公路隧道火灾通风控制对于抑制高温烟气扩散与保障运营安全至关重要。针对现有控制系统依赖均匀断面风速假设而难以直接检测和控制的问题,提出以通风量为控制输入、以顶棚温度为反馈的方案,实现烟气逆流的实时有效控制。通过建立通风作...公路隧道火灾通风控制对于抑制高温烟气扩散与保障运营安全至关重要。针对现有控制系统依赖均匀断面风速假设而难以直接检测和控制的问题,提出以通风量为控制输入、以顶棚温度为反馈的方案,实现烟气逆流的实时有效控制。通过建立通风作用下顶棚温度动态响应系统,基于一阶惯性加纯滞后系统(First Order Plus Dead Time,FOPDT)模型假设,采用阶跃通风量激发系统响应,研究在不同火灾和不同通风量下顶棚温度的响应特性。根据通风量对顶棚温度的调控作用,提出动态温度响应分类方法,得出不同火灾热释放速率下顶棚温度响应的通风量范围。结果表明,阶跃通风量在接近临界值(分类Ⅱ)时,烟气逆流长度得到控制,顶棚温度对通风量的响应符合FOPDT系统特征,模型决定系数R2可达0.96;增益系数|K|与通风量呈正相关,滞后时间τ与通风量呈负相关。展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,...The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.展开更多
文摘公路隧道火灾通风控制对于抑制高温烟气扩散与保障运营安全至关重要。针对现有控制系统依赖均匀断面风速假设而难以直接检测和控制的问题,提出以通风量为控制输入、以顶棚温度为反馈的方案,实现烟气逆流的实时有效控制。通过建立通风作用下顶棚温度动态响应系统,基于一阶惯性加纯滞后系统(First Order Plus Dead Time,FOPDT)模型假设,采用阶跃通风量激发系统响应,研究在不同火灾和不同通风量下顶棚温度的响应特性。根据通风量对顶棚温度的调控作用,提出动态温度响应分类方法,得出不同火灾热释放速率下顶棚温度响应的通风量范围。结果表明,阶跃通风量在接近临界值(分类Ⅱ)时,烟气逆流长度得到控制,顶棚温度对通风量的响应符合FOPDT系统特征,模型决定系数R2可达0.96;增益系数|K|与通风量呈正相关,滞后时间τ与通风量呈负相关。
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant No.52278415the National Key Research and Development Program of China under Grant No.2022YFC3801104+2 种基金Hebei Provincial Department of Education Project under Grant No.QN2025304the Innovation Fund Project of Hebei University of Engineering under Grant No.SJ2401002066the Sichuan Science and Technology Program under Grant No.2023YFS0407。
文摘The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.
