From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

Comprehensive Survey of the Landscape of Digital Twin Technologies and Their Diverse Applications

The concept of the digital twin,also known colloquially as the DT,is a fundamental principle within Industry 4.0 framework.In recent years,the concept of digital siblings has generated considerable academic and practical interest.However,academia and industry have used a variety of interpretations,and the scientific literature lacks a unified and consistent definition of this term.The purpose of this study is to systematically examine the definitional landscape of the digital twin concept as outlined in scholarly literature,beginning with its origins in the aerospace domain and extending to its contemporary interpretations in the manufacturing industry.Notably,this investigationwill focus on the research conducted on Industry 4.0 and smartmanufacturing,elucidating the diverse applications of digital twins in fields including aerospace,intelligentmanufacturing,intelligent transportation,and intelligent cities,among others.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

A Dual Closed-Loop Digital Twin Construction Method for Optimizing the Copper Disc Casting Process

The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.

Control Strategies for Digital Twin Systems

With the continuous breakthrough in information technology and its integration into practical applications, industrial digital twins are expected to accelerate their development in the near future. This paper studies various control strategies for digital twin systems from the viewpoint of practical applications.To make full use of advantages of digital twins for control systems, an architecture of digital twin control systems, adaptive model tracking scheme, performance prediction scheme, performance retention scheme, and fault tolerant control scheme are proposed. Those schemes are detailed to deal with different issues on model tracking, performance prediction, performance retention, and fault tolerant control of digital twin systems. Also, the stability of digital twin control systems is analysed. The proposed schemes for digital twin control systems are illustrated by examples.

Exploring Alternatives to Create Digital Twins from and for Process Simulation

In this work,Digital Twins based on Neural Networks for the steady state production of styrene were generated.Thus,both the Aspen Technology AI Model Builder(alternative 1)and a homemade MS Excel VBA code connected to Aspen HYSYS and Aspen Plus(alternative 2)were used with this same aim.The raw data used for generating the Digital Twins were obtained from process simulations using Aspen HYSYS and/or Aspen Plus,which were connected through a recycle-like stream via automation for solving the entire simulation flowsheet.Aspen HYSYS was used for solving the pre-heating,reaction,and stabilization sections of the process whereas Aspen Plus ensured the computing of the separation and purification columns.Both alternatives led to an excellent prediction showing the capability of creating Digital Twins from and for process simulation.

Inaugural editorial-Digital Twins and Applications

Academician of the CAE member Youxian Sun from Zhejiang University initiated Digital Twins and Applications(ISSN 2995-2182).It is published by Zhejiang University Press and the Institution of Engineering and Technology and sponsored by Zhejiang Univer-sity.Digital Twins and Applications aim to provide a specialised platform for researchers,practitioners,and industry experts to publish high-quality,state-of-the-art research on digital twin technologies and their applications.

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

Generative Adversarial Networks Based Digital Twin Channel Modeling for Intelligent Communication Networks

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and networks.With DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly increased.Artificial intelligence(AI)based modeling approach shows outstanding performance to solve such problems.In this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured channel.Model validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured channel.Finally,a link-layer simulation is implemented based on DT channel.It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data.The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

Modeling Methods of 3D Model in Digital Twins

To understand the current application and development of 3D modeling in Digital Twins(DTs),abundant literatures on DTs and 3D modeling are investigated by means of literature review.The transition process from 3D modeling to DTs modeling is analyzed,as well as the current application of DTs modeling in various industries.The application of 3D DTs modeling in the􀀀elds of smartmanufacturing,smart ecology,smart transportation,and smart buildings in smart cities is analyzed in detail,and the current limitations are summarized.It is found that the 3D modeling technology in DTs has broad prospects for development and has a huge impact on all walks of life and even human lifestyles.At the same time,the development of DTs modeling relies on the development and support capabilities of mature technologies such as Big Data,Internet of Things,Cloud Computing,Articial Intelligence,and game technology.Therefore,although some results have been achieved,there are still limitations.This work aims to provide a good theoretical support for the further development of 3D DTs modeling.

Application of Digital Twin in Smart Battery Management Systems

Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.

Systematic review of digital twin technology and applications

As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application value.This phenomenon has been widely studied in academia and industry.In this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are summarized.The internal association between DT and related technologies is explained.The four stages of DT development history are identified.The fundamentals of the technology,evaluation indexes,and model frameworks are reviewed.Subsequently,a conceptual ternary model of DT based on time,space,and logic is proposed.The technology and application status of typical DT systems are described.Finally,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.

