To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development...To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.展开更多
A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a n...A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a new mitigation method is urgently expected to be explored.This study proposed a novel asymptotic linear method(ALM)for micro pressure wave(MPW)mitigation to achieve a constant gradient of initial c ompression waves(ICWs),via a study with various open ratios on hoods.The properties of ICWs and MPWs under various open ratios of hoods were analyzed.The results show that as the open ratio increases,the MPW amplitude at the tunnel exit initially decreases before rising.At the open ratio of 2.28%,the slope of the ICW curve is linearly coincident with a supposed straight line in the ALM,which further reduces the MPW amplitude by 26.9%at 20 m and 20.0%at 50 m from the exit,as compared to the unvented hood.Therefore,the proposed method effectively mitigates MPW and quickly determines the upper limit of alleviation for the MPW amplitude at a fixed train-tunnel operation condition.All achievements provide a ne w potential measure for the adaptive design of tunnel hoods.展开更多
电压质量低和系统网损较大是困扰偏远地区配电系统的两个主要问题,也是近几年配电网研究的重点。基于美国电科院(EPRI)开发的Open Distribution System Simulator(Open DSS)平台,进行了三相不平衡配电系统的潮流计算,分析了三相平衡负...电压质量低和系统网损较大是困扰偏远地区配电系统的两个主要问题,也是近几年配电网研究的重点。基于美国电科院(EPRI)开发的Open Distribution System Simulator(Open DSS)平台,进行了三相不平衡配电系统的潮流计算,分析了三相平衡负荷与不平衡负荷对电压和有功网损的影响。以Matlab为平台,设计了Open DSS的COM接口,使得在Matlab程序中能够反复调取Open DSS对配电网进行三相潮流计算,由此建立了配电网无功优化和低电压治理决策模型,将电压分段罚函数引入扩展的无功优化目标函数中,并将高斯罚函数用于处理无功优化变量,使得最终得到的解尽可能地逼近离散值。基于粒子群优化算法(PSO)实现了无功优化问题的求解。实际仿真算例验证了所建立的无功优化模型和算法的有效性与可行性。展开更多
文摘To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.
基金Project(24A0006)supported by the Key Project of Scientific Research Fund of Hunan Provincial Department of Education,ChinaProject(2024JJ5430)supported by the Natural Science Foundation of Hunan Province,ChinaProjects(2024JK2045,2023RC3061)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a new mitigation method is urgently expected to be explored.This study proposed a novel asymptotic linear method(ALM)for micro pressure wave(MPW)mitigation to achieve a constant gradient of initial c ompression waves(ICWs),via a study with various open ratios on hoods.The properties of ICWs and MPWs under various open ratios of hoods were analyzed.The results show that as the open ratio increases,the MPW amplitude at the tunnel exit initially decreases before rising.At the open ratio of 2.28%,the slope of the ICW curve is linearly coincident with a supposed straight line in the ALM,which further reduces the MPW amplitude by 26.9%at 20 m and 20.0%at 50 m from the exit,as compared to the unvented hood.Therefore,the proposed method effectively mitigates MPW and quickly determines the upper limit of alleviation for the MPW amplitude at a fixed train-tunnel operation condition.All achievements provide a ne w potential measure for the adaptive design of tunnel hoods.
文摘电压质量低和系统网损较大是困扰偏远地区配电系统的两个主要问题,也是近几年配电网研究的重点。基于美国电科院(EPRI)开发的Open Distribution System Simulator(Open DSS)平台,进行了三相不平衡配电系统的潮流计算,分析了三相平衡负荷与不平衡负荷对电压和有功网损的影响。以Matlab为平台,设计了Open DSS的COM接口,使得在Matlab程序中能够反复调取Open DSS对配电网进行三相潮流计算,由此建立了配电网无功优化和低电压治理决策模型,将电压分段罚函数引入扩展的无功优化目标函数中,并将高斯罚函数用于处理无功优化变量,使得最终得到的解尽可能地逼近离散值。基于粒子群优化算法(PSO)实现了无功优化问题的求解。实际仿真算例验证了所建立的无功优化模型和算法的有效性与可行性。
