矿井火灾是煤矿重大灾害之一，产生的高温烟气发展蔓延会严重威胁安全生产。火灾灾变区域的快速密闭和自动隔离能够有效控制火势蔓延，减少高温-有害烟气对救援人员的影响。火势发展过程特征信息的实时研判和态势预测，对于矿井火灾应急控制至关重要。本文针对矿井火区封闭过程中烟气与温度变化规律、态势预测和快速密闭控制方法等关键问题开展研究，分析不同封闭速率条件下巷道火灾的火源行为特性、烟气沉降-逆流和顶棚下方最高烟气温度动态演变规律，建立巷道火灾封闭过程中的火灾态势预测模型，提出巷道火灾动态感知及快速密闭控制方法，对矿井火灾防治具有重要的理论与现实意义。

建立了1:10缩尺寸的巷道火灾实验平台，研究了在4种封闭速率下的巷道火灾烟气和温度的变化特征。封闭过程中，高温烟气对柴油燃料表面的热反馈和火源附近氧气的抑制作用，导致燃料质量损失速率随时间呈现非线性复合函数关系。当封闭速率为Δt=0 s时火灾熄灭时间为200 s~700 s，Δt=30 s时火灾熄灭时间为1300 s~1700 s，表明封闭速率越快，火源热释放速率达到峰值和衰减阶段的时间越短。火灾区域不完全封闭时氧气浓度大于17%，完全封闭后氧气浓度降低至13%~15%导致火源熄灭。

采用N-百分比法分析了烟气沉降和逆流长度的变化特征。封闭过程中烟气在火源的远端发生沉降和填充，封闭速率与烟气前锋到达火源上游封闭端的时间成正比。讨论了封闭过程中顶棚下方最高烟气温升的影响因素，完全封闭后顶棚下方最高烟气温度均出现在火源正上方。顶棚纵向温度衰减服从指数函数，提出了不同封闭速率条件下顶棚纵向温度衰减预测模型。

利用FDS模拟了巷道火灾封闭过程中烟气蔓延与温度分布规律。分析了不同封闭速率下烟气蔓延和沉降规律，得到了封闭速率越快烟气的沉降和填充速度越快。高温烟气撞击封闭端产生的回流效应加速了烟气沉降，造成火源向火风压低势能区域倾斜。随着封闭速率和火源热释放速率增大，封闭火区内烟气层内部之间气流的频繁运动，导致混合气体中O₂体积分数快速下降，产生大量的CO气体。当封闭断面面积达到50%后，火源中心顶棚温度出现急剧升高，超过75%后，顶棚下方出现最高烟气温度。封闭速率越快时火源熄灭的临界时间超前完全封闭时间。

采用四层结构的长短期记忆网络（LSTM）和门控循环单元网络（GRU）深度学习算法，利用矿井火灾发展过程中的温度、CO和O₂体积分数等特征指标，建立了以历史数据为基础的矿井火区火源燃烧动态演变特征信息的态势预测模型。引入粒子群优化算法（PSO）对基于LSTM和GRU网络时间序列算法中的隐藏层神经元数量和学习率等超参数进行优化调整，提高了预测模型的准确性和鲁棒性。

提出了矿井火灾动态感知及快速隔离控制方法。研发了矿井巷道火灾灾变区域信息动态感知、快速应急隔离及远程控制系统，通过全尺寸模拟巷道现场试验测试，得到了火灾发展与封闭控制过程中烟气流动与温度变化等特征参数，快速密闭时间为5 min~8 min；开展了快速隔离密闭装置抗冲击实验，抗冲击强度大于0.8 MPa。通过在国家能源集团宁夏煤业公司羊场湾煤矿2号井的现场试验研究，快速密闭气囊与巷道壁面贴合紧密，漏气率小于5%，满足密闭要求，能够实现巷道快速密闭、自动隔离与远程控制。

Mine fire is one of the major disasters in coal mine, the development and spread of high temperature smoke will seriously threaten the safety of production. Fast sealing and automatic isolation of fire disaster areas can effectively control the spread of fire and reduce the impact of high-temperature and harmful smoke on rescue workers. It is very important for mine fire emergency control to study and predict the disaster characteristic information of fire development process in real time. In this thesis, the key issues such as change law of smoke and temperature, situation prediction and fast sealing control method in the process of mine fire sealing were studied. The behavior characteristics of fire source, the dynamic evolution law of smoke sediment-countercurrent and the maximum smoke temperature under the ceiling under different sealing rates of roadway fire were analyzed, and the fire situation prediction model in the process of roadway fire sealing was established. The dynamic sensing and fast sealing control methods of roadway fire were proposed. It has important theoretical and practical significance for the prevention and control of mine fires.

A 1:10 reduced roadway fire test platform was established to study the change characteristics of smoke and temperature in roadway fire under four sealing rates. In the sealing process, the thermal feedback of the high temperature smoke on the diesel fuel surface and the inhibition of oxygen near the fire source result in the nonlinear complex function relationship of the fuel mass loss rate with time. When the sealing rate was Δt=0 s, the extinguishing time was 200 s-700 s, and when Δt=30 s, the extinguishing time was 1300 s-1700 s, indicating that the faster the sealing rate was, the shorter the time for the heat release rate of fire source to reach the stable and peak stage. When the fire area was not completely closed, the oxygen concentration was greater than 17%. After the fire area was completely closed, the oxygen concentration was reduced to 13%-15%, resulting in the extinguishing of the fire source.

