Air Force vehicles will require lighter mass while being subjected to higher loads and more extreme service conditions over longer time periods than the present generation. Current approaches for certification, fleet management and sustainment are largely based on statistical distributions of material properties, heuristic design philosophies, physical testing and assumed similitude between testing and operational conditions and will likely be unable to address these extreme requirements. To address the shortcomings of conventional approaches, a fundamental paradigm shift is needed. This paradigm shift, the Digital Twin, integrates ultra-high fidelity simulation with the vehicle's on-board integrated vehicle health management system, maintenance history and all available historical and fleet data to mirror the life of its flying twin and enable unprecedented levels of safety and reliability.
... The initial terminological articulation of the subject domain was rendered by Grieves [7] in 2005, coining the term Mirrored Space Model (MSM), subsequently followed by NASA in 2010, which introduced the term DT and provided its inaugural definition [7,8]. The trajectory of scientific development has transcended the confines of the aerospace sector, aspiring to cultivate an intelligent manufacturing environment, now encompassing an array of new technologies, applications, and methodologies in machine learning. ...
... The trajectory of scientific development has transcended the confines of the aerospace sector, aspiring to cultivate an intelligent manufacturing environment, now encompassing an array of new technologies, applications, and methodologies in machine learning. Within the corpus of literature, the functions of DT in the context of the cyber-physical system, along with its multifarious applications, have been categorically distilled into three primary domains: monitoring, life cycle analysis, and decision-making [7,8]. ...
The burgeoning importance of digitalization and cyber-physical manufacturing systems in the industrial sector is undeniable, yet discussions around viable solutions for small and medium-sized enterprises remain scant. These enterprises often face constraints in replacing extant machinery or implementing extensive IT upgrades, despite the availability of skilled engineering personnel. In response to this gap, an illustrative use case involving the application of Digital Twins (DT) to legacy systems is delineated, encompassing a detailed exploration of necessary hardware and software components, alongside pertinent considerations for implementation design. The establishment of a symbiotic relationship between the physical and digital realms is underscored as imperative, necessitating a granular understanding of the system to uncover opportunities and constraints for intervention. Such understanding is posited as a critical determinant of the DT's utility. This case study, situated within the Cyber-Physical Manufacturing Systems Laboratory at Széchenyi István University, serves to elucidate these principles and contribute to the discourse on smart manufacturing solutions for legacy systems.
... Digital Twins (DT) represent virtual models of their physical counterparts, facilitating remote in-situ diagnosis of assets and prognostication of remaining useful life in a rapid and reliable fashion [1]. They have found wide applications in the aviation, healthcare and manufacturing sectors in recent times [2][3][4][5]. Opportunities for DTs are also abound in the civil engineering industry for digital twinning of floating platforms, bridges and wind turbine support structures, which commonly deploy welded circular hollow sections (CHS) joints as local structural connections [6,7]. ...
... In aeronautical and mechanical structures, cyclic loading can result in the initiation and growth of cracks in critical components, leading to structural failure and posing a threat to structural integrity. To address this issue, the digital twin [1], developed from individual aircraft tracking, enables structural damage diagnosis and prognosis by creating a multiphysics, multiscale, and probabilistic virtual model of an as-built system [2] that can integrate multiple heterogeneous and uncertain sources of information from models and data to support decisions for proactive fleet maintenance [3][4][5]. ...
Digital-twin-based structural diagnosis and prognosis are growing topics that have an important role in improving in-service safety and the economy. However, current research focuses primarily on individual structures using Bayesian-based updating approaches, leaving little attention to the multiple similar structures at the fleet level. Given the nonlinear and non-Gaussian nature of the structural damage evolution, direct modeling of multiple structures would require a larger number of particles. The study presents a novel copula-based approach for efficiently modeling multi-structure damage diagnosis and prognosis in a fleet. The proposed approach leverages the particle filter to model the damage growth in each structure and utilizes the copula function to capture the relationship between structures as the joint probability distribution. The relevant parameters in the copula function are estimated using the maximum mean discrepancy metric based on the similarity of the predicted damage state, and structural parameters. Once an observation is available for a structure, the damage states of the structure and other structures in the fleet are updated using the approximate copula-based joint distribution. The results from hypothetical datasets demonstrate that the proposed approach improves prediction accuracy compared to traditional individual-based methods and effectively controls uncertainties for each structure, even during intervals of no observations. This approach holds promise for integration into the fleet maintenance digital twin.
