Digital Twin Research Articles

Retraction Note: Fuzzy logic-enabled autonomous IoT systems for proactive maintenance in industry 4.0 digital twin scenarios

Retraction Note: Fuzzy logic-enabled autonomous IoT systems for proactive maintenance in industry 4.0 digital twin scenarios

Digital-twin-based multi-scale simulation supports urban emergency management: a case study of urban epidemic transmission

ABSTRACT Improving emergency management capabilities is vital for sustainable cities. However, urban emergency management is a complex, giant system that interacts between urban virtual and real spaces, requiring simulation in the virtual space and feedback to the real space. The digital twin model can potentially play an important role in this process, but still faces significant challenges in terms of modeling, simulation, and visualization. In this study, we propose a digital-twin-based multi-scale simulation method and take epidemic transmission as a case study. First, an entity-oriented unified urban database integrating multi-source urban data was constructed as the digital twins of urban human settlements for emergency modeling. Then, a multi-scale urban emergency simulation method in 3D geographic environment was proposed to support emergency simulation for multi-level urban management units. Moreover, an adaptive fusion rendering of emergency information and 3D scene was proposed to support the refinement of emergency visualization. An experimental case study on epidemic risk estimation was conducted through thousands of outbreak scenarios. The results demonstrate that the proposed method can establish the digital twins of urban human settlements, where modeling, simulation, and visualization of emergency at multi-scales can be carried out, thus enhancing the efficacy and efficiency of emergency management.

NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking

The paper presents a novel framework for implementing decentralized algorithms based on non-fungible tokens (NFTs) for digital twin management in aviation, with a focus on component lifecycle tracking. The proposed approach uses NFTs to create unique, immutable digital representations of physical aviation components capturing real-time records of a component’s entire lifecycle, from manufacture to retirement. This paper outlines detailed workflows for key processes, including part tracking, maintenance records, certification and compliance, supply chain management, flight logs, ownership and leasing, technical documentation, and quality assurance. This paper introduces a class of algorithms designed to manage the complex relationships between physical components, their digital twins, and associated NFTs. A unified model is presented to demonstrate how NFTs are created and updated across various stages of a component’s lifecycle, ensuring data integrity, regulatory compliance, and operational efficiency. This paper also discusses the architecture of the proposed system, exploring the relationships between data sources, digital twins, blockchain, NFTs, and other critical components. It further examines the main challenges of the NFT-based approach and outlines future research directions.

The Role of the Internet of Things in Enhancing Sustainable Urban Energy Systems: A Review of Lessons Learned from the COVID-19 Pandemic

ABSTRACT The Internet of Things (IoT) has emerged as an indispensable technology enabling the efficient management of energy consumption and deployment of renewable energy solutions within urban energy systems. While urban energy systems can be made sustainable in both smart cities (SC) and traditional cities, the former provide a more advantageous environment for achieving sustainability thanks to advanced technology integration, data-driven decision-making, integration of renewable energy, as well as policy and infrastructure support. Across the globe, cities are evolving into SCs through the implementation of sustainable urban energy systems and adaptation to the latest IoT technologies. For them, advanced cutting-edge technologies such as digital twins, artificial intelligence (AI), Big Data, and IoT are all positioned to underpin the civic platform for new forms of a city that is simultaneously sustainable and prosperous. The COVID-19 pandemic brought about significant challenges, emphasizing the need for resilient and sustainable urban environments and demonstrating that IoT became a must for the modern cities in energy generation, transmission, and the efficient use of energy sources by cities. This bibliometric review focuses on the new role of IoT in the development of post-COVID sustainable urban energy systems. It demonstrates how IoT can help to efficiently manage energy consumption and to deploy novel renewable energy solutions in the cities using the experience from the two years of the COVID-19 pandemic. The article explores the role of IoT in the post-COVID urban energy development highlighting how the technology can contribute to sustainable urbanization. It employs the extended literature review including both 151 publications retrieved from the Web of Science (WoS) database selected based on the relevant keywords and the analytical tools based on Google Trends as well as the enhanced network dynamics VOSviewer v.1.6.15 software (the latter being frequently used for identifying the dominant trends in intersectoral bibliometric research). The findings stemming from this research can contribute to the clustering of the discussion of the strategies required for boosting SCs’ development and growth. Therefore, the results and outcomes can provide many useful insights for academics and stakeholders alike on the role of the IoT in the development of sustainable urban energy in post-pandemic cities.