The N-percentage method was used to analyze the variation characteristics of smoke settlement and back-layer length. In the sealing process, the smoke settled and filled at the far end of the fire source. The sealing rate was proportional to the time for the smoke front to reach the upstream closed end of the fire source. The influencing factors of the maximum smoke temperature rise under the ceiling during the sealing process were analyzed. After complete seal off, the highest smoke temperature under the ceiling would appear right above the fire source. The longitudinal temperature attenuation under the ceiling obeyed the exponential function, and a prediction model of ceiling longitudinal temperature attenuation under different sealing rates was presented.

The law of smoke movement and temperature distribution in the process of mine roadway fire sealing was simulated by FDS. The law of smoke spreading and settling under different sealing rate was analyzed. It is found that the faster the sealing rate, the faster the settling and filling speed of smoke. The backflow effect of high temperature smoke colliding with the closed end accelerated the smoke settlement and caused the fire source to tilt towards the area of potential energy reduced by the fire wind. With the increase of the sealing rate and the heat release rate of the fire source, the frequent movement of the airflow between the smoke layer in the closed fire zone led to the rapid decrease of the volume fraction of O₂ in the gas mixture and the generation of a large amount of CO. When the closed section area reaches 50%, the temperature of the central ceiling of the fire source increased sharply, and the temperature reached the highest when it exceeded 75%. The faster the sealing rate is, the critical time of fire extinguishing is ahead of the complete sealing time.

The situation prediction model of mine fire dynamic evolution characteristic information based on historical data was established by using the four-layer Long and Short Term Memory network (LSTM) and Gate Recurrent Unit (GRU) deep learning algorithm, and the characteristic indexes of temperature, CO and O₂ in the development process of mine fire. Particle swarm optimization algorithm (PSO) was introduced to optimize and adjust the number of hidden layer neurons and learning rate in LSTM and GRU network time series algorithms, which improved the accuracy and robustness of the prediction model.

The dynamic sensing and rapid isolation control methods of mine fire were proposed. The system of dynamic sensing, rapid emergency isolation and remote control of fire disaster area in mine roadway information was developed. The characteristic parameters such as smoke flow and temperature change during the fire development and sealing control were obtained through full-scale field test of simulated roadway. The fast sealing time was 5 min~8 min. The impact test of rapid isolation sealing device was carried out, and the impact strength was greater than 0.8 MPa. Through the field test study in the Yangchangwan No. 2 Coal Mine of Ningxia Coal Industry Company of National Energy Group, the fast sealing air bag fitted closely with the roadway, and the leakage rate was less than 5%, which meets the sealing requirements and can realize the fast sealing, automatic isolation and remote control of the roadway fire.

[1] 王凯, 蒋曙光, 张卫青, 等. 矿井火灾应急救援系统的数值模拟及应用研究[J]. 煤炭学报, 2012, 37(5): 857–862.

[2] Shi G, Wang G, Ding P, et al. Model and simulation analysis of fire development and gas flowing influenced by fire zone sealing in coal mine[J]. Process Safety and Environmental Protection, 2021, 149: 631–642.

[3] 郭军, 刘荫, 金永飞, 等. 矿井胶带火灾巷道环境多参数时空演化规律[J]. 西安科技大学学报, 2019, 39(1): 21–27.

[4] 郝海清, 王凯, 张春玉, 等. 矿井皮带巷火灾风烟流场–区–网演化与调控规律[J]. 中国矿业大学学报, 2021, 50(4): 716–724.

[5] 周福宝. 矿井火灾下行风流逆转的突变动力学分析[J]. 辽宁工程技术大学学报, 2006, 25(2): 164–167.

[6] 梁强, 李炎锋, 李俊梅. 狭长通道内火灾烟气流动特性实验研究[J]. 消防科学与技术, 2016, 35(9): 1221–1224.

[7] 张培红, 俞艳秋, 赵鹏程, 等. 狭长受限空间火灾演化规律的试验研究[J]. 消防科学与技术, 2015, 34(6): 719–721.

[8] 王欢, 齐庆杰, 姜海洋, 等. 挡烟垂壁作用下狭长通道内烟气输运特征研究[J]. 中国安全生产科学技术, 2017, 13(6): 150–156.

[9] Wang K, Jiang S, Ma X, et al. Information fusion of plume control and personnel escape during the emergency rescue of external–caused fire in a coal mine[J]. Process Safety and Environmental Protection, 2016, 103: 46–59.

[10] Wang G, Shi J, Yang Y, et al. Experimental study on the exogenous fire evolution and smoke migration during the fire zone sealing period of the coal mining face[J]. Fuel, 2022, 320: 123879.

[11] 姚勇征, 张文明, 吴兵, 等. 巷道火灾对通风系统影响的全尺寸实验与模拟[J]. 中国矿业大学学报, 2021, 50(4): 709–715.

[12] Wang G, Shi G, Wang Y, et al. Numerical study on the evolution of methane explosion regions in the process of coal mine fire zone sealing[J]. Fuel, 2021, 289: 119744.

[13] 王德明, 邵振鲁, 朱云飞. 煤矿热动力重大灾害中的几个科学问题[J]. 煤炭学报, 2021, 46(1): 57–64.

[14] Geng J, Sun Q, Zhang Y, et al. Non–destructive testing and temperature distribution of coal mine roadway lining structure under exogenous fire[J]. Journal of Loss Prevention in the Process Industries, 2018, 55: 144–151.

[15] 李敏, 林志军, 王德明, 等. 我国煤矿重特大火灾事故统计分析[J]. 中国安全科学学报, 2023, 33(1): 115–121.

[16] 齐庆杰, 王欢, 董子文, 等. 矿井胶带运输巷火灾蔓延规律的数值模拟研究[J]. 中国安全科学学报, 2016, 26(10): 36–41.

