The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenu...With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Asymmetric tilt boundaries on conventional twin boundaries(TBs)are significant for understanding the role of twins on coordinating plastic deformation in many metallic alloys.However,the formation modes of many asymme...Asymmetric tilt boundaries on conventional twin boundaries(TBs)are significant for understanding the role of twins on coordinating plastic deformation in many metallic alloys.However,the formation modes of many asymmetric tilt boundaries are hard to be accounted for based on traditional theoretical models,and the corresponding solute segregation is complex.Herein,atomic structures of a specific asymmetric boundary on{1012}TBs were reveled using aberration-corrected high-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM),molecular dynamics(MD)and density functional theory(DFT)simulations.Reaction between<a60>M dislocations and the{1012}TB can generate a~61°/25°asymmetric tilt boundary.The segregation of Gd and Zn atoms is closely related to the aggregateddislocations and the interfacial interstices of the asymmetric tilt boundary,which is energetically favorable in reducing the total system energy.展开更多
It is difficult to generate coherent twin boundaries in bulk Al alloys due to their high intrinsic stacking fault energy. Here, we report a strategy to induce high-density growth twins in aluminum alloys through the h...It is difficult to generate coherent twin boundaries in bulk Al alloys due to their high intrinsic stacking fault energy. Here, we report a strategy to induce high-density growth twins in aluminum alloys through the heterogeneous nucleation of twinned Al grains on twin-structured TiC nucleants and the preferred growth of twinned dendrites by laser surface remelting of bulk metals. The solidification structure at the surface shows a mixture of lamellar twinned dendrites with ultra-fine twin boundary spacing (∼2 µm), isolated twinned dendrites, and regular dendrites. EBSD analysis and finite element method (FEM) simulations have been used to understand the competitive growth between twinned and regular dendrites, and the solidification conditions for the preferred growth of twinned dendrites during laser remelting and subsequent rapid solidification are established. It is shown that the reduction in the ratio of temperature gradient G to solidification rate V promotes the formation of lamellar twinned dendrites. The primary trunk spacing of lamellar twinned dendrites is refined by the high thermal gradient and solidification rate. The present work paves a new way to generate high-density growth twins in additive-manufactured Al alloys.展开更多
Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional ...Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional crown control method is no longer sufficient to meet the precision requirements of schedule-free rolling.To address this limitation,an optimization framework for hot-rolled strip crown control was developed based on model-driven digital twin(MDDT).This framework enhances the strip crown control precision by facilitating collaborative operations among physical entities,virtual models,and functional application layers.In virtual modeling,a data-driven approach that integrates the extreme gradient boosting and the improved Harris hawk optimization algorithm was firstly proposed to fit the relationship between key process parameters and strip crown,and a global-local collaborative training strategy was proposed to enhance the model adaptability to diverse working conditions.Subsequently,the influence of crucial process factors on the virtual model was examined through model responses.Furthermore,a novel optimization mode for crown control based on MDDT was established by aligning and reconstructing both the physical and virtual models,thereby enhancing the crown control precision.Finally,data trials were conducted to validate the effectiveness of the proposed framework.The results indicated that the proposed method exhibited satisfactory performance and could be effectively utilized to improve the crown control precision.展开更多
1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-...1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-sized particles(5-50μm)to fabricate coatings[1-3].CS has been extensively used in a variety of coating applications,such as aerospace,automotive,energy,medical,marine,and others,to provide protection against high temperatures,corrosion,erosion,oxidation,and chemicals[4,5].Nowadays,the technical interest in CS is twofold:(i)as a repair process for damaged components,and(ii)as a solid-state additive manufacturing process.Compared to other fusion-based additive manufacturing(AM)technologies,Cold Spray Additive Manufacturing(CSAM)is a new member of the AM family that can enable the fabrication of deposits without undergoing melting.The chemical composition has been largely preserved from the powder to the deposit due to the minimal oxidation.The significant advantages of CSAM over other additive manufacturing processes include a high production rate,unlimited deposition size,high flexibility,and suitability for repairing damaged parts.展开更多
Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-s...Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-silience study face many challenges.The introduction of digital twin technology provides an innovative solution for the resilience study of smart water distribution networks,which can more effectively support the network’s real-time monitoring and intelligent control.This paper proposes a digital twin architecture of smart water dis-tribution networks,laying the foundation for the resilience assessment of water distribution networks.Based on this,a performance evaluation model based on user satisfaction is proposed,which can more intuitively and effectively reflect the performance of urban water supply services.Meanwhile,we propose a method to quantify the importance of water distribution pipes’residual resilience,considering the time value to optimize the re-covery sequence of failed pipes and develop targeted preventive maintenance strategies.Finally,to validate the effectiveness of the proposed method,this paper applies it to a water distribution network.The results show that the proposed method can significantly improve the resilience and enhance the overall resilience of smart urban water distribution networks.展开更多
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金Supported by Beijing Municipal Natural Science Foundation of China(Grant No.24JL002)China Postdoctoral Science Foundation(Grant No.2024M754054)+2 种基金National Natural Science Foundation of China(Grant No.52120105008)Beijing Municipal Outstanding Young Scientis Program of Chinathe New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金supported by the Scientific and Technological Developing Scheme of Jilin Province under grants no.YDZJ202301ZYTS538the Chinese Academy of Sciences Youth Innovation Promotion Association under grants number 2023234+3 种基金the National Natural Science Foundation of China under grants number U21A20323the Scientific and Technological Developing Scheme of Jilin Province under grants no.SKL202302038the Major Scientific and Technological Projects of Hebei Province under grants No.23291001Zthe Scientific and Technology Project of Hanjiang District.
