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.

Construction of an Arrhenius constitutive model for Mg-Y-Nd-Zr-Gd rare earth magnesium alloy based on the Zener-Hollomon parameter and objective evaluation of its accuracy in the twinning-rich intervals

According to a high-temperature compression test of rare earth magnesium alloy(WE43),a strain-compensated constitutive model of the Arrhenius equation based on Zener-Hollomon parameters was established,and the rheological behaviors were predicted.The model exhibited relatively serious prediction distortion in the low-temperature and high-strain rate parameter interval,and its accuracy was still unsatisfactory even after modification by a correction operator considering the coupling of temperature and strain rate.The microstructure characterization and statistical analysis showed that a large number of twinning occurred in the parameter intervals with prediction deviation.The occurrence of twinning complicated the local internal stress distribution by drastically changing the crystal orientation and led to significant fluctuations in the macroscopic strain-stress and hardening curves relative to the rheological processes dominated by the dislocation and softening mechanisms,making the logarithm of the strain rate and stress deviate from the linear relationship.This twinning phenomenon was greatly influenced by the temperature and strain rate.Herein,the influence mechanism on twinning behavior was analyzed from the perspective of the interaction of dislocation and twinning.

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。

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.

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management.To completely extract the correlated state information of SPNDs,we constructed a twin model based on a generalized regression neural network(GRNN)that represents the common relationships among overall signals.Faulty SPNDs were determined because of the functional concordance of the twin model and real monitoring sys-tems,which calculated the error probability distribution between the model outputs and real values.Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures.A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity.The experimental evaluation of the proposed method showed promising results,with excellent output consistency and high detection accuracy for both single-and multiple-point faulty SPNDs.For unexpected excessive failures,the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.

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.

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.

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.

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.

Three-dimensional Modeling for Predicting the Vibration Modes of Twin Ball Screw Driving Table

As a redundant drive mechanism, twin ball screw feed system has the advantage of high stiffness and little yaw vibration in the feeding process, while leads to increased difficulty with vibration characteristics analysis and structure optimization. Only low-dimensional structure and dynamics parameters are considered in the existing research, the complete and effective model for predicting the table＇s vibrations is lacked. A three-dimensional（3D） mechanical model of twin ball screw driving table is proposed. In order to predict the vibration modes of the table quantitatively, an analytical formulation following a comprehensive approach is developed, where the drive system is modeled as a lumped mass-spring system, and the Lagrangian method is used to obtain the table＇s independent and coupled axial, yaw, and pitch vibration modes. The frequency variation of each mode is studied for different heights of the center of gravity, nut positions and table masses by numerical simulations. Modal experiment is carried out on the Z-axis feed table of the horizontal machining center MCH63. The results show that for each mode, the error between the estimated and the measured frequencies is less than 13%. The independent and coupled vibration modes are in accordance with the experimental results, respectively The proposed work can serve a better understanding of the table＇s dynamics and be beneficial for optimizing the structure parameters of twin ball screw drive system in the design stage.

Mathematical modeling of thermo-mechanical behavior of strip during twin roll casting of an AZ31 magnesium alloy

The effect of set-back distance on the thermo-mechanical behavior of the strip during twin roll casting(TRC)of an AZ31 magnesium alloy was modeled using finite element method(FEM).Model validation was done by comparing the predicted and measured exit strip surface temperature as well as the secondary dendrite arm spacing(SDAS)through the thickness of the sheet to those measured during experiments.Model results showed as the set-back distance increases,the strip exit temperature decreases and the solidification front moves toward the entry of the roll gap.The cast strip also experiences more plastic deformation and consequently,the normal stress on the strip surface and effective strain at the strip center-line increase.Moreover,higher separating forces were predicted for longer set-back distances.Model predictions showed that changing the set-back distance by varying the final thickness has a more significant effect on the temperature and stress-strain fields than altering the nozzle opening height.

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.