Fault diagnosis of computer numerical control machine tools table feed system based on digital twin and machine learning

Fault diagnosis of computer numerical control machine tools table feed system based on digital twin and machine learning

Directional wave spectrum estimation through onboard measurement data utilizing B-spline basis functions

The utilization of digital twin technology, which integrates real-world measurement data with a digital representation of the vessel, is garnering increasing attention within the realm of structural integrity management. Due to limitations in sensor deployment on ships, the integration of the digital twin framework, which merges measurement data with the ship's digital design model, becomes crucial for ensuring the effectiveness of structural integrity management. This investigation introduces a methodological approach for estimating localized responses at unmeasured locations, leveraging both measurement and design datasets. To approximate the response at unobserved sites, the directional wave spectrum is initially determined utilizing the least squares method, thereby, minimizing the disparity between the estimated and measured response spectra. The methodology notably incorporates cubic B-spline interpolation specifically to smooth the directional wave spectrum deployed on the heading-frequency domain. This deliberate choice of employing cubic B-spline interpolation underscores the emphasis on achieving a refined and continuous representation of the directional wave spectrum, thus enhancing the accuracy and reliability of the estimation process. To validate the proposed methodology, synthetic pseudo-measurement data derived from the wave spectrum and RAO of a 13,000 TEU container ship are employed. Subsequently, the directional wave spectrum is estimated utilizing the pseudo-measurement data, and the estimated directional wave spectrum, in conjunction with the Response Amplitude Operator (RAO), is harnessed to approximate the response spectrum at the location where sensors are not installed.

Digital twin water conservancy early warning system based on physical level and numerical simulation in intelligent water conservancy construction

Abstract. In 2023, the Central Committee of the Communist Party of China and The State Council issued the Overall Layout Plan for the Construction of Digital China, emphasizing the construction of a smart water conservancy system with digital twin basins as the core. The Guangxi Zhuang Autonomous Region government has clearly proposed to actively build a "digital twin Pinglu Canal", and attaches great importance to the construction of digital twin platform application services during the construction of the Western land-Sea New Passage (Pinglu) canal. Aiming at the problems such as unsatisfactory early warning effect, inaccurate data and imprecise model of existing smart water conservancy technology, this paper proposes a digital twin system for disaster early warning based on physical level and numerical simulation technology, establishes a visual digital water conservancy model and digital twin smart water conservancy cloud platform, and combines multi-physics field and multi-dimensional coupling algorithm to respond to technical pain points. To achieve scientific and accurate monitoring and intelligent management, and contribute to the construction of digitally empowered Pinglu Canal.

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

Metadata schema for virtual building models in digital twins: VB schema implemented in GPT-based applications

A virtual building model (VBM) is a virtual entity that represents the physical behavior of a target building mathematically within a digital twin environment. The creation and synchronization of a VBM are achieved by utilizing various interrelated virtual sub-models, including behavior, correction, and distance models. To achieve continuous digital twinning, it is essential to manage the VBM with virtual sub-models. However, these metadata schemas have limitations in describing VBMs representing operational building behaviors within the concept of building digital twins (DTs). Therefore, this study proposes a novel metadata schema, termed the virtual building model metadata schema (VB schema), to represent and manage VBMs in DT-built environments. The VB schema is established according to the mathematical and semantic ontology of the in-situ modeling and calibration approach for constructing and correcting virtual models during building operations, and it is linked to physical entities, data, and applications within DTs. Specifically, it involves: (1) determining classes for operational data and virtual models; (2) establishing relationships for interactions between model and data entities, between model classes, between model and physical entities, and between model and applications; (3) defining properties for each class of models; and (4) extending into the exiting metadata schema of Brick. To demonstrate the proposed VB schema, a virtual model describing supply pressure behaviors in a central heating system was developed and represented using the VB schema for DT-enabled building operations. Additionally, the VB schema was utilized for implementing generative pre-trained transformer (GPT)-based DT applications, which highlights its benefits in enhancing ontology comprehension of DTs in the context of VBMs, improving autonomous problem-solving capabilities in real building systems, and providing better interpretation of application results compared to cases where only the Brick schema was used. The VB schema is expected to enable continuous and autonomous in-situ management of VBMs for intelligent building services within the DT.