文摘Asymmetric tilt boundaries on conventional twin boundaries(TBs)are significant for understanding the role of twins on coordinating plastic deformation in many metallic alloys.However,the formation modes of many asymmetric tilt boundaries are hard to be accounted for based on traditional theoretical models,and the corresponding solute segregation is complex.Herein,atomic structures of a specific asymmetric boundary on{1012}TBs were reveled using aberration-corrected high-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM),molecular dynamics(MD)and density functional theory(DFT)simulations.Reaction between<a60>M dislocations and the{1012}TB can generate a~61°/25°asymmetric tilt boundary.The segregation of Gd and Zn atoms is closely related to the aggregateddislocations and the interfacial interstices of the asymmetric tilt boundary,which is energetically favorable in reducing the total system energy.
基金supported by the National Natural Science Foundation of China(grant no.52371029)the Science and Technology Development Program of Jilin Province,China(grant no.20210402083GH).
文摘It is difficult to generate coherent twin boundaries in bulk Al alloys due to their high intrinsic stacking fault energy. Here, we report a strategy to induce high-density growth twins in aluminum alloys through the heterogeneous nucleation of twinned Al grains on twin-structured TiC nucleants and the preferred growth of twinned dendrites by laser surface remelting of bulk metals. The solidification structure at the surface shows a mixture of lamellar twinned dendrites with ultra-fine twin boundary spacing (∼2 µm), isolated twinned dendrites, and regular dendrites. EBSD analysis and finite element method (FEM) simulations have been used to understand the competitive growth between twinned and regular dendrites, and the solidification conditions for the preferred growth of twinned dendrites during laser remelting and subsequent rapid solidification are established. It is shown that the reduction in the ratio of temperature gradient G to solidification rate V promotes the formation of lamellar twinned dendrites. The primary trunk spacing of lamellar twinned dendrites is refined by the high thermal gradient and solidification rate. The present work paves a new way to generate high-density growth twins in additive-manufactured Al alloys.
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFB3710204)Guangxi Science and Technology Major Program(Grant No.AA23023028-1)+1 种基金Natural Science Foundation of Heilongjiang Province of China for Distinguished Young Scientists(Grant No.JQ2022E007)Xinjiang Production and Construction Corps Science and Technology Plan(Grant No.2023AA003).
文摘Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional crown control method is no longer sufficient to meet the precision requirements of schedule-free rolling.To address this limitation,an optimization framework for hot-rolled strip crown control was developed based on model-driven digital twin(MDDT).This framework enhances the strip crown control precision by facilitating collaborative operations among physical entities,virtual models,and functional application layers.In virtual modeling,a data-driven approach that integrates the extreme gradient boosting and the improved Harris hawk optimization algorithm was firstly proposed to fit the relationship between key process parameters and strip crown,and a global-local collaborative training strategy was proposed to enhance the model adaptability to diverse working conditions.Subsequently,the influence of crucial process factors on the virtual model was examined through model responses.Furthermore,a novel optimization mode for crown control based on MDDT was established by aligning and reconstructing both the physical and virtual models,thereby enhancing the crown control precision.Finally,data trials were conducted to validate the effectiveness of the proposed framework.The results indicated that the proposed method exhibited satisfactory performance and could be effectively utilized to improve the crown control precision.
基金supported by the National Natural Science Foundation of China(No.52061135101 and 52001078)the German Research Foundation(DFG,No.448318292)+3 种基金the Technology Innovation Guidance Special Foundation of Shaanxi Province(No.2023GXLH-085)the Fundamental Research Funds for the Central Universities(No.D5000240161)the Project of Key areas of innovation team in Shaanxi Province(No.2024RS-CXTD-20)The author Yingchun Xie thanks the support from the National Key R&D Program(No.2023YFE0108000).
文摘1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-sized particles(5-50μm)to fabricate coatings[1-3].CS has been extensively used in a variety of coating applications,such as aerospace,automotive,energy,medical,marine,and others,to provide protection against high temperatures,corrosion,erosion,oxidation,and chemicals[4,5].Nowadays,the technical interest in CS is twofold:(i)as a repair process for damaged components,and(ii)as a solid-state additive manufacturing process.Compared to other fusion-based additive manufacturing(AM)technologies,Cold Spray Additive Manufacturing(CSAM)is a new member of the AM family that can enable the fabrication of deposits without undergoing melting.The chemical composition has been largely preserved from the powder to the deposit due to the minimal oxidation.The significant advantages of CSAM over other additive manufacturing processes include a high production rate,unlimited deposition size,high flexibility,and suitability for repairing damaged parts.
基金the financial support for this research from the Program for the Program for young backbone teachers in Universities of Henan Province(No.2021GGJS007).
文摘Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-silience study face many challenges.The introduction of digital twin technology provides an innovative solution for the resilience study of smart water distribution networks,which can more effectively support the network’s real-time monitoring and intelligent control.This paper proposes a digital twin architecture of smart water dis-tribution networks,laying the foundation for the resilience assessment of water distribution networks.Based on this,a performance evaluation model based on user satisfaction is proposed,which can more intuitively and effectively reflect the performance of urban water supply services.Meanwhile,we propose a method to quantify the importance of water distribution pipes’residual resilience,considering the time value to optimize the re-covery sequence of failed pipes and develop targeted preventive maintenance strategies.Finally,to validate the effectiveness of the proposed method,this paper applies it to a water distribution network.The results show that the proposed method can significantly improve the resilience and enhance the overall resilience of smart urban water distribution networks.