[17] 任晓伟, 王振兴, 王锐, 等. 煤矿电缆火灾危险性研究[J]. 煤矿安全, 2022, 53(9): 151–156.

[18] 马砺, 张鹏宇, 李超华, 等. 不同封堵条件下巷道火灾烟气温度场预测模型[J]. 西安科技大学学报, 2021, 41(2): 213–220.

[19] Sosa E, Thompson G, Barbero E. Experimental investigation of initial deployment of inflatable structures for sealing of rail tunnels[J]. Tunnelling and Underground Space Technology, 2017, 69: 37–51.

[20] 肖长亮, 周二元, 翟建斌, 等. 煤矿井下快速密闭装置的门体结构研究[J]. 煤矿机械, 2019, 40(3): 105–107.

[21] 李来保, 王永西, 张益民. 封堵战术在铁路隧道火灾扑救中的运用[J]. 消防科学与技术, 2011, 30(10): 955–959.

[22] 王旭东, 王德明, 司卫彬, 等. 屯宝矿1192封闭工作面火区治理关键技术[J]. 煤矿安全, 2015, 46(12): 70–72.

[23] 牛会永, 邓军, 周心权, 等. 煤矿火区封闭过程中瓦斯积聚规律研究及危险性分析[J]. 中南大学学报, 2013, 44(9): 3918–3924.

[24] 陈立, 张英华, 黄志安, 等. 掘进巷道火灾惰化置换快速灭火技术研究[J]. 煤炭技术, 2017, 36(2): 145–147.

[25] Ji J. Bi Y, Venkatasubbaiah K, et al. Influence of aspect ratio of tunnel on smoke temperature distribution under ceiling in near field of fire source[J]. Applied Thermal Engineering, 2016, 106: 1094–1102.

[26] Zhao S, Liu F, Wang F, et al. Experimental studies on fire–induced tem-perature distribution below ceiling in a longitudinal ventilated metro tunnel[J]. Tunnelling and Underground Space Technology, 2018, 72: 281–293.

[27] 张辛亥, 郭戎, 白枫桐, 等. 无机发泡充填材料密闭技术[J]. 煤矿安全, 2015, 46(11): 74–76.

[28] Jiang L, Zhang Y, Huang Z, et al. Experimental study of fast sealing airbag in simulating tunnel[J]. Procedia Engineering, 2012, 45: 780–785.

[29] 刘尚明, 马砺, 魏高明, 等. 井下灾变风烟流快速密闭气囊特性[J]. 中国矿业大学学报, 2021, 50(4): 735–743.

[30] 程彩霞, 周心权. 巷道木材火灾试验与数值模拟对比研究[J]. 中国安全科学学报, 2010, 20(7): 55–62.

[31] 李宗翔，王天明，张明乾，等．矿井巷道淋湿蒸发换热系数构建及风流温度计算[J]. 煤炭学报，2017，42(12) : 3176–3181.

[32] 王凯, 郝海清, 蒋曙光, 等. 矿井火灾风烟流区域联动与智能调控系统研究[J]. 工矿自动化, 2019, 45(7): 21–27.

[33] Zhang S, Cheng X, Zhu K, et al. Experimental study on curved flame characteristics under longitudinal ventilation in a subway tunnel[J]. Applied Thermal Engineering, 2017, 114: 733–743.

[34] Hu L, Liu S, Peng W, et al. Experimental study on burning rates of square/rectangular gasoline and methanol pool fires under longitudinal air flow in a wind tunnel[J]. Journal of hazardous materials, 2009, 169: 972–979.

[35] 易欣, 范晶, 马砺, 等. 纵向通风对地铁区间隧道火灾温度特性影响[J]. 中南大学学报, 2019, 50(12): 3163–3170.

[36] Ingason H, Li Y. Model scale tunnel fire tests with longitudinal ventilation[J]. Fire Safety Journal, 2010, 45: 371–384.

[37] Li Y, Lei B, Ingason, H. Study of critical velocity and backlayering length in longitudinally ventilated tunnel fires[J]. Fire Safety Journal, 2010, 45: 361–370.

[38] 姜学鹏, 胡杰, 徐志胜, 等. 铁路隧道火灾烟气逆流的计算模型[J]. 中南大学学报, 2011, 42(9): 2837–2842.

[39] Zhang S, Yao Y, Zhu K, et al. Prediction of smoke back–layering length under different longitudinal ventilations in the subway tunnel with metro train[J]. Tunnelling and Underground Space Technology, 2016, 53: 13–21.

[40] Ko G, Kim S, Ryou H. An experimental study on the effect of slope on the critical velocity in tunnel fires[J]. Fire Safety Journal, 2010, 28: 27–47.

[41] 王峰, 张路华, 袁松, 等. 高海拔公路隧道火灾烟气控制临界风速研究[J]. 中国公路学报, 2022, 35(5): 153–160.

[42] Li Y, Lei B, Ingason H. Theoretical and experimental study of critical velocity for smoke control in a tunnel cross–passage[J]. Fire Technology, 2013, 49: 435–449.

[43] Ji J, Wan H, Li K, et al. A numerical study on upstream maximum temperature in inclined urban road tunnel fires[J]. International Journal of Heat and Mass Transfer, 2015, 88: 516–526.

[44] 易亮, 杨洋, 徐志胜, 等. 纵向通风公路隧道火灾拱顶烟气最高温度试验研究[J]. 燃烧科学与技术. 2011, 17(2): 109–114.

[45] Guedri K, Borjini M, Jeguirim M, et al. Numerical study of radiative heat transfer effects on a complex configuration of rack storage fire[J]. Energy, 2011, 36: 2984–2996.