Experimental prototype of mesh antenna based on digital twin technology

The ring mesh antenna plays an important role in the field of aerospace communication owing to its excellent performance characteristics, but faces the challenge of decreasing profile accuracy due to environmental and structural failures during in-orbit operation. Digital twin, a promising technology, can effectively monitor and adjust the antenna surface accuracy. In other words, by creating a digital model of the physical entity, real-time monitoring and adjustment can be made to maintain the electrical performance of the antenna. In this paper, a digital twin system for an experimental prototype of a ring mesh antenna is proposed. Firstly, we introduce the preliminaries of digital twin technology. Secondly, we propose a system framework based on a five-dimensional digital twin model. Then, a digitization software and a visualization interface are developed. Finally, according to efficiency analysis and simulation results, the feasibility of the system was verified. We demonstrate its effectiveness in terms of real-time monitoring and automatic mesh adjustment functions, and significantly improve the efficiency of the adjustment process. It provides a reference with significant value for the application of digital twin technology in the field of aerospace communication, which significantly advances the development of this field.

Research on online deformation monitoring of thin-walled parts driven by digital twin

Thin-walled parts are widely used in aerospace. Because thin-walled parts have thin walls, weak stiffness, and are easy to deform and other characteristics, it is easy to produce deformation in the processing, affecting the quality and accuracy of the workpiece processing. Therefore, accurate and efficient online real-time monitoring of the deformation of thin-walled parts has become a vital issue in process control. The visualization, real-time monitoring, and iterative optimization of digital twin technology can effectively solve the problem of online monitoring of thin-walled parts processing. Therefore, this paper proposes a digital twin driven deformation monitoring method for thin-walled parts, which realizes the effective monitoring of deformation during the processing of thin-walled parts. Firstly, the digital twin architecture for online deformation monitoring of thin-walled parts is established, and the critical technologies of the digital twin for online deformation monitoring of thin-walled parts are proposed. Secondly, the Support Vector Regression (SVR) finite element surrogate model is established to realize the rapid simulation of thin-walled parts cutting. At the same time, a One-Dimensional Convolutional Neural Network (1DCNN) combined with the Self-Attention Mechanism (SAM) deformation prediction model was established based on sensor data. The real-time monitoring and accurate prediction of the deformation of thin-walled components are realized through the parallel guidance of the deep learning and surrogate model. Finally, an experimental analysis was carried out by machining thin-walled plates of aluminum alloy 7075, and the fit of the monitoring models was above 95%, thus verifying the validity of the proposed method.

A strain-interfaced digital twin solution for corner fatigue crack growth using Bayesian inference

This paper introduces a digital twin solution for corner fatigue crack growth assessment. The digital twin comprises three core features: (1) diagnosis, (2) prognosis and (3) updating. The diagnosis arm performs remote crack size measurement via strain data collected from strategically identified locations. The prognosis component postulates the fatigue life across both linear-elastic and elasto-plastic loading regimes through a fatigue crack growth power law with the cyclic J-integral, ΔJ, as the crack driving force. Uncertainty in power law parameters, however, may result in differences between the prognosis and observed fatigue life. Hence, the digital twin completes the feedback loop via Bayesian updating of the power law parameters, thereby mirroring its physical counterpart closely. An improved estimation of the remaining useful life follows. The proposed digital twin solution validates against three specimens under constant amplitude loading and a single specimen under variable amplitude loading. The successful application of the approach marks a significant step toward operational digital twins within practical settings.

Developing Prototype Digital Twins for biodiversity conservation and management: achievements, challenges and perspectives

The BioDT project, funded by the European Union Horizon Europe Programme, seeks to investigate and push the boundaries of methodological, intellectual, data, technical and relevance aspects of the digital twin (DT) approach for understanding and predicting biodiversity patterns and processes. In this special issue, we present the reasoning and goals of the project and the overall advancements made towards the development of ten prototype Digital Twins (pDTs). Based on models of ecosystems, species and biological processes, digital twins integrate diverse data sources with advanced modelling and simulation techniques to provide a comprehensive, dynamic and data-driven understanding of biodiversity. Leveraging the EuroHPC LUMI infrastructure and data and expertise from GBIF, eLTER, DiSSCo and LifeWatch ERIC research infrastructures, the project aims to develop hybrid models combining the strengths of statistical and process-based models for improved predictive capabilities. While there are specific challenges and limitations to biodiversity DTs, the pDTs of BioDT lay the foundation for model-data fusions that will offer potential benefits to a wide range of end-users, from researchers and conservation organisations to policy-makers, land managers, educators and private sector companies. By fostering global interoperability in biodiversity data and research, BioDT paves the way for future collaborative efforts in biodiversity conservation and management.