... According to NASA's definition, a digital twin is "an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin. The digital twin is ultra-realistic and may consider one or more important and interdependent vehicle systems" [28]. ...
This paper presents a systematic literature review on the application of digital twins in the energy sector. Initially, we generated an overview through a survey of prior reviews, independent of market vertical, then followed by a more detailed review concentrating on the power production and distribution domains, as per the NIST (National Institute of Standards and Technology) smart grid standard. We implemented a rigorous method, which included seven stages, beginning with the collection of 2238 articles. We observed that the energy sector range was too broad and filtered by generation and distribution during the practical screening, resulting in 275 for further screening. This amount was then condensed to 81 papers that matched the quality screening criteria for synthesis and examination. In summary, digital twin architectures and frameworks include five components: the physical entity, bidirectional communication, the virtual entity (with modeling and simulation), data management, and services. Our study contributed by determining that distribution management is the most pertinent application of digital twins in the distribution domain and fault diagnosis in the generation domain. Furthermore, we found that digital twins involve multiple stakeholders whose role is rarely discussed in studies, and we identified a similar absence of emphasis for security. Research on security often presents the digital twin as an additional layer of protection, yet rarely investigates the security of the digital twin by itself. The potential limitations of our study to answer some of the technical research questions may be because of the criteria for the selection of papers. However, as the emphasis of this study is on the energy sector, it enabled domain-specific findings for generation and distribution.
... Digital Twin (DT) was first defined in 2002 as a digital informational of a physical system by creating a mirror entity of its own and linked with the physical asset for Product Lifecycle Management (PLM) whereby it is logically centralized information of the product throughout its lifecycle. Later, the Digital Twin come into a concrete term appeared at DARPA's Defense Sciences Office (DSO) for an aerospace industry in 2010 [6]. A DT is a model of virtual model with an advanced version of simulation, which is a replication of a physical system or process [7]. ...
Digital Twin (DT) is a virtual representation that is parameterized based on the real process data to model, simulate, monitor, analyze, and optimize the physical systems they represent. DT have been predominantly used in the mechanical engineering field and have yet to be extensively used in chemical flow processes particularly for the challenge of scale-up which is very important particularly when moving from lab experiment to industrial scales. It is a challenge to maintain various process parameters while increasing the scale of reactors geometry. The parameters might not show a predictable linear co-relationship due to concurrent chemical conversion processes behaving differently on different scale. We apply and compare various machine learning methodologies such as Radial Basis Function Neural Networks, Gaussian Process Regression and Polynomial Regression to the development of chemical flow process DT for scale-up. We show that these methodologies can be used to predict the product yield of a chemical flow process during scale-up.
... DT was first proposed by Grieves as 'the digital equivalent to a physical product' in the lecture on life cycle at The University of Michigan in 2003 (Grieves, 2014). Later, Stargel and Glaessgen from NASA specified the definition of DT as 'an integrated multiphysics, multiscale, probabilistic simulation of a vehicle or system to mirror the lifecycle of its flying twin' (Glaessgen & Stargel, 2012). This detailed definition triggered research on DT in aerospace engineering and rapidly penetrated other fields, such as industrial manufacturing, civil construction, and medical care (Liu et al., 2021a). ...
The pervasive smart manufacturing is bringing increasing attention to digital twin. As a core part of virtual modeling, 3D virtual modeling is crucial to improve the intuitiveness of state monitoring, enhance human-cyber interactions, visualize conditions and simulations, and provide visual guides in digital twin. However, although 3D virtual modeling in digital twin has many benefits for different applications, its complex characteristics become obstacles for novice engineers to develop and utilize. Besides, research information about 3D virtual modeling in digital twin is too scattered, while there has been no literature review that specially and comprehensively summarizes and analyzes it. To help novice engineers understand and scheme 3D virtual modeling in digital twin for future research and applications, this paper reviews 106 digital twin 3D modeling cases with their characteristics, including deployment targets, purposes & roles, collaborative models, data flows, the autonomy of 3D modeling, fidelity, twinning rates, enabling technologies, and enabling tools. This paper then discusses and analyzes the review outcomes via statistics. Finally, this paper also proposes a thinking map for scheming the 3D virtual modeling in digital twin. In general, 3D virtual modeling is oriented by the motivation behind different digital twins, engineers hence should reflect on the purposes, scenarios, resources, and long-term visions of their projects. When designing characteristics of 3D virtual modeling, engineers must consider functions, capabilities of data processing and transmission, timeliness of data, applicability, and specialty of each characteristic. For future work, this paper highlights three important research issues to realize the prospect of 3D virtual modeling, including the versatility of autonomous 3D modeling, incremental updates of 3D models, and optimal planning of data collections for 3D modeling. Besides, future work will also investigate the enhancement of 3D virtual modeling via relevant information technologies, such as IoT-based data collections, machine vision-based data processing, and adaptive machine learning-based dynamic modeling.
