The increasing integration of renewable energy sources and power electronic devices has significantly increased the complexity of modern power systems,making modeling and simulation challenging due to multi-time scale...The increasing integration of renewable energy sources and power electronic devices has significantly increased the complexity of modern power systems,making modeling and simulation challenging due to multi-time scale dynamics and multi-physics coupling.To address these challenges,this paper proposes a multi-level simulation framework based on unified energy flow theory.The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports,ensuring compatibility with artificial intelligence algorithms.By integrating AI techniques,such as back propagation neural networks,the framework predicts variables with high computational complexity,improving accuracy and simulation efficiency.A multi-level simulation architecture leveraging Field Programmable Gate Arrays(FPGAs)enables faster-than-real-time system-level simulation and real-time component-level modeling with time resolution as small as 5 nanoseconds.A DC microgrid case study with photovoltaic generation,battery storage,and power electronic converters demonstrates the proposed method,achieving up to a 500×speedup over traditional Simulink models while maintaining high accuracy.The results confirm the framework’s ability to capture multiphysics interactions,optimize energy distribution,and ensure system stability under dynamic conditions,providing an efficient and scalable solution for advanced DC microgrid simulations.展开更多
The traffic and user have significant impacts on the electric vehicle(EV)charging load but are not considered in the existing research.We propose a novel integrated simulation framework considering the traffic,the use...The traffic and user have significant impacts on the electric vehicle(EV)charging load but are not considered in the existing research.We propose a novel integrated simulation framework considering the traffic,the user,and power grid as well as the EV traveling,parking and charging based on cellular automaton(CA).The traffic is modeled by the traffic module of the proposed framework based on CA,while the power grid and user are modeled in the EV charging module.The traffic flow,user’s charging preference,user’s charging satisfaction,and the total supply capability(TSC)in the surveyed region are considered in the proposed framework.Two cases are carried out to show the interactions between the user and power grid.It is shown that the proposed framework can accurately simulate the interactions among traffic situation,user's behavior and TSC,which are significantly lacking in the existing research.The proposed framework is scalable in considering additional interrelated elements.展开更多
A computational study was firstly performed in this work to examine the applicability of an acid-functionalized metal-organic framework(MOF), Ui O-66(Zr)-(COOH)2, in membrane-based H2S/CH4 separation. The results show...A computational study was firstly performed in this work to examine the applicability of an acid-functionalized metal-organic framework(MOF), Ui O-66(Zr)-(COOH)2, in membrane-based H2S/CH4 separation. The results show that this MOF could be potentially interesting when being used as the pure membrane material for the separation of the mixture with low H2 S concentration. Further, the performance of 10 different mixed matrix membranes(MMMs) on the basis of the MOF was predicted by combing the molecular simulation data and the Maxwell permeation model. The results indicate that using this MOF as filler particles in MMMs can signi ficantly enhance the permeation performance of pure polymers. The findings obtained in this work may be helpful in facilitating the application of this promising MOF for practical desulfurization process of fuel gas.展开更多
Simulation is an important method to evaluate future computer systems. Currently microprocessor architecture has switched to parallel, but almost all simulators remained at sequential stage, and the advantages brought...Simulation is an important method to evaluate future computer systems. Currently microprocessor architecture has switched to parallel, but almost all simulators remained at sequential stage, and the advantages brought by multi-core or many-core processors cannot be utilized. This paper presents a parallel simulator engine （SimK） towards the prevalent SMP/CMP platform, aiming at large-scale fine-grained computer system simulation. In this paper, highly efficient synchronization, communication and buffer management policies used in SimK are introduced, and a novel lock-free scheduling mechanism that avoids using any atomic instructions is presented. To deal with the load fluctuation at light load case, a cooperated dynamic task migration scheme is proposed. Based on SimK, we have developed large-scale parallel simulators HppSim and HppNetSim, which simulate a full supercomputer system and its interconnection network respectively. Results show that HppSim and HppNetSim both gain sound speedup with multiple processors, and the best normalized speedup reaches 14.95X on a two-way quad-core server.展开更多