Digital Twin‐Enabled Deep Reinforcement Learning for Safety‐Guaranteed Flocking Motion of UAV Swarm

ABSTRACTMulti‐agent deep reinforcement learning (MADRL) has become a typical paradigm for the flocking motion of UAV swarm in dynamic, stochastic environments. However, sim‐to‐real problems, such as reality gap, training efficiency, and safety issues, restrict the application of MADRL in flocking motion scenarios. To address these problems, we first propose a digital twin (DT)‐enabled training framework. With the assistance of high‐fidelity digital twin simulation, effective policies can be efficiently trained. Based on the multi‐agent proximal policy optimization (MAPPO) algorithm, we then design the learning approach for flocking motion with matching observation space, action space, and reward function. Afterward, we employ a distributed flocking center estimation algorithm based on position consensus. The estimated center is used as a policy input to improve the aggregation behavior. Moreover, we introduce a repulsion scheme, which applies an additional repulsion force to the action to prevent UAVs from colliding with neighbors and obstacles. Simulation results show that our method performs well in maintaining flocking formation and avoiding collisions, and has better decision‐making ability in near‐realistic environments.

К МЕТОДИКЕ ФОРМИРОВАНИЯ ЦИФРОВОГО ДВОЙНИКА ТЕХНОЛОГИЧЕСКОГО ПРОЦЕССА СУШКИ ОРГАНИЧЕСКИХ СЕМЯН ТИМОФЕЕВКИ

An important specific feature of the functioning of machines and equipment in the technologies of post-harvest processing of grain and seeds is the probabilistic variability of external conditions that negatively affect the efficiency of their work. The drying process is also characterized by non-stationarity in its probabilistic and statistical characteristics. The methods of forming digital twins of stationary processes are quite well mastered, but the construction of digital twins of non-stationary processes is a much more complex task, the solution of which requires taking into account the nature of non-stationarity, as well as the features and internal structure of the processes being modeled. The purpose of the research is to build a digital twin of the non-stationary process of drying timothy seeds and assess the reliability of the resulting model. The experimental data were obtained by drying Leningradskaya 204 timothy grass seeds from the field organic crop rotation using a universal drying unit with a Fauna PMDR4 digital seed moisture meter equipped with submersible flow sensors. As a result of the experimental studies, realizations of random processes of change in seed moisture content at the final stage of drying were obtained for constructing mathematical models using statistical identification methods. Discrete analogs of the processes under study were constructed using experimental correlation functions approximated by the corresponding functions to obtain the parameters of the modeling recurrent equations. The considered algorithm for modeling the dryer operation allows reproducing processes of unlimited length and modeling various conditions and operating modes to find the specified optima for quality, energy consumption and environmental friendliness. The accuracy of the simulation results was determined based on the convergence of the moment functions of the experimental and modeled technological processes. Estimates of statistical characteristics of the simulated process of drying timothy grass seeds with 95% probability correspond to statistical estimates of the drying process obtained in the course of experimental studies, and the graphs of their normalized correlation functions clearly confirm the identity of the processes. The obtained mathematical dependencies of the technological process of drying organic timothy grass seeds are its digital twin, allowing the methods of simulation modeling to solve optimization problems.

Digital twins in process engineering: An overview on computational and numerical methods

Digital twins in process engineering: An overview on computational and numerical methods

Bim-based Digital Twin development for university Campus management. Case study ETSICCP

Innovation and digitalization are outstanding topics acquiring each day more importance for local governments, especially in Facility Management sector. Moreover, during the COVID-19 situation, new management needs emerged, especially in large public buildings. Building Information Modeling (BIM) is considered as one of the emerging technologies used to reach a total digitalization of the infrastructure. Nevertheless, BIM implementation carries important barriers with itself like, high software and hardware investments, initial BIM skills training or low data interoperability. The objective of this project is to overpass those implementation barriers. For this purpose, the paper shows the creation of a BIM-based intelligent platform for infrastructure management that leads to the development of a Digital Twin (DT). To show the potential of the software developed, a real implementation in the Civil Engineering School at Universidad Politécnica de Madrid was carried out, obtaining significant results thanks to the actual feedback of infrastructure users and managers. The novelty of this project relies on the final results achieved, obtaining a complete DT for management functionalities like space reservation, live sensors data or assets management. All of it, linking BIM models with own software and hardware development using Internet of Things and cloud computing. A multidisciplinary work is compiled in this paper, providing the reader with the most relevant challenges detected in a real digitalization process.