... Optimisation/DSE: Co-simulations are run as part of an optimisation loop, for example, in a Design Space Exploration (DSE) approach. This includes decision support systems, used, for example, in a digital twin (Glaessgen and Stargel, 2012) setting, where a modelled system is updated based on the operating system. Some of the specific requirements include: the ability to define co-simulation stop conditions, the ability to compute sensitivity, high performance, fully automated configuration, faster than real-time computation. ...
... История появления ЦД ведется с 2010 года, когда НАСА сделала попытку улучшить моделирование космических аппаратов [1]. Потребность интеграции человеческого суждения в формальный процесс моделирования была описана впервые в работе Джозефа Фикселя на основе теории нечетких множеств [2]. ...
The paper presents a method of developing digital twins (DTs) by creating special cognitive shell- metamodels that allow the creation, correction, scaling and implementation of arbitrary complexity of DTs of productions and their products. The method of cognitive matamodeling of DTs will allow to integrate the main properties of the software of DTs: 3D-modeling, accounting of functioning and dynamics of changes, management of configurations of DTs, monitoring of communication relations, calculation of mass and inertial characteristics, storage and visualization of chronicles of all operational and dynamic activities. This approach will add flexibility to the chain of interaction: human- DTs- communication channel- physical object, which in turn will eliminate the gap between the stages of development and implementation, as well as the scaling of the project. The use of metamodels can bring all parties involved in development and operation closer together in terms of time and location.
... A DT aims to establish a mirrored connection between the physical and virtual realms, mapping sensor-measured data onto the virtual model. NASA's 2010 technology roadmap draft outlined the utilization of DTs as physical models, updated through sensor feedback to reflect vehicle conditions [15]. Tao et al. propose a DT to be fivedimensional: Physical, Virtual, Connection, Data, and Service. ...
The rapid adoption of automation in the Cyber-Physical Systems (CPS) is triggering Industry 4.0 (I4.0), integrating cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity into traditionally isolated systems. These I4.0 changes are optimizing the performance of Smart Manufacturing (SM) systems at the cost of increased complexity, exposing I4.0 systems to more cyberattacks than ever before. To address these challenges, this work presents DT4I4-Secure: A Digital Twin Framework for Industry 4.0 Security. The DT4I4-Secure presents a framework to create Digital Twins (DT) for I4.0 systems using a combination of models (including physics and data-based models). This paper showcases the use of the DT framework to detect attacks on I4.0 systems by comparing observations with future predictions from the DT. This paper evaluates the performance of the DT4I4-Secure for a Computer Numerical Control (CNC) turning process manufacturing a metallic spool, wherein the experimental results show the model can predict normal operations with a mean absolute error (MAE) of 0.005081. This work also explores using an Exponentially Weighted Moving Average (EWMA) based dynamic threshold instead of a traditional static threshold for attack detection when the CNC turning process is under three separate attack scenarios. The DT4I4-Secure combined with the dynamic threshold shows a 3.46 times improvement in F 1 -Scores over all three attack scenarios for instantaneous attack detection while having 100% accuracy during the manufacturing cycle.
... It involves the integration of models, algorithms, and feedback mechanisms to achieve a high degree of synchronization between the physical and virtual realms. Authors of [6] provided a widely recognized definition of Digital Twin, describing it as a probabilistic simulation of a vehicle or system that combines physical models, sensor data, fleet history, and other factors to replicate the behavior of its real-world counterpart. A Digital Twin is not limited to early-stage planning or simulation; it also facilitates realtime monitoring, control, diagnostics, and prognostics during the system's operation. ...
