Geological storage and utilization of CO_(2)involve complex interactions among Thermo-hydromechanical-chemical(THMC)coupling processes,which significantly affect storage integrity and efficiency.To address the challen...Geological storage and utilization of CO_(2)involve complex interactions among Thermo-hydromechanical-chemical(THMC)coupling processes,which significantly affect storage integrity and efficiency.To address the challenges in accurately simulating these coupled phenomena,this paper systematically reviews recent advances in the mathematical modeling and numerical solution of THMC coupling in CO_(2)geological storage.The study focuses on the derivation and structure of governing and constitutive equations,the classification and comparative performance of fully coupled,iteratively coupled,and explicitly coupled solution methods,and the modeling of dynamic changes in porosity,permeability,and fracture evolution induced by multi-field interactions.Furthermore,the paper evaluates the capabilities,application scenarios,and limitations of major simulation platforms,including TOUGH,CMG-GEM,and COMSOL.By establishing a comparative framework integrating model formulations and solver strategies,this work clarifies the strengths and gaps of current approaches and contributes to the development of robust,scalable,and mechanism-oriented numerical models for long-term prediction of CO_(2)behavior in geological formations.展开更多
The Self-Propping Phase-transition Fracturing Technology(SPFT)represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources,e...The Self-Propping Phase-transition Fracturing Technology(SPFT)represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources,especially in the context of a carbon-constrained sustainable future.SPFT involves the coupling of Thermal,Hydraulic,Mechanical,and Chemical(THMC)fields,which makes it challenging to understand the mechanism and path of hydraulic fracture propagation.This study addresses these challenges by developing a set of THMC multifield coupling models based on SPFT parameters and the physical/chemical characteristics of the Phase-transition Fracturing Fluid System(PFFS).An algorithm,integrating the Finite Element Method,Discretized Virtual Internal Bonds,and Element Partition Method(FEM-DVIB-EPM),is proposed and validated through a case study.The results demonstrate that the FEM-DVIB-EPM coupling algorithm reduces complexity and enhances solving efficiency.The length of the hydraulic fracture increases with the quantity and displacement of PFFS,and excessive displacement may result in uncontrolled fracture height.Within the parameters considered,a minimal difference in fracture length is observed when the PFFS amount exceeds 130 m^(3),that means the fracture length tends to stabilize.This study contributes to understanding the hydraulic fracture propagation mechanism induced by SPFT,offering insights for optimizing hydraulic fracturing technology and treatment parameters.展开更多
This paper proposes a novel parallel hybrid deep reinforcement learning(DRL)approach to address the real-time energy management problem for microgrid(MG).As the proposed approach can directly approximate a discrete-co...This paper proposes a novel parallel hybrid deep reinforcement learning(DRL)approach to address the real-time energy management problem for microgrid(MG).As the proposed approach can directly approximate a discrete-continuous hybrid policy,it does not require the discretization of continuous actions like regular DRL approaches,which avoids accuracy degradation and the curse of dimensionality.In addition,a novel experience-sharing-based parallel technique is further developed for the proposed approach to accelerate the training speed and enhance the training robustness.Finally,a safety projection technique is introduced and incorporated into the proposed approach to improve the decision feasibility.Comparative numerical simulations with several existing MG real-time energy management approaches(i.e.,myopic policy,model predictive control,and regular DRL approaches)demonstrate the effectiveness and superiority of the proposed approach.展开更多
Recent advances in autonomous system simulation platforms have significantly enhanced the safe and scalable testing of driving policies.Although existing simulators have greatly accelerated development by providing co...Recent advances in autonomous system simulation platforms have significantly enhanced the safe and scalable testing of driving policies.Although existing simulators have greatly accelerated development by providing controlled testing environments,they face limitations in addressing the evolving needs of future transportation research,particularly in enabling effective human−artificial intelligence(human−AI)collaboration and modeling socially aware driving agents.This study introduces Sky-Drive,a novel distributed multiagent simulation platform that addresses these limitations through four key innovations:(1)a distributed architecture for synchronized simulation across multiple terminals;(2)a multimodal human-in-the-loop framework that integrates diverse sensors to collect rich behavioral data;(3)a human−AI collaboration mechanism that supports continuous and adaptive knowledge exchange;and(4)a digital twin framework for constructing high-fidelity virtual replicas of real-world transportation environments.Sky-Drive supports diverse applications,such as autonomous vehicle-human road user interaction modeling,human-in-the-loop training,socially aware reinforcement learning,personalized driving development,and customized scenario generation.Future extensions will incorporate foundation models for context-aware decision support and hardware-in-theloop testing for real-world validation.By bridging scenario generation,data collection,algorithm training,and hardware integration,Sky-Drive has the potential to become a foundational platform for the next generation of human-centered and socially aware autonomous transportation system research.展开更多
The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically co...The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.展开更多
The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch ...The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.展开更多
基金supported by the China Postdoctoral Science Foundation(No.2024M752803)the National Natural Science Foundation of China(No.52179112)the Open Fund of National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University)(No.PLN2023-02)。
文摘Geological storage and utilization of CO_(2)involve complex interactions among Thermo-hydromechanical-chemical(THMC)coupling processes,which significantly affect storage integrity and efficiency.To address the challenges in accurately simulating these coupled phenomena,this paper systematically reviews recent advances in the mathematical modeling and numerical solution of THMC coupling in CO_(2)geological storage.The study focuses on the derivation and structure of governing and constitutive equations,the classification and comparative performance of fully coupled,iteratively coupled,and explicitly coupled solution methods,and the modeling of dynamic changes in porosity,permeability,and fracture evolution induced by multi-field interactions.Furthermore,the paper evaluates the capabilities,application scenarios,and limitations of major simulation platforms,including TOUGH,CMG-GEM,and COMSOL.By establishing a comparative framework integrating model formulations and solver strategies,this work clarifies the strengths and gaps of current approaches and contributes to the development of robust,scalable,and mechanism-oriented numerical models for long-term prediction of CO_(2)behavior in geological formations.