Image Denoising Combining the P-M Model and the LLT Model

In this paper, we present a noise removal technique by combining the P-M model with the LLT model. The combined technique takes full use of the advantage of both filters which is able to preserve edges and simultaneously overcomes the staircase effect. We use a weighting function in our model, and compare this model with the P-M model as well as other fourth-order functional both in theory and numerical experiment.

Water Modeling of Twin-Roll Strip Casting

Twin-roll strip casting is regarded as a prospective technology of near net shape continuous casting. The fluid flow field and level fluctuation in the pool have a strong influence not only on composition and temperature homogeneity of pool, but also on the strip quality. A 1 ： 1 water model of a twin-roll strip caster was set up based on the criteria of Froude number and Reynold number similarity. The level fluctuation was measured. The influence of pool depth, casting speed and feeding system configuration on level fluctuation in the pool was studied. The experimental results provided a basis for the optimization of feeding system and process parameters.

Synchro-control of Twin-rudder with Cloud Model

In ships having two rudders, an angle error exists if there is a difference in structural and electrical parameters in two steering gear systems. Such an error also results in reduced efficiency of ship maneuverability during navigation. For the sake of reducing the angle error, a synchro-ballistic control approach based on cloud model is proposed in this paper. First, the mechanism model of steering gear system is introduced. Second, the structure of synchro-control system of twin-rudder is proposed based on the master-slave control strategy. Third, synchro-ballistic controller based on cloud model is designed to solve the nonlinearity and uncertainty of system. Finally, the designed controller is tested via simulation under two different situations. The simulated results demonstrate that this method is simple and has stronger robustness against the variation of states and parameters of plants. Hence, the validity and reliability of the method is proved for synchro-control of two rudders, which is a significant engineering application.

Application of Anand's constitutive model on twin roll casting process of AZ31 magnesium alloy

Twin-roll thin strip casting process combines casting and hot rolling into a single process, in which thermal stress and thermal mechanical stress were involved. Considering the high temperature gradient, the existing of liquid and solid regions and rolling deformation, suitable constitutive model is the key to describe the process. Anand's model is a temperature-dependent, rate-dependent and unified of creep and plasticity model and the Jaumann derivative was employed in Anand's model which makes the constitutive model frame-indifferent or objective, therefore the highly nonlinearities behavior in the twin-roll casting process can be simulated. The parameters of the Anand's model were regressed based on the compression tests of AZ31 magnesium alloy. The simulation results reveal that the Anand's model can well describe the deformation characteristics of twin-roll casting process. Based on the simulation results, the form of evolution equations in Anand's model was discussed.

Single Production of Top Quark via e^-γ Collision in Left-Right Twin Higgs Model

In the context of the left-right twin Higgs （LRTH） model, we fist study single production of the standard model （SM） top quark via e-γ collisions. We find that the corrections of the LRTH model to the cross section of the process e-γ→vebt might be observed only for f ≤750 GeV and the heavy top quark mass scale M ≥500 GeV in future high energy linear e^＋e^- collider （LC） experiment with the center-of-mass （CM） energy √s = 500 GeV and a yearly integrated luminosity of ￡ = 100 fb^-1. We also consider single production of the heavy top quark T via e-γ collisions. Our numerical results show that the possible signals of the heavy top quark T might be observed via the decay channel T →Ф^＋b→tbb in future LC experiment with √s = 3 TeV and ￡ = 500 fb^-1.

Production of Charged Higgs Bosons from Left-Right Twin Higgs Model at TeV Energy e^-γ Colliders

The left-right twin Higgs （LRTH） model predicts the existence of three additional Higgs bosons： one neutral Higgs φo and a pair of charged Higgs bosons φ±. In this paper, we study two pair production processes of these new particles at the next generation eγ colliders, i.e., e-γ → e-φ＋φ- , and e-γ→vRφ-φ0. We find that the production cross section of the process e-γ → e-φ＋φ- are at the level of several tens fb, the production cross section of the process e-γ→vRφ-φ0 can reach 0.35 fb with the reasonable parameter values. As long as the charged Higgs bosons are not too heavy, we conclude that these processes might be used to test for the left-right twin Higgs model in future high-energy linear collider experiments.