Characterizing the Impact of Emergent Technologies on Earth Communications Reliance for Crewed Deep Space Missions

Future human expeditions into deep space will encounter unique design challenges posed by the increasing distance from Earth, one of which is the inability to maintain near-continuous communication with ground support teams. As a result, the habitat and crew will require a higher level of operational self-sufficiency informed by onboard self-awareness and decision-making capabilities to accomplish functions that have historically involved extensive support from mission control personnel. These include, for example, task planning and anomaly detection, diagnosis and correction, as well as monitoring of consumable usage rates and system performance. Novel emergent technologies in the domain of ‘smart’ systems and digital twins can be employed to enable the onboard capabilities needed to function with minimal Earth communication. However, these technologies are generally low-TRL and difficult to incorporate through traditional systems engineering methods.This work proposes an extension of utility theory to assess the potential reduction in Earth communication reliance between baseline and emergent technologies, scored through a summation of weighted attributes and evaluated against future deep space mission needs and constraints. Case studies are defined from technology under development in the NASA Habitats Optimized for Missions of Exploration (HOME) Space Technology Research Institute (STRI), a project charged with developing and evaluating technologies to enable highly autonomous deep space habitats, and compared to the current baseline technologies used onboard the International Space Station. Notional deep space mission needs and constraints are informed from current NASA plans for the Moon and Mars. These in turn are broken out into higher resolution attributes that are used to assess the operational capabilities of each emergent technology and whether it offers a potentially viable solution for deep space. These technology attributes are scored through a series of semi-structured subject-matter interviews and compared to notional deep space mission constraints. Additional considerations for technology maturation and integration are also discussed. The design analysis paradigm described and demonstrated here can be adapted to assess the degree to which other emergent technologies rely on Earth-based communication, and is intended to provide trade space considerations for future research and development towards deep space habitat self-sufficiency.

Advanced graph neural network-based surrogate model for granular flows in arbitrarily shaped domains

In next-generation manufacturing, the introduction of a surrogate model is crucial for constructing a digital twin. This trend is no exception in powder industry. Various mesh-free approaches such as the discrete element method (DEM) are versatile tools for facilitating simulation-based digital twins of powder industries. Unfortunately, the DEM-based high-fidelity simulation of actual industrial processes poses a significant challenge because of the prohibitive computational costs. In this context, the surrogate model has emerged as a promising technique to solve this problem. However, surrogate modeling of powder processes within arbitrarily shaped boundaries is substantially impossible. To resolve this essential problem in the existing surrogate models, we propose a novel surrogate model, namely, a signed distance function-based graph neural network (SGN), to accelerate the simulations of granular flows. In the SGN, the wall boundary is efficiently modeled by combining the graph structure with a signed distance function. Validation studies were conducted in typical powder systems, such as a sandglass, and a rotating-paddle mixer. A systematic comparison of the simulation results between the high-fidelity and SGN simulations shows that the proposed SGN accurately simulates granular dynamics, with a remarkable increase in computational efficiency. This study significantly contributes to the next-generation manufacturing in the powder industry.

The Changing Role of the Architect from Craftsmanship to Artificial Intelligence Environment in Historical Context

Architects differentiated themselves from master builders, craftsmen, and engineers, acquiring a distinct identity, social status, and position with modernity. The observation of this study indicates the presence of signs that a similar radical change is beginning to emerge. The knowledge and skills that contemporary architects need to possess are diverging with the incorporation of systems such as artificial intelligence, metaverse, blockchain, machine learning, and digital twins into the process, thereby transforming the ways architects design and represent. In this context, the study attempts to identify changes in the architect's identity through the evolving role of the architect in the historical process. It concludes that the role of the architect throughout the historical process is not stable but subject to continuous and dynamic change.