GPUs and programmable data planes have gone through an enormous evolution in the past years. GPUs can be used for modeling the real-world environment accurately, while programmable data planes can monitor the network in real-time and implement novel packet processing and decision logic. In this paper, we investigate how these two distant technologies can be combined to implement efficient coordinated multi-point (CoMP) transmission in an indoor environment. The proposed system implements the concept of dynamic on-off switching (DOOS) among various radio transmitters, relying on two information sources: 1) radio propagation digital twin based on real-time simulations of radio channels in the accurate 3D digital representation of the real industrial environment and 2) traffic load collected by the data plane for each transmitter. The proposed DOOS method implemented as a data plane algorithm dynamically selects a set of radio transmitters for each receiver by mixing information provided by the digital twin and the in-network traffic load measurements. The proposed method has low computational complexity and reduces the number of actively used radio transmitters. The proof-of-concept implementation of the proposed system has been validated in simulations as well as with measurements. The method achieves 69% energy saving for the radio compared to the default CoMP transmission and reception.
... It is generally described as a comprehensive, all-encompassing simulation of a system, like a machine, that incorporates various physics, scales, and probabilities. This simulation mirrors the entire lifecycle of its twin by leveraging the most accurate physical model, real-time sensor data, historical records, and other relevant information (Glaessgen & Stargel, 2012). A DT also functions as a virtual image that defines the comprehensive physical and functional characteristics of the entire product life cycle, and it can transmit and receive product information. ...
The rapid adoption of digital technologies has revolutionized business operations and introduced emerging concepts such as Digital Twin (DT) technology, which has the potential to predict system responses before they occur, making it an attractive option for smart and sustainable tourism. However, implementing DT software systems poses significant challenges, including compliance with regulations and effective communication among stakeholders, and concerns surrounding security, privacy, and trust with the use of big data. To address these challenges, this paper proposes a documentation framework for architectural decisions (DFAD) that applies the concept of big data governance to the digital system. The framework aims to ensure accountability, transparency, and trustworthiness while adhering to rules and regulations. To demonstrate its applicability, a case study and three case scenarios on the potential use of Mobile Positioning Data (MPD) in Indonesia for DT technology in smart and sustainable tourism were examined. The paper highlights the benefits of DFAD in shaping stakeholder communication and human–machine interactions while leveraging the potential of MPD to measure tourism statistics by Statistics Indonesia since 2016. Not only the documentation framework promotes compliance with regulations, but it also facilitates effective communication among stakeholders and enhances trust and transparency in the use of big data in DT technology for smart and sustainable tourism. This paper emphasizes the importance of effective big data governance and its potential to promote sustainable tourism practices. The multidisciplinarity approach on political science, software engineering, tourism, and official statistics provides an opportunity for academic contribution and decision-making processes.
... Due to the limitation of software and hardware technology, the historical background of the digital twin is relatively short. So far, its development period may be divided into three epochs: formation stage, incubation stage and growth stage [21,29,30]. ...
... Digital Twin (DT) was first defined in 2002 as a digital informational of a physical system by creating a mirror entity of its own and linked with the physical asset for Product Lifecycle Management (PLM) whereby it is logically centralized information of the product throughout its lifecycle. Later, the Digital Twin come into a concrete term appeared at DARPA's Defense Sciences Office (DSO) for an aerospace industry in 2010 [6]. A DT is a model of virtual model with an advanced version of simulation, which is a replication of a physical system or process [7]. ...
Digital Twin (DT) is a virtual representation that is parameterized based on the real process data to model, simulate, monitor, analyze, and optimize the physical systems they represent. DTs have been predominantly used in the mechanical engineering field and have yet to be extensively used in chemical flow processes particularly for the challenge of scale-up which is very important particularly when moving from lab experiment to industrial scales. It is a challenge to maintain various process parameters while increasing the scale of reactors geometry. The parameters might not show a predictable linear co-relationship due to concurrent chemical conversion processes behaving differently on different scale. We apply and compare various machine learning methodologies such as Radial Basis Function Neural Networks, Gaussian Process Regression and Polynomial Regression to the development of chemical flow process DTs for scale-up. We show that these methodologies can be used to predict the product yield of a chemical flow process during scale-up.
... The concept of digital twin was first introduced by Grieves [12]. NASA then released a white paper on digital twin [13], Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
As a technology for the interaction and integration of products and simulation models, the digital twin can achieve accurate prediction and evaluation of product performance. However, the accurate model base is computationally complex, has a long iteration time, and is unable to perceive changes in the operating state in time. This leads to poor adaptability of the model and low efficiency of performance evaluation. The surrogate model can simplify the above model and improve computational efficiency. Based on this, this paper proposes a digital twin modelling and updating approach. The surrogate model is applied to the digital twin modelling process, which can accurately describe the physical mechanism and achieve interaction with the physical world. Then, this paper defines the consistency metric function, which achieves the rapid perception of the operation state and follows the physical world. Meanwhile, an improved LHS-Adam model update algorithm is used to adaptively update the model structure, improving the efficiency of the model parameters adjustment. Finally, experiments are conducted on the bogie suspension system to verify the feasibility and effectiveness of the update method in practical applications. The experimental results show that the established digital twin model has good updating performance and more efficient performance evaluation capability.