A generic framework for qualifications of digital twins in maintenance

Digital twins have emerged as a promising technology for maintenance applications,enabling organizations to simulate and monitor physical assets to improve their performance.In Operation and Maintenance(O&M),digital twin facilitates the diagnosis and prognosis of critical assets,forming the basis for smart maintenance planning and reducing downtime.However,there is a lack of standardized approaches for the qualifications of digital twins in maintenance,leading to low trustworthiness and limiting its application.This paper proposes a novel framework for the qualifications of digital twins in maintenance based on five pillars,namely fidelity,smartness,timeliness,integration,and standard compliance.We demonstrate the effectiveness of the framework through two case studies,showing how it can be implemented on digital twins for preventive maintenance and condition-based maintenance.Our proposed framework can help organizations across different industrial domains develop and implement digital twins in maintenance more effectively and efficiently,leading to significant benefits in terms of cost reduction,performance improvement,and sustainability.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

基于Twin Builder的110kV油浸式变压器3维磁场降阶模型及损耗分析

为解决3维变压器磁场有限元仿真计算效率低的问题,提出基于本征正交分解法的磁场降阶模型与基于二次多项式的损耗响应面模型。以1台110 kV变压器为算例,首先建立了考虑绕组涡流损耗的变压器磁场仿真模型,通过仿真分析了其在额定工况下的磁场及损耗分布;其次,通过仿真试验设计获取了多组工况下的磁场与损耗数据,在Twin Builder中训练得到变压器磁场的各降阶模块;最后,搭建面向数字孪生应用的变压器3维磁场降阶模型,实现了变压器磁场损耗的快速计算与云图分布的可视化。与计算单数据相比,仿真结果考虑了涡流效应的绕组损耗在靠近端部时有明显升高,最大值分别出现在高、低压绕组两端,约为绕组中部损耗的1.3~1.65倍。基于该磁场结果建立的降阶模型在计算效率方面较全阶模型有显著提升,将实际仿真时间缩短至0.76s,可满足数字孪生应用场景下的磁场快速计算需求。

Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving

Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields,such as new energy vehicles and energy storage.The core issues hindering their further promotion and application are reliability and safety.A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety.This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions.An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics.By adding the descriptions of temperature distribution and particle-level stress,a multi-particle size electrochemical-thermal-mechanical coupling model is established.Then,considering the different electrical and thermal effect among individual cells,a model for the battery pack is constructed.A digital twin model construction method is finally developed and verified with battery operating data.

Digital twin-enabled smart facility management: A bibliometric review

In recent years,the architecture,engineering,construction,and facility management(FM)industries have been applying various emerging digital technologies to facilitate the design,construction,and management of infrastructure facilities.Digital twin(DT)has emerged as a solution for enabling real-time data acquisition,transfer,analysis,and utilization for improved decision-making toward smart FM.Substantial research on DT for FM has been undertaken in the past decade.This paper presents a bibliometric analysis of the literature on DT for FM.A total of 248 research articles are obtained from the Scopus and Web of Science databases.VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence,citation,and co-authorship networks;furthermore,the research topics,authors,sources,and countries contributing to the use of DT for FM are identified.The findings show that the current research of DT in FM focuses on building information modeling-based FM,artificial intelligence(AI)-based predictive maintenance,real-time cyber–physical system data integration,and facility lifecycle asset management.Several areas,such as AI-based real-time asset prognostics and health management,virtual-based intelligent infrastructure monitoring,deep learning-aided continuous improvement of the FM systems,semantically rich data interoperability throughout the facility lifecycle,and autonomous control feedback,need to be further studied.This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape,state-of-the-art research trends,and future needs with regard to DT in FM.

Architecture,application,and prospect of digital twin for highway infrastructure

The years we are experiencing are often identified as those of the age of digital technologies,where“digital”is commonly associated with intelligence,efficiency,and convenience.The emergence of digital technologies has significantly impacted and transformed various aspects of our society compared to the past.In this panorama,some arising questions regard transportation infrastructure systems and,first of all,highway infrastructure.This research focuses on one central issue:how highways fit into this digital revolution.Actually,the work in this paper can be described as follows.Although there are many different theoretical model systems for the architecture of the digital twin,we have chosen to review the main body of research on the digital twin in highway infrastructure based on a relatively well-established modeling framework,the five-dimensional model of the digital twin.After discussing the components of the digital twin for highway infrastructure's five-dimensional model,the paper reviews some innovative technologies that make these items effective.In addition to this,the digital twin maturity level of highway infrastructure and the MBSE-based(model-based systems engineering)digital twin model for highway are also discussed in this paper.Therefore,the paper provides a bird's eye view of this extremely dynamic technology for a new system of intelligent highways and discusses some of their criticalities and strengths,allowing for the optimization and development of new transportation functions and services,improving the adaptability of highways to the digital revolution.

Research on Digital Twin System Platform Framework and Key Technologies of Unmanned Ground Equipment

As an emerging technology,digital twin is expected to bring novel application modes to the whole life cycle process of unmanned ground equipment,including research and development,design,control optimization,operation and maintenance,etc.The highly dynamic,complex,and uncertain characteristics of unmanned ground equipment and the battlefield environment also pose new challenges for digital twin technology.Starting from the new challenges faced by the digital twin of unmanned ground equipment,this paper designs a service-oriented cloud-edge-end collaborative platform architecture of the digital twin system of unmanned ground equipment,and further analyzes several key technologies supporting the implementation of the platform architecture.