[46] 刘方, 翁庙成, 余龙星, 等. 地铁区间隧道顶部热烟气温度分布[J]. 中南大学学报, 2015, 46(2): 661–669.

[47] Li L, Cheng X, Wang X, et al. Temperature distribution of fire–induced flow along tunnels under natural ventilation[J]. Journal of fire sciences, 2012, 30: 122–137.

[48] 赵胜中. 纵向通风作用下隧道火灾烟气输运规律研究[D]. 重庆大学, 2019.

[49] Hu L, Huo R, Peng W, et al. On the maximum smoke temperature under the ceiling in tunnel fires[J], Tunnelling and Underground Space Technology, 2006, 21: 650–655.

[50] Ingason H, Li Y, LÖnnermark A. Runehamar tunnel fire tests[J]. Fire Safety Journal, 2015, 71: 134–149.

[51] Shi C, Li J, Xu X. Full–scale tests on smoke temperature distribution in long-large subway tunnels with longitudinal mechanical ventilation[J], Tunnelling and Underground Space Technology, 2021, 109: 103784.

[52] 周延. 一个新的水平隧道火灾烟气逆流层长度模型研究[J]. 中国矿业大学学报, 2007, 36(5): 569–572.

[53] Chow W, Gao Y, Zhao J, et al. Smoke movement in tilted tunnel fires with longitudinal ventilation[J]. Fire Safety Journal, 2015, 75: 14–22.

[54] 陈俊沣, 程辉航, 魏旋, 等. 隧道火灾全尺寸实验中温度测量误差[J]. 清华大学学报, 2022, 62(10): 1618–1625.

[55] 翁庙成, 余龙星, 刘方. 地铁区间隧道的烟气逆流长度与临界风速[J]. 华南理工大学学报, 2014, 42(6): 121–128.

[56] 王文才, 公维刚, 姜宇鸿, 等. 水平巷道火灾时烟气逆流临界风速的研究[J]. 中国煤炭, 2013, 39(7): 101–104.

[57] Thomas P. The movement of smoke in horizontal passages against an air flow[M]. BRE Trust. Fire Research Station, 1968.

[58] Lin P, Zuo C, Xiong Y, et al. An experimental study of the self–extinction mechanism of fire in tunnels[J]. Tunnelling and Underground Space Technology, 2021, 109: 103780.

[59] Han J, Geng P, Wang Z, et al. Effects of fire–blockage distance on pool fire burning behavior and thermal temperature profiles in a naturally ventilated tunnel[J]. Tunnelling and Underground Space Technology, 2021, 117: 104131.

[60] Yao Y, He K, Peng M, et al. The maximum gas temperature rises beneath the ceiling in a longitudinal ventilated tunnel fire[J]. Tunnelling and Underground Space Technology, 2021, 108: 103672.

[61] Cong W, Shi L, Shi Z, et al. Effect of train fire location on maximum smoke temperature beneath the subway tunnel ceiling[J]. Tunnelling and Underground Space Technology, 2020, 97: 103282.

[62] Wang Y, Li Y, Yan P, et al. Maximum temperature of smoke beneath ceiling in tunnel fire with vertical shafts[J]. Tunnelling and Underground Space Technology, 2015, 50: 189–198.

[63] Hua N, Tessari A, Khorasani E. Characterizing damage to a concrete liner during a tunnel fire[J]. Tunnelling and Underground Space Technology, 2021, 109: 103761.

[64] 樊晋元, 陈鸿伟. 中国动力煤的着火温度与着火热的分布规律[J]. 化工学报, 2015, 66(10): 4170–4176.

[65] 王秋红, 方向, 李州昊. 4种典型易燃烟煤的着火特性分析[J]. 中国安全生产科学技术, 2020, 16(9): 89–95.

[66] Chen C, Zhang Y, Lei P, et al. A study for predicting the maximum gas temperature beneath ceiling in sealing tactics against tunnel fire[J]. Tunnelling and Underground Space Technology, 2020, 98: 103275.

[67] Chen C, Xiao H, Wang N, et al. Experimental investigation of pool fire behavior to different tunnel–end ventilation opening areas by sealing[J]. Tunnelling and Underground Space Technology, 2017, 63: 106–117.

[68] Pan R, Zhu G, Wang P, et al. Experimental investigation of maximum temperature and longitudinal temperature distribution induced by linear fire in portals–sealed tunnel[J]. Case Studies in Thermal Engineering, 2021, 23: 100804.

[69] Yao Y, He K, Peng M, et al. Maximum gas temperature rise beneath the ceiling in a portals–sealed tunnel fire[J]. Tunnelling and Underground Space Technology, 2018, 80: 10–15.

[70] Yao Y, Cheng X, Shi L, et al. Experimental study on the effects of initial sealing time on fire behaviors in channel fires[J]. International Journal of Thermal Sciences, 2018, 125: 273–282.

[71] You S, Wang K, Shi J, et al. Self–extinction of methanol fire in tunnel with different configuration of blocks[J]. Tunnelling and Underground Space Technology, 2020, 98: 103343.

[72] Liu H, Zhu G, Pan R, et al. Experimental investigation of fire temperature distribution and ceiling temperature prediction in closed utility tunnel[J]. Case Studies in Thermal Engineering, 2019, 14: 100493.

[73] Shi J, Zuo C, Xiong Y, et al. Experimental study of different sealing ratios on the self–extinction of tunnel fires[J]. Tunnelling and Underground Space Technology, 2021, 112: 103894.

[74] Chen L, Liu X, Lan Y, et al. Experimental study on the smoke movement and flame extension length characteristics of strong fire plumes in a sealed tunnel[J]. Tunnelling and Underground Space Technology, 2023, 131: 104780.