基金supported by the National Natural Science Foundation of China(52179112)the Open Fund of National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University)(PLN2023-02)Fundamental Research Funds for the Central Universities(2021FZZX001-14).
文摘The Self-Propping Phase-transition Fracturing Technology(SPFT)represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources,especially in the context of a carbon-constrained sustainable future.SPFT involves the coupling of Thermal,Hydraulic,Mechanical,and Chemical(THMC)fields,which makes it challenging to understand the mechanism and path of hydraulic fracture propagation.This study addresses these challenges by developing a set of THMC multifield coupling models based on SPFT parameters and the physical/chemical characteristics of the Phase-transition Fracturing Fluid System(PFFS).An algorithm,integrating the Finite Element Method,Discretized Virtual Internal Bonds,and Element Partition Method(FEM-DVIB-EPM),is proposed and validated through a case study.The results demonstrate that the FEM-DVIB-EPM coupling algorithm reduces complexity and enhances solving efficiency.The length of the hydraulic fracture increases with the quantity and displacement of PFFS,and excessive displacement may result in uncontrolled fracture height.Within the parameters considered,a minimal difference in fracture length is observed when the PFFS amount exceeds 130 m^(3),that means the fracture length tends to stabilize.This study contributes to understanding the hydraulic fracture propagation mechanism induced by SPFT,offering insights for optimizing hydraulic fracturing technology and treatment parameters.
基金supported in part by the National Natural Science Foundation of China(No.51977081)the Natural Science Foundation of Guangdong Province(No.2022A1515011193).
文摘This paper proposes a novel parallel hybrid deep reinforcement learning(DRL)approach to address the real-time energy management problem for microgrid(MG).As the proposed approach can directly approximate a discrete-continuous hybrid policy,it does not require the discretization of continuous actions like regular DRL approaches,which avoids accuracy degradation and the curse of dimensionality.In addition,a novel experience-sharing-based parallel technique is further developed for the proposed approach to accelerate the training speed and enhance the training robustness.Finally,a safety projection technique is introduced and incorporated into the proposed approach to improve the decision feasibility.Comparative numerical simulations with several existing MG real-time energy management approaches(i.e.,myopic policy,model predictive control,and regular DRL approaches)demonstrate the effectiveness and superiority of the proposed approach.
基金funded by the U.S.Department of Transportation(No.#69A3552348305)。
文摘Recent advances in autonomous system simulation platforms have significantly enhanced the safe and scalable testing of driving policies.Although existing simulators have greatly accelerated development by providing controlled testing environments,they face limitations in addressing the evolving needs of future transportation research,particularly in enabling effective human−artificial intelligence(human−AI)collaboration and modeling socially aware driving agents.This study introduces Sky-Drive,a novel distributed multiagent simulation platform that addresses these limitations through four key innovations:(1)a distributed architecture for synchronized simulation across multiple terminals;(2)a multimodal human-in-the-loop framework that integrates diverse sensors to collect rich behavioral data;(3)a human−AI collaboration mechanism that supports continuous and adaptive knowledge exchange;and(4)a digital twin framework for constructing high-fidelity virtual replicas of real-world transportation environments.Sky-Drive supports diverse applications,such as autonomous vehicle-human road user interaction modeling,human-in-the-loop training,socially aware reinforcement learning,personalized driving development,and customized scenario generation.Future extensions will incorporate foundation models for context-aware decision support and hardware-in-theloop testing for real-world validation.By bridging scenario generation,data collection,algorithm training,and hardware integration,Sky-Drive has the potential to become a foundational platform for the next generation of human-centered and socially aware autonomous transportation system research.
基金supported in part by the National Natural Science Foundation of China under Grant 52432012in part by the Shanghai Science and Technology Project with 25ZR1402508。
文摘The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.
基金supported by State Key Laboratory of HVDC under Grant SKLHVDC-2021-KF-09.
文摘The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.