Improving simulation performance using activity tracking has attracted attention in the modeling field in recent years.The reference to activity has been successfully used to predict and promote the simulation perform...Improving simulation performance using activity tracking has attracted attention in the modeling field in recent years.The reference to activity has been successfully used to predict and promote the simulation performance.Tracking activity,however,uses only the inherent performance information contained in the models.To extend activity prediction in modeling,we propose the activity enhanced modeling with an activity meta-model at the meta-level.The meta-model provides a set of interfaces to model activity in a specific domain.The activity model transformation in subsequence is devised to deal with the simulation difference due to the heterogeneous activity model.Finally,the resource-aware simulation framework is implemented to integrate the activity models in activity-based simulation.The case study shows the improvement brought on by activity-based simulation using discrete event system specification(DEVS).展开更多
Simulation modelers,engineers and managers are faced with new challenges at large-scale complex simulation application development.To reduce the difficulty of developing such simulation applications,the simulation env...Simulation modelers,engineers and managers are faced with new challenges at large-scale complex simulation application development.To reduce the difficulty of developing such simulation applications,the simulation environment is required to be extensible,reusable and composable.In order to promote the reusability from coarse-grained federate to fine-grained components,this paper proposes a modeling and simulation environment which consists of component-based architecture,modeling methods,and simulation services to support and simplify the process of complex simulation application construction.Moreover,a standard process and simulation tools are developed to ensure the rapid and effective development of simulation applications.展开更多
A software framework is an infrastructure or architecture intended to enable the integration and interoperation of a set of software components.A specialized type of software frameworks are those specifically designed...A software framework is an infrastructure or architecture intended to enable the integration and interoperation of a set of software components.A specialized type of software frameworks are those specifically designed to support the composition of models or other components within a simulation system.Such frameworks are intended to simplify the process of assembling a complex model or simulation system from simpler component models as well as to promote the reuse of the component models.Several different types of software frameworks for model composition have been designed and implemented;the various framework types have different component types,processes for composing models,and intended applications.The different framework types and the implemented examples of them vary widely in terms of features and capabilities.Comparing alternative frameworks so as to assess their likely utility for a specific application has heretofore been largely subjective and qualitative,and consequently of uncertain reliability.To address this issue an assessment methodology specifically designed for comparing model composition frameworks is developed and explained.The methodology employs a quantitative metric based on a set of well-defined criteria relating to the features and capabilities of simulation frameworks and is intended to be objective and quantitative.The methodology is illustrated and demonstrated by applying it to a set of existing model composition frameworks.The assessment results suggest that using the methodology can,at a minimum,improve the objectivity and reliability of framework selection decisions.The assessment criteria may also be useful as guidelines when designing and developing a framework.展开更多
Autonomous driving systems(ADS)are at the forefront of technological innovation,promising enhanced safety,efficiency,and convenience in transportation.This study investigates the potential of end-to-end reinforcement ...Autonomous driving systems(ADS)are at the forefront of technological innovation,promising enhanced safety,efficiency,and convenience in transportation.This study investigates the potential of end-to-end reinforcement learning(RL)architectures for ADS,specifically focusing on a Go-To-Point task involving lane-keeping and navigation through basic urban environments.The study uses the Proximal Policy Optimization(PPO)algorithm within the CARLA simulation environment.Traditional modular systems,which separate driving tasks into perception,decision-making,and control,provide interpretability and reliability in controlled scenarios but struggle with adaptability to dynamic,real-world conditions.In contrast,end-to-end systems offer a more integrated approach,potentially enhancing flexibility and decision-making cohesion.This research introduces CARLA-GymDrive,a novel framework integrating the CARLA simulator with the Gymnasium API,enabling seamless RL experimentation with both discrete and continuous action spaces.Through a two-phase training regimen,the study evaluates the efficacy of PPO in an end-to-end ADS focused on basic tasks like lane-keeping and waypoint navigation.A comparative analysis with modular architectures is also provided.The findings highlight the strengths of PPO in managing continuous control tasks,achieving smoother and more adaptable driving behaviors than value-based algorithms like Deep Q-Networks.However,challenges remain in generalization and computational demands,with end-to-end systems requiring extensive training time.While the study underscores the potential of end-to-end architectures,it also identifies limitations in scalability and real-world applicability,suggesting that modular systems may currently be more feasible for practical ADS deployment.Nonetheless,the CARLA-GymDrive framework and the insights gained from PPO-based ADS contribute significantly to the field,laying a foundation for future advancements in AD.展开更多
基金support by National Natural Science Foundation of China,Grant agreement No:52107216.