[75] 马砺, 李超华, 张鹏宇, 等. 封堵比例对隧道火灾顶棚温度分布的影响[J]. 中国安全科学学报, 2020, 30(2): 79–85.

[76] 黄萍, 秦亮, 余龙星, 等. 综合管廊电缆桥架火灾的温度场分布特性[J]. 福州大学学报, 2021, 49(4): 544–550.

[77] 王宝宁. 管廊内火源高度与防火封堵耦合作用下温度分布[J]. 消防科学与技术, 2021, 40(3): 337–339.

[78] Han J, Liu F, Wang F, et al. Study on the smoke movement and downstream temperature distribution in a sloping tunnel with one closed portal[J]. International Journal of Thermal Sciences, 2020, 149: 106165.

[79] Xu G, Zhu G, Pan R, et al. Investigation on temperature distribution under the coupling action of transverse position and fire sealing of linear fire in tunnel[J]. Case Studies in Thermal Engineering, 2021, 26: 101032.

[80] Han J, Geng P, Wang Z, et al. Effect of ceiling extraction on the smoke spreading characteristics and temperature profiles in a tunnel with one closed end[J]. Tunnelling and Underground Space Technology, 2022, 119: 104236.

[81] An W, Tang Y, Liang K, et al. Study on temperature distribution and CO diffusion induced by cable fire in L–shaped utility tunnel[J]. Sustainable Cities and Society, 2020, 62: 102407.

[82] 周福宝, 王德明. 矿井火灾烟流滚退距离的数值模拟[J]. 中国矿业大学学报, 2004, 33(5): 9–13.

[83] Saito S, Yamauchi Y. Numerical study of the influence of tunnel wall properties on ceiling jet temperature in tunnel fires[J]. Tunnelling and Underground Space Technology, 2021, 116: 104087.

[84] 李士戎. 移动火源对隧道温度场分布及烟气流动影响规律研究[D]. 西安科技大学, 2013.

[85] 李义杰, 邬长福, 高宗杰, 等. 矿井巷道火灾烟气流动影响因素模拟分析[J]. 矿业研究与开发, 2017, 37(2): 18–21.

[86] Fan C, Li X, Mu Y, et al. Smoke movement characteristics under stack effect in a mine laneway fire[J]. Applied Thermal Engineering, 2017, 110: 70–79.

[87] 王建国, 武睿萌, 殷雄, 等. 综采工作面进风巷火灾数值模拟研究[J]. 矿业安全与环保, 2019, 46(5): 7–11.

[88] 刘业娇, 薛俊华, 袁亮, 等. 巷道火灾时期烟流参数变化规律模拟分析[J]. 中国矿业, 2018, 27(8): 144–150.

[89] Xu T, Zhao D, Tao H, et al. Extended CFD models for numerical simulation of tunnel fire under natural ventilation: Comparative analysis and experimental verification[J]. Case Studies in Thermal Engineering, 2022, 31: 101815.

[90] 王星, 师江涛, 柴伦磊, 等. 考虑人员逃生公路隧道火灾控制风速研究[J]. 地下空间与工程学报, 2019, 15(1): 277–286.

[91] 齐庆杰, 王欢, 周新华, 等. 线火源与点火源在巷道火灾中烟流特性对比[J]. 辽宁工程技术大学学报, 2017, 36 (2): 147–153.

[92] 文虎, 张铎, 郑学召. 煤矿平巷火灾数值模拟及其特征参数研究[J]. 煤炭科学技术, 2017, 45(4): 62–67.

[93] Zhang S, Wu Z, Zhang R, et al. Dynamic numerical simulation of coal mine fire for escape capsule installation[J]. Safety Science, 2012, 50: 600–606.

[94] 付巍. 巷道截面突变对烟气蔓延的影响数值模拟[J]. 煤矿安全, 2020, 51(4): 181–184.

[95] 李兆周, 梁天水, 钟委, 等. 封闭空间火焰自熄灭时间数值模拟研究[J]. 工业安全与环保, 2015, 41(2): 41–43.

[96] Kayili S, Yozgatligil A, Eralp O. An experimental study on the effects of blockage ratio and ventilation velocity on the heat release rate of tunnel fires[J]. Journal of Fire Sciences, 2011, 29(6): 555–575.

[97] Sykora J, Jaruskova D, Sejnoha M, et al. Fire risk analysis focused on damage of the tunnel lining[J]. Fire Safety Journal, 2018, 95: 51–65.

[98] 董炳燕, 吴晋湘, 黄有波. 封堵比例对隧道火灾温度影响的数值模拟研究[J]. 消防科学与技术, 2018, 37(8): 1051–1054.

[99] 张洪杰, 丁玉洁, 姜学鹏. 阻塞物对矿井火灾临界风速影响研究[J]. 安全与环境学报, 2016, 16(6): 44–49.

[100] 周西华, 李诚玉, 张丽丽, 等. 封闭过程中火区气体运移规律的数值模拟[J]. 中国地质灾害与防治学报, 2015, 26(2): 116–122.

[101] 牛会永, 邓湘陵, 李石林, 等. 封闭顺序对煤矿火区气体分布规律的影响[J]. 中南大学学报, 2016, 47(9): 3239–3245.

[102] Wang W, Zhu Z, Jiao Z, et al. Characteristics of fire and smoke in the natural gas cabin of urban underground utility tunnels based on CFD simulations[J]. Tunnelling and Underground Space Technology, 2021, 109: 103748.

[103] Huang Y, Li Y, Dong B, et al. Numerical investigation on the maximum ceiling temperature and longitudinal decay in a sealing tunnel fire[J]. Tunnelling and Underground Space Technology, 2018, 72: 120–130.