... A key enabler of smart manufacturing is the digital twin (DT), a virtual representation or mirror of a physical object that provides valuable information in a consistent format [1]. The concept of DT can be traced back to 2003 when Michael Grieves first proposed it, and NASA was one of the early adopters, using it to monitor the flight characteristics of a "flying twin" in the form of a rocket [2]. DTs have since found applications in various fields, including manufacturing [3], smart homes [4], greenhouses [5], mine ventilation [6], nuclear reactors [7], and virtual PLCs [8]. ...
The potential of digital twin (DT) technology to revolutionize industry by enabling virtual simulations of physical systems in real-time has garnered significant attention in recent years. DTs have been widely applied in the manufacturing field to solve various problems, such as shopfloor resource optimization, layout design, commissioning, monitoring, and supervisory control. Cloud-based DT (CBDT) is an emerging concept and shows promise in achieving enhanced remote accessibility, data processing and analysis capabilities, and scalability. However, current CBDT research is still very limited and mainly focuses on theoretical framework that leverages cloud computing advantages in data processing aspects. Yet, practical implementation with technical details for creating a CBDT of a complex manufacturing system is rarely reported, and the interactions between cloud infrastructure and DT modeling and visualization are scarcely investigated. To fill the gaps, this paper first proposes a general CBDT framework for supporting smart manufacturing services. This framework features the integration of modularized cloud intelligence, DT modeling, and DT visualization to achieve enhanced remote accessibility. Moreover, a prototyping system that entails the CBDT-enabled remote monitoring and control services is implemented for a legacy robotic assembly system to partially showcase the process of the proposed framework. The usefulness and remote accessibility of the developed CBDT-based prototype system is further demonstrated with web-based functionalities such as assembly job status update, real-time 3-dimensional DT visualization and simulation of assembly tasks, and remote feedback control over the physical system. Lastly, the prototype system is built upon open-source toolkits (e.g., WebGL) and low-cost commercial software platforms (e.g., Unity and Google Cloud Platform), which could potentially open new opportunities for aiding small-to-medium companies for digital transformation. Future works and limitations are also discussed in the end.
... This new emerging and fast-growing system connects the physical and virtual worlds and can be used to increase a product's adaptability to changing requirements. 19,20 In product architectures the modules are defined to maximize module independency. 21 The information on functional and/or physical interrelationships between components are mainly utilized to identify modules and evaluate the degree of modules' independency. ...
To meet the various demands for bus platforms tailored for the emerging new power sources, an adaptive design method is proposed for a bus chassis development using a design structure matrix. The structural feature parameters of the product modules are extracted and the corresponding design structure matrix is established to analyze structural correlations between modules. An adaptive design method process model is constructed following quantification of the design structure matrix. A digital matrix of the module interface is then built to characterize integration and interchangeability of functional modules. Reconfiguration of modules is conducted using the parent-child relationship premise so that the interface matrixes are well integrated. The adaptability of these products is subsequently improved by sharing dominant modules and replacement of local modules. Finally, the implementation of the proposed methodology is illustrated for a current bus chassis.
... There are some distinctions in how DT is interpreted [12,13]. For instance, NASA claimed that DT was a simulation of a vehicle or a system that uses a model of the physical system and sensors to gather data and depict a flying object [14,15]. According to Grieves and Vickers, DT includes both real and virtual space as well as data and information that connected the two. ...