文摘The increasing integration of renewable energy sources and power electronic devices has significantly increased the complexity of modern power systems,making modeling and simulation challenging due to multi-time scale dynamics and multi-physics coupling.To address these challenges,this paper proposes a multi-level simulation framework based on unified energy flow theory.The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports,ensuring compatibility with artificial intelligence algorithms.By integrating AI techniques,such as back propagation neural networks,the framework predicts variables with high computational complexity,improving accuracy and simulation efficiency.A multi-level simulation architecture leveraging Field Programmable Gate Arrays(FPGAs)enables faster-than-real-time system-level simulation and real-time component-level modeling with time resolution as small as 5 nanoseconds.A DC microgrid case study with photovoltaic generation,battery storage,and power electronic converters demonstrates the proposed method,achieving up to a 500×speedup over traditional Simulink models while maintaining high accuracy.The results confirm the framework’s ability to capture multiphysics interactions,optimize energy distribution,and ensure system stability under dynamic conditions,providing an efficient and scalable solution for advanced DC microgrid simulations.
基金This work was supported by the National Natural Science Foundation of China(No.51936003).
文摘The traffic and user have significant impacts on the electric vehicle(EV)charging load but are not considered in the existing research.We propose a novel integrated simulation framework considering the traffic,the user,and power grid as well as the EV traveling,parking and charging based on cellular automaton(CA).The traffic is modeled by the traffic module of the proposed framework based on CA,while the power grid and user are modeled in the EV charging module.The traffic flow,user’s charging preference,user’s charging satisfaction,and the total supply capability(TSC)in the surveyed region are considered in the proposed framework.Two cases are carried out to show the interactions between the user and power grid.It is shown that the proposed framework can accurately simulate the interactions among traffic situation,user's behavior and TSC,which are significantly lacking in the existing research.The proposed framework is scalable in considering additional interrelated elements.
基金Supported by the National Key Basic Research Program of China(2013CB733503)the National Natural Science Foundation of China(21136001,21276009 and 21322603)the Program for New Century Excellent Talents in University(NCET-12-0755)
文摘A computational study was firstly performed in this work to examine the applicability of an acid-functionalized metal-organic framework(MOF), Ui O-66(Zr)-(COOH)2, in membrane-based H2S/CH4 separation. The results show that this MOF could be potentially interesting when being used as the pure membrane material for the separation of the mixture with low H2 S concentration. Further, the performance of 10 different mixed matrix membranes(MMMs) on the basis of the MOF was predicted by combing the molecular simulation data and the Maxwell permeation model. The results indicate that using this MOF as filler particles in MMMs can signi ficantly enhance the permeation performance of pure polymers. The findings obtained in this work may be helpful in facilitating the application of this promising MOF for practical desulfurization process of fuel gas.
基金Supported by the National Natural Science Foundation of China under Grant No. 60633040the National High Technology Research and Development 863 Program of China under Grant Nos. 2006AA01A102 and 2007AA01Z115
文摘Simulation is an important method to evaluate future computer systems. Currently microprocessor architecture has switched to parallel, but almost all simulators remained at sequential stage, and the advantages brought by multi-core or many-core processors cannot be utilized. This paper presents a parallel simulator engine （SimK） towards the prevalent SMP/CMP platform, aiming at large-scale fine-grained computer system simulation. In this paper, highly efficient synchronization, communication and buffer management policies used in SimK are introduced, and a novel lock-free scheduling mechanism that avoids using any atomic instructions is presented. To deal with the load fluctuation at light load case, a cooperated dynamic task migration scheme is proposed. Based on SimK, we have developed large-scale parallel simulators HppSim and HppNetSim, which simulate a full supercomputer system and its interconnection network respectively. Results show that HppSim and HppNetSim both gain sound speedup with multiple processors, and the best normalized speedup reaches 14.95X on a two-way quad-core server.