[104] 马砺, 刘顺, 李超华, 等. 巷道火灾密闭过程中烟气温度及流动特性研究[J]. 中国安全科学学报, 2021, 17(2): 46–52.

[105] Yao Y, Wang R, Xia Z, et al. Numerical study of the characteristics of smoke spread in tunnel fires during construction and method for improvement of smoke control[J]. Case Studies in Thermal Engineering, 2022, 34: 102043.

[106] Shibani, Salehi F, Baalisampang T, et al. Numerical modeling towards the safety assessment of multiple hydrogen fires in confined areas[J]. Process Safety and Environmental Protection, 2022, 160: 594–609.

[107] 张英喆, 何丽辉, 任飞, 等. 综合管廊电缆火灾窒息灭火试验与数值模拟研究[J]. 中国安全生产科学技术, 2023, 19(2): 173–179.

[108] Li L, Zhu D, Gao Z, et al. A study on longitudinal distribution of temperature rise and carbon monoxide concentration in tunnel fires with one opening portal[J]. Case Studies in Thermal Engineering, 2021, 28: 101535.

[109] Wang Z, Han J, Wang J, et al. Temperature distribution in a blocked tunnel with one closed portal under natural ventilation[J]. Tunnelling and Underground Space Technology, 2021, 109: 103752.

[110] 杨戌初. 社交网络中突发事件的态势感知算法研究与实现[D]. 北京交通大学, 2018.

[111] Lenders V, Tanner A, Blarer A. Gaining an edge in cyberspace with advanced situational awareness[J]. Security and Privacy IEEE, 2015, 13(2): 65–74.

[112] Endsley M R. Design and evaluation for situation awareness enhancement[C] //Proceedings of the Human Factors Society Annual Meeting. Los Angeles, CA: Sage Publications, 1988: 97–101.

[113] Bass T. Intrusion Detection Systems and Multisensor Data Fusion: CreatingCyberspace Situational Awareness[J]. Communications of the ACM, 2000, 43(4): 99–105.

[114] Bass T, Gruber D. A glimpse into the future of id[J]. The Magazine of usenix and sage. 1999, 24(3): 40–49.

[115] 刘晟源, 林振智, 李金城, 等. 电力系统态势感知技术研究综述与展望[J]. 电力系统自动化, 2020, 44(3): 229–239.

[116] 李艳, 王纯子, 黄光球, 等. 网络安全态势感知分析框架与实现方法比较[J]. 电子学报, 2019, 47(4): 927–945.

[117] 丁雨淋, 何小波, 朱庆, 等. 实时威胁态势感知的室内火灾疏散路径动态优化方法[J]. 测绘学报, 2016, 45(12): 1464–1475.

[118] 唐尧, 王立娟, 邓琮, 等. 高分遥感技术助力森林火灾应急扑救及隐患预判——以冕宁“4·20”森林火灾为例[J]. 遥感学报, 2021, 25(9): 2015–2026.

[119] 黄蔓云, 卫志农, 孙国强, 等. 基于历史数据挖掘的配电网态势感知方法[J]. 电网技术, 2017, 41(4): 1139–1145.

[120] 唐辉. 基于MEMS技术带式输送机火灾监测系统研究[J]. 煤炭技术, 2016, 35(9): 219–221.

[121] 肖国强, 范新丽, 马砺, 等. 矿井火灾环境信息动态感知与应急隔离系统[J]. 工矿自动化, 2021, 47(9): 65–69.

[122] 付文俊. 基于红外非接触缆式线型的煤矿高压电缆温度监测技术[J]. 煤矿安全, 2020, 51(11): 106–108.

[123] 邓军, 肖旸, 陈晓坤, 等. 矿井火灾多源信息融合预警方法的研究[J]. 采矿与安全工程学报, 2011, 28(4): 638–643.

[124] 李军, 张书林, 龚仲强, 等. 基于多传感信息融合的输送带运输巷道火灾预警方法[J]. 煤矿机械, 2017, 38(10): 132–135.

[125] 程永新. 煤矿带式输送机火灾光纤传感检测技术研究[J]. 煤炭科学技术, 2019, 47(2): 131–135.

[126] 张军杰. 煤矿束管监测系统的现状与发展趋势[J]. 煤矿安全, 2019, 50(12): 89–92.

[127] 董绍朴, 刘剑, 李艳昌, 等. 基于主成分分析法的东荣一矿煤层自然发火指标气体实验研究[J]. 矿业安全与环保, 2019, 46(2): 1–5.

[128] Sun B, Liu X, Xu Z, et al.Temperature data–driven fire source estimation algorithm of the underground pipe gallery[J]. International Journal of Thermal Sciences, 2022, 171: 107247.

[129] 孙继平, 孙雁宇, 范伟强. 基于可见光和红外图像的矿井外因火灾识别方法[J]. 工矿自动化, 2019, 45(5): 1–5.

[130] 韩斌, 吴一全, 倪康. 利用改进CV模型分割煤矿井下早期火灾图像[J]. 中国矿业大学学报, 2018, 47(2): 429–435.

[131] 冯路佳, 王慧琴, 王可, 等. 基于目标区域的卷积神经网络火灾烟雾识别[J]. 激光与光电子学进展, 2020, 57(16): 83–91.

[132] 吴雪, 宋晓茹, 高嵩, 等. 基于数据增强的卷积神经网络火灾识别[J]. 科学技术与工程, 2020, 20(3): 1113–1117.

[133] 王仲根, 聂文艳. 红外成像技术在煤矿井下传送系统温度保护中的应用[J]. 矿山机械, 2010, 38(16): 62–64.