Manufacturing and industrial engineering education has encountered several challenges in addressing the educational demands of rapidly advancing technologies such as Industry 4.0, virtual reality (VR), digital twin (DT), etc. On the one hand, conventional laboratory facilities are unable to cope with this demand due to their nature of isolated operations. On the other, there is also a need to expose the students to concepts like Industry 4.0 and DT, where a virtual replica of the physical system is created and connected in real time. Therefore, this study aimed to establish DT connectivity between the physical and digital models of a conveyor available in a manufacturing system in a learning factory (LF). The integration of existing hardware with a simulated platform, utilizing an IoT-enabled environment and operating the hardware through VR setups, enabled seamless interaction between the physical and virtual realms, enhancing control, visualization, and optimization of hardware systems. Moreover, in addition to facilitating the DT, a VR platform has been integrated with the digital model of the conveyor system, enabling control of the physical system through a VR setup. The positioning accuracy of the conveyor carried pallet was tested for several locations, and results revealed that the digital model indicates the pallet position to the accuracy level of the nearest 2.5 mm. Furthermore, it could be concluded that the setup developed in this research could be used as an LF for engineering undergraduates to learn new concepts such as Industry 4.0, DT, and VR-enabled digital-physical integration.
A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The paper focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This paper proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the trade-off between update overhead and simulation reliability.
The desire for faster data speeds and increased Energy Efficiency has prompted the development of femtocells, which are short-range, low-cost, customer cellular access points. However, in a situation of Distributed Denial of Service (DDoS) which is caused by inefficient energy, distributed attack sources could be employed to amplify the assault and increase the attack's impact. By flooding the network with packets and creating malicious traffic, Distributed Denial of Service (DDoS) attacks try to deplete the network's communication and processing capability. A DDoS assault must be identified and neutralized quickly before a valid user can reach the attacker's target for 5G network to have an effective Energy Efficient service. For the next Fifth Generation (5G) Wireless Network, there is a pressing need to build an effective Energy Efficient mobile network solution. Despite their evident promise in assisting the development and deployment of the complicated 5G environment. The physical product, the digital product, and the relationship between both the physical and virtual goods are said to make up Digital Twin (DT). On the other hand, DT allows real-time communication with both the physical twins. The synergy of energy efficiency and security improvements in this research contributes to a more holistic optimization of 5G networks. This approach seeks to minimize energy consumption while fortifying the network against evolving security threats. Integrating energy-efficient practices with robust security measures enhances the overall resilience and sustainability of 5G systems. This is crucial for ensuring continuous, reliable, and secure communication in the face of dynamic challenges.
Digital Twins possess the capability to create virtual representations of a device’s components and dynamics. They transcend static images or blueprints, offering intricate models that reveal the entire lifecycle of system design, construction, and operation. Digital Twins now spearhead the virtual revolution, equipped to faithfully replicate each component through sensor-driven data collection. This replication aids in informed decision-making, monitoring complex systems, and validating novel products and services. Numerous companies already leverage digital twins within these domains to detect issues and enhance productivity. Conversely, accurate data collection and analysis from digital twins can pose challenges, potentially introducing ambiguity in decision-making and complicating object lifecycle management. Consequently, ongoing debates and discussions revolve around the fundamental concepts, frameworks, and technologies of digital twins. In this work, we delve into the realm of Industrial Applications of Digital Twins, exploring their merits and limitations.
The concept of digital twins in bridge engineering is still vague and even confused with the Bridge Information Model (BrIM). Therefore, this study provides a detailed review of 42 papers related to digital twins in bridge engineering, focusing on a proper definition, key features and creation techniques for bridge digital twin (BDT). The paper also compares BDT and BrIM from the perspectives of their elements, features, fidelity, services provided, and degree of development. The applications of BDT at different life cycle stages are identified, and the related technologies are analyzed in detail. The results show that the research clusters of BDT are divided into geometric model generation, finite element model updating, and management and are focused on the operation and maintenance phase while lacking attention in the design and construction phase. Besides, a reference framework of BDT based on the life cycle of bridges is proposed, and directions for future research are suggested.
Digital Twins are becoming fundamental tools to monitor the status of entities, predict their future evolutions, and simulate alternative scenarios to understand the impact of possible changes. More recently, Digital Twin solutions have been applied in the context of Smart Cities. Thanks to the large deployment of sensors, together with the increasing amount of information available for municipalities and government organizations, it is possible to build wide virtual reproductions of urban environments including structural data and real-time information that can undoubtfully help city councils and decision makers to face future challenges in the urban development and improve the citizen quality of life. In this paper, the Snap4City Smart City Digital Twin framework is presented, which is capable to respond to requirements identified in recent literature and by international forums. The proposed architecture provides an integrated solution for data gathering, indexing, computing, and information distribution, therefore realizing a continuously updated digital twin of the urban environment at global and local scales. It addresses 3D building models, road networks, Internet of Things entities, point of interests, paths, as well as results from analytical processes for traffic density reconstruction, pollutant dispersion, predictions, and what-if analysis with dynamic and constrained routing, all integrated into a freely accessible interactive 3D web interface, enabling stakeholder and citizens participation to the city decision processes. As case of study, the digital twin of the city of Florence (Italy) is presented. The solution is released on top of the Snap4City platform as open-source and made available through our GitHub repository (https://github.com/disit) and as Docker compose.