基金Project supported by the National Natural Science Foundation of China(Nos.71303252 and 91024030)
文摘Improving simulation performance using activity tracking has attracted attention in the modeling field in recent years.The reference to activity has been successfully used to predict and promote the simulation performance.Tracking activity,however,uses only the inherent performance information contained in the models.To extend activity prediction in modeling,we propose the activity enhanced modeling with an activity meta-model at the meta-level.The meta-model provides a set of interfaces to model activity in a specific domain.The activity model transformation in subsequence is devised to deal with the simulation difference due to the heterogeneous activity model.Finally,the resource-aware simulation framework is implemented to integrate the activity models in activity-based simulation.The case study shows the improvement brought on by activity-based simulation using discrete event system specification(DEVS).
基金supported by grant 61104057 from the Natural Science Foundation of China.
文摘Simulation modelers,engineers and managers are faced with new challenges at large-scale complex simulation application development.To reduce the difficulty of developing such simulation applications,the simulation environment is required to be extensible,reusable and composable.In order to promote the reusability from coarse-grained federate to fine-grained components,this paper proposes a modeling and simulation environment which consists of component-based architecture,modeling methods,and simulation services to support and simplify the process of complex simulation application construction.Moreover,a standard process and simulation tools are developed to ensure the rapid and effective development of simulation applications.
文摘A software framework is an infrastructure or architecture intended to enable the integration and interoperation of a set of software components.A specialized type of software frameworks are those specifically designed to support the composition of models or other components within a simulation system.Such frameworks are intended to simplify the process of assembling a complex model or simulation system from simpler component models as well as to promote the reuse of the component models.Several different types of software frameworks for model composition have been designed and implemented;the various framework types have different component types,processes for composing models,and intended applications.The different framework types and the implemented examples of them vary widely in terms of features and capabilities.Comparing alternative frameworks so as to assess their likely utility for a specific application has heretofore been largely subjective and qualitative,and consequently of uncertain reliability.To address this issue an assessment methodology specifically designed for comparing model composition frameworks is developed and explained.The methodology employs a quantitative metric based on a set of well-defined criteria relating to the features and capabilities of simulation frameworks and is intended to be objective and quantitative.The methodology is illustrated and demonstrated by applying it to a set of existing model composition frameworks.The assessment results suggest that using the methodology can,at a minimum,improve the objectivity and reliability of framework selection decisions.The assessment criteria may also be useful as guidelines when designing and developing a framework.
基金supported by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.
文摘Autonomous driving systems(ADS)are at the forefront of technological innovation,promising enhanced safety,efficiency,and convenience in transportation.This study investigates the potential of end-to-end reinforcement learning(RL)architectures for ADS,specifically focusing on a Go-To-Point task involving lane-keeping and navigation through basic urban environments.The study uses the Proximal Policy Optimization(PPO)algorithm within the CARLA simulation environment.Traditional modular systems,which separate driving tasks into perception,decision-making,and control,provide interpretability and reliability in controlled scenarios but struggle with adaptability to dynamic,real-world conditions.In contrast,end-to-end systems offer a more integrated approach,potentially enhancing flexibility and decision-making cohesion.This research introduces CARLA-GymDrive,a novel framework integrating the CARLA simulator with the Gymnasium API,enabling seamless RL experimentation with both discrete and continuous action spaces.Through a two-phase training regimen,the study evaluates the efficacy of PPO in an end-to-end ADS focused on basic tasks like lane-keeping and waypoint navigation.A comparative analysis with modular architectures is also provided.The findings highlight the strengths of PPO in managing continuous control tasks,achieving smoother and more adaptable driving behaviors than value-based algorithms like Deep Q-Networks.However,challenges remain in generalization and computational demands,with end-to-end systems requiring extensive training time.While the study underscores the potential of end-to-end architectures,it also identifies limitations in scalability and real-world applicability,suggesting that modular systems may currently be more feasible for practical ADS deployment.Nonetheless,the CARLA-GymDrive framework and the insights gained from PPO-based ADS contribute significantly to the field,laying a foundation for future advancements in AD.