[134] Sun B, Xu Z, Zhou H. A multiple back propagation neural network fusion algorithm for ceiling temperature prediction in tunnel fires[J]. Engineering Structures, 2023, 280: 115601.

[135] Hong Y, Kang J, Fu C. Rapid prediction of mine tunnel fire smoke movement with machine learning and supercomputing techniques[J]. Fire Safety Journal, 2022, 127: 103492.

[136] 马砺, 张鹏宇, 郭睿智, 等. 巷道火灾密闭过程烟气温度预测的GA–SVM模型[J]. 中国矿业大学学报, 2021, 50(4): 641–648.

[137] Sun B, Hu Z, Liu X, et al. A physical model–free ant colony optimization network algorithm and full scale experimental investigation on ceiling temperature distribution in the utility tunnel fire[J]. International Journal of Thermal Sciences, 2022, 174: 107436.

[138] Liu X, Sun B, Xu Z, et al. An adaptive Particle Swarm Optimization algorithm for fire source identification of the utility tunnel fire[J]. Fire Safety Journal, 2021, 126: 103486.

[139] 丁雨淋, 何小波, 朱庆, 等. 实时威胁态势感知的室内火灾疏散路径动态优化方法[J].测绘学报, 2016, 45(12): 1464–1475.

[140] 李爽, 李丁炜, 犹梦洁. 煤矿安全态势感知预测系统设计及关键技术[J]. 煤矿安全, 2020, 51(5): 244–248.

[141] 汪腾蛟, 周西华, 白刚, 等. 煤矿火灾诱发瓦斯爆炸危险性预测[J]. 煤炭学报, 2020, 45(12): 4104–4110.

[142] 李祥春, 蒋颖, 李梅生. 巷道火灾时期流场及瓦斯浓度变化规律数值模拟研究[J]. 煤炭科学技术, 2019, 47(5): 119–125.

[143] 张美金, 田宇驰, 方志朋. 矿井主运输系统火灾预测的RS–SVM模型[J]. 测控技术, 2018, 37(9): 34–37.

[144] Esmatullah D, Mustafa O. Application of Fuzzy Logic for Predicting of Mine Fire in Underground Coal Mine[J]. Safety and Health at Work, 2020, 11: 322–334.

[145] 朱令起, 郭立稳, 周心权, 等. 密闭火区火源燃烧状态的综合判定[J]. 煤矿安全, 2010, 41(7): 48–51.

[146] 谭波, 朱红青, 王海燕, 等. 巷道高冒封闭火区燃烧状态及表面温度场演变规律[J]. 中南大学学报, 2014, 45(3): 946–951.

[147] 王书芹. 基于深度学习的瓦斯时间序列预测与异常检测[D]. 中国矿业大学, 2018.

[148] 王雨虹. 煤与瓦斯突出态势感知方法研究[D]. 辽宁工程技术大学, 2020.

[149] Kumari K , Dey P , Kumar C , et al. UMAP and LSTM based fire status and explosibility prediction for sealed–off area in underground coal mine[J]. Process Safety and Environmental Protection, 2021, 146: 837–852.

[150] 周福宝, 刘应科, 张仁贵. 新型气囊式自动封闭装备研究[J]. 中国矿业大学学报, 2008, 37(5): 604–607.

[151] 李飞, 肖洋. 一种新型水墙式密闭装置的研究[J]. 煤炭技术, 2010, 29(3): 118–120.

[152] 张楠. 煤矿井下火灾区远距离自动封闭装置[J]. 煤矿安全, 2018, 49(4): 103–105.

[153] 姜文忠, 肖长亮. 煤矿井下灾区快速密闭技术及装备研究[J]. 煤炭科学技术, 2019, 47(11): 231–238.

[154] 宋先明, 王振平. 矿山救护用快速密闭技术[J]. 煤矿安全, 2012, 43(8): 70–73.

[155] 张英华, 李刚强, 黄志安, 等. 新型矿井火灾快速密闭气囊研制[J]. 采矿与安全工程学报, 2011, 28(4): 649–654.

[156] 丁维国. 煤矿火区远程控制快速隔离密闭装置[J]. 煤矿安全, 2019, 50(5): 100–102.

[157] Shao S, Wu C, Hao M, et al. A novel coating technology for fast sealing of air leakage in underground coal mines[J]. International Journal of Mining Science and Technology, 2021, 31: 313–320.

[158] 周西华, 李昂, 宋东平, 等. 煤矿火区快速封闭泄爆门研究[J]. 中国安全生产科学技术, 2016, 12(10): 125–129.

[159] Tao L, Zhang Y, Hou K, et al. Experimental study on temperature distribution and smoke control in emergency rescue stations of a slope railway tunnel with semi–transverse ventilation[J]. Tunnelling and Underground Space Technology, 2020, 106: 103606.

[160] Beyler C, Hirschler M. Handbook of fire protection engineering, Chapter 7[J]. Industrial Safety and Environmental Protection, 2016, 29: 487–500.

[161] Roh J, Yang S, Ryou H, et al. An experimental study on the effect of ventilation velocity on burning rate in tunnel fires–heptane pool fire case[J]. Building and Environment, 2008, 43: 1225–1231.

[162] 陶亮亮, 曾艳华, 彭俊钦等. 火源位置对铁路隧道救援站内拱顶温度纵向分布的影响[J]. 中国铁道科学, 2022, 43(1):101–109.

[163] 姚勇征. 受限出口边界下隧道火灾火行为和烟气输运规律研究[D]. 中国科学技术大学, 2019.

[164] 傅培舫, 周怀春. 巷道火灾过程中燃烧速率和释热速率的预测[J]. 燃烧科学与技术, 2006(5): 408–412.