The integration of AI technology with digital transformation has profoundly shaped the evolution towards digital
triplet architecture, grounded in human-centric methodologies. By infusing human intellectual activities into both physical and cyberspace, innovative links between humans and machines are established. Despite limitations in transitioning from tangible human presence to the digital realm in cyberspace, extensive efforts are underway to harness emotional, visual, and oral responses, thereby enhancing the reasoning and predictive
capabilities of digital twins. These advancements aim to elevate real-time human interactions with physical and
virtual systems by integrating intelligent AI algorithms and cognitive computing systems into digital twins. This
paper meticulously analyses recent trends in digital twins, tracing their evolution from traditional concepts and
applications to a nuanced digital triplet hierarchy that incorporates human intuition, knowledge, and creativity
within cyberspace. we delve into the hierarchical framework of the digital triplet, resonating with maturity,
domination, and volition levels, enhances cognitive and perceptual capabilities in cyberspace. The study provides
a systematic overview of the development of ultra-realistic digital models, incorporating real-time data-driven
artefacts that integrate intelligent activities with multidomain, multiphysics, and multiscale simulations. The
research scope is focused on augmenting the perceptive and heuristic capabilities of the digital triplet framework
by utilizing AI in data analytics, retrieving heterogeneous data from virtual entities using semantic artificial
intelligence technologies, and amalgamating AI and machine learning with human insight and perceptual
knowledge. The proposed digital triplet hierarchy aims to enhance cyberspace’s capacity for learning, cognitive
skills, and knowledge transfer. It can be a guideline for the researcher to promote cognitive augmentation of the
human brain through brain-machine/computer interface, virtual, augmented, and extended reality, fostering a
symbiotic relationship between humans and machines in the industrial metaverse and industry 5.0. The paper
discusses future directions for research and the challenges involved in developing intelligent digital twins towards the digital triplet paradigm, aiming to embody intelligent activities and cognitive capabilities within the
framework of human–machine symbiosis.
Gêmeo digital é a representação virtual de um ambiente ou de um ativo físico. Não se restringe a um modelo geométrico tradicional, mas é uma referência de dados que podem ser utilizados durante todo o ciclo de vida de um ativo. Gêmeos digitais não se restringem a um conjunto de dados estáticos, pois se comunicam com suas contrapartes físicas, compartilham informações, utilizando tecnologias como Inteligência Artificial (IA) e Internet of Things (IoT). Este trabalho tem por objetivo, através de uma pesquisa bibliográfica com caráter exploratório, analisar a relação entre Gêmeo Digital e uma estrutura modelada em Building Information Modeling (BIM), por meio de uma aplicação de monitoramento de estruturas. Foi proposta uma aplicação do Gêmeo Digital em conjunto com o BIM, instalando um sensor de distância acoplado a uma placa micro controladora (Arduino), funcionando como um servidor remoto para armazenamento de dados, capaz de se comunicar em tempo real com o software BIM e o gêmeo digital da estrutura. O exemplo mostra o amplo campo de aplicação e as possibilidades que a combinação entre sensores e tecnologia oferece para a monitoramento de estruturas e sua vida útil, capaz de gerar informação estratégica para o monitoramento, controle e gerenciamento das estruturas.
This paper extends the traditional factory digital twins by incorporating human characterisation in Asset Administration Shell (AAS). The extension lays the basis for human-centred control and management, as demonstrated by employing a prototype of the extended AAS in two proposed use cases. Referred to Industry 5.0, an accurate digital representation of humans as a basis of the data-based decision support to improve operators’ well-being and resilience. The AAS is extended to include dedicated digital models accommodating a set of properties to describe the human operators and its interactions with the surrounding shop-floor resources. Two reference use cases have been designed in the context of a complete lab-scale manufacturing system: equipment and devices have been modelled according to the AAS standard, exposing information via MQTT, and have been integrated with the proposed AAS definition of human operators. Operators have been equipped with wearable sensors and a dashboard providing them with feedback from the manufacturing environment and notifications about changes. As part of the extension process, some ethical and regulation concerns are discussed, highlighting that the extended AAS is mature enough to support the inclusion of human operators, but regulations struggle to keep up with technological advances.
Maintenance optimization has been of high interest in recent years for both the industry and the knowledge institutions. For example, tens of billions of dollars are spent on annual aviation maintenance, repair, and overhaul (MRO) activities. At the same time, the attention also grows in the direction of the advances in data analytics and digital technologies which can enable the next step in maintenance transition from preventive to predictive. The integration and operational deployment of physics-based (domain knowledge) and data-driven (AI, digital twin) innovative technologies can enhance the optimization of lifecycles and processes. Main objectives are the reduction of aircraft downtime and costs as well as a minimal waste in terms of materials and energy.
The rapid emergence and widespread adoption of next-generation information technologies have led to a growing recognition of the need for digitalized warehouse designs. However, creating a digital replica of a physical warehouse in a virtual environment is a complex task. This research introduces a framework for designing smart warehouses based on digital twins, consisting of four key steps: (1) defining the dimensions of the digital twin, (2) establishing a digital twin framework that encompasses the physical warehouse, digital twin, and design processes, (3) implementing modularization techniques for the digital twin, and (4) operating the smart warehouse based on the digital twin. To validate the proposed framework, we present a detailed case study involving a semiconductor manufacturing plant. The results demonstrate the effectiveness of the digital twin-based smart warehouse design and its operational processes.
span>The article considers the digital transformation of the aviation industry based on twins of maintenance and repair. As a result of digitization, a set of documents and application software are formed in a virtual reality environment. The concept of a digital twin is presented in the context of a model-based system design of the helicopter maintenance process according to the technical regulations. Based on statistical modeling technologies, a model for assembling aircraft units has been developed, in which time characteristics are qualitative estimates of learning processes and the effectiveness of digital twins. The results of experimental studies based on the method of analysis of students' certification using digital twins in the assembly of aircraft units are presented. It has been established that at the production site of an aircraft repair enterprise it is effective to apply training at the first stage using digital twins, and at the second stage using real objects. Based on analytical and experimental studies, regression models are proposed for the relationship between the optimal number of trainings at the second stage and the relative coefficient of training time for successful training and certification of mechanics and electronics engineers.</span
Intelligent Fuzzy Edge Computing (IFEC) has emerged as an innovative technology to enable real-time decision-making in Internet of Things (IoT)-based Digital Twin environments. Digital Twins provide virtual models of physical systems, facilitating predictive maintenance and optimization. However, implementing real-time decision-making in these environments is challenging due to massive data volumes and need for quick response times. IFEC addresses this by offering a flexible, scalable and efficient platform for real-time decision-making. This paper presents an overview of key aspects of IFEC including fuzzy logic, edge computing and Digital Twins. The use of fuzzy logic in IFEC provides an adaptive framework for handling uncertainties in data. Edge computing enables localized processing, reducing latency. The integration of Digital Twins allows system monitoring, analysis and optimization. Potential applications of IFEC are highlighted in domains such as manufacturing, healthcare, energy management and transportation. Recent advancements in IFEC are also discussed, covering new fuzzy inference systems, edge computing architectures, Digital Twin modeling techniques and security mechanisms. Overall, IFEC shows great promise in enabling real-time decision-making in complex IoT-based Digital Twin environments across various industries. Further research on IFEC will facilitate the ongoing digital transformation of industrial systems.
The construction industry has a great impact on social and economic development because of its wide coverage and a large number of stakeholders involved. It is precisely owing to its large volume that technological innovation of the construction industry is relatively slow. The birth and rapid development of digital twins brings more hope to the construction industry. This paper summarizes the current development of digital twin and its applications in construction industry. First, the concepts and applications of digital twin are analyzed. Then, the research on digital twins in the construction industry in the past five years is reviewed. The main research directions and key technologies are pointed out in the end. This paper could guide related practitioners to clearly grasp the research application status of digital twin in the construction industry. It could also help to find suitable research directions.
Reengineering of the aircraft structural life prediction process to fully exploit advances in very high performance digital computing is proposed. The proposed process utilizes an ultrahigh fidelity model of individual aircraft by tail number, a Digital Twin, to integrate computation of structural deflections and temperatures in response to flight conditions, with resulting local damage and material state evolution. A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented. The technical challenges to developing and deploying a Digital Twin are discussed in detail.