[165] 沈云鸽, 王德明. 基于FDS的矿井巷道火灾烟气致灾的数值模拟[J]. 煤矿安全, 2020, 51(2): 183–187.

[166] 李贺, 田丽, 曾钢, 等. 基于FDS的风速对矿井火灾蔓延规律的影响研究[J]. 中国安全生产科学技术, 2022, 18(5): 143–149.

[167] Ji J, Fan C, Zhong W, et al. Experimental investigation on influence of different transverse fire locations on maximum smoke temperature under the tunnel ceiling[J]. International Journal of Heat and Mass Transfer, 2012, 55: 4817–4826.

[168] 刘剑, 李雪冰, 白崇国, 等. 管道风流PIV实验中示踪粒子特性[J]. 辽宁工程技术大学学报, 2016, 35(10): 1057–1062.

[169] Stewart J, Phylaktou H, Andrews G, et al. Evaluation of CFD simulations of transient pool fire burning rates[J]. Journal of Loss Prevention in the Process Industries, 2021, 71: 104495.

[170] 赵威风. 狭长空间油池火燃烧特性的实验与数值模拟研究[D]. 中国科学技术大学, 2013.

[171] 郑源. 综合管廊火灾烟气运动规律及其通风优化研究[D]. 中国科学技术大学, 2020.

[172] 黎昌海, 陆守香, 袁满, 等. 封闭空间池火火焰游走实验研究[J]. 中国科学技术大学学报, 2010，40(7): 751–756.

[173] 张培红, 俞艳秋, 赵鹏程, 等. 狭长受限空间火灾演化规律的试验研究[J]. 消防科学与技术, 2015, 34(6): 719–721.

[174] 胡隆华. 隧道火灾烟气蔓延的热物理特性研究[D]. 中国科学技术大学, 2006.

[175] 纪杰. 地铁站火灾烟气流动及通风控制模式研究[D]. 中国科学技术大学, 2008.

[176] 李宗翔, 王雅迪, 高光超, 等. 巷道火灾火焰局部阻力模型构建及参数识别[J]. 煤炭学报, 2015, 40(4): 909–914.

[177] 赵建贺. 长通道内热浮力驱动流流动特征研究[D]. 哈尔滨工程大学, 2012.

[178] Cooper L, Harkleroad M, Quintiere J, et al. An experimental study of upper hot layer stratification in full–scale multiroom fire scenarios[J]. Journal of Heat Transfer, 1982, 104: 741–749.

[179] Vantelon J, Gueizim A, Quach D. Investigation of fire–induced smoke movement in tunnels and stations: an application to the Paris Metro[J]. Fire Safety Science, 1991, 3: 907–918.

[180] Weng M, Lu X, Liu F, et al. Study on the critical velocity in a sloping tunnel fire under longitudinal ventilation[J]. Applied Thermal Engineering, 2016, 94: 422–434.

[181] Oka Y, Kakae N, Imazeki K, et al. Temperature property of ceiling jet in an inclined tunnel[J]. Procedia Engineering, 2013, 62: 234–241.

[182] Alpert R. Turbulent ceiling–jet induced by large–scale fires[J]. Combustion Science and Technology. 1975, 11: 197–213.

[183] Kurioka H, Oka Y, satoh H, et al. Fire properties in near field of square fire source with longitudinal ventilation in tunnels[J]. Fire Safety Journal, 2003, 38: 319–340.

[184] Li Y, Lei B, Ingason H. The maximum temperature of buoyancy–driven smoke flow beneath the ceiling in tunnel fires[J]. Fire Safety Journal, 2011, 46: 204–210.

[185] Hu L, Tang W, Chen L, et al. A non–dimensional global correlation of maximum gas temperature beneath ceiling with different blockageefire distance in a longitudinal ventilated tunnel[J]. Applied Thermal Engineering. 2013, 56: 77–82.

[186] Hu L, Huo R, Li Y, et al. Full–scale burning tests on studying smoke temperature and velocity along a corridor[J]. Tunnelling and Underground Space Technology, 2005, 20: 223–229.

[187] Oka Y, Imazeki O. Temperature distribution within a ceiling jet propagating in an inclined flat–ceilinged tunnel with natural ventilation[J]. Fire Safety Journal, 2015, 71: 20–33.

[188] Kashef A, Yuan Z, Lei B. Ceiling temperature distribution and smoke diffusion in tunnel fires with natural ventilation[J]. Fire Safety Journal, 2013, 62: 249–255.

[189] 陈长坤, 王玮玉, 康恒, 等. 不同火源面积下隧道火灾温度场试验与数值模拟分析[J]. 中国公路学报, 2018, 31(6): 235–243.

[190] Khattri S. From small–scale tunnel fire simulations to predicting fire dynamics in realistic tunnels[J]. Tunnelling and Underground Space Technology, 2017, 61: 198–204.

[191] 徐浩祯, 赵威风, 吕思嘉. 隧道双火源火灾燃烧特性的实验研究[J]. 工程热物理学报, 2020, 41(5): 1254–1260.

[192] Johansson N, Ronchi E, Scozzari R, et al. The use of multi–zone modelling for tunnel fires[J]. Tunnelling and Underground Space Technology, 2023, 134: 104996.

[193] 王明年, 崔鹏, 郭晓晗, 等. 高海拔大纵坡铁路隧道火灾特性数值模拟研究[J]. 现代隧道技术, 2019, 56(S2): 1–8.

[194] Jia Y, Fan X, Deng Y, et al. Study on the temperature characteristics caused by two fires in a tunnel based on multi–layer zone model[J]. Tunnelling and Underground Space Technology, 2023, 135: 105045.

[195] 王蕾, 夏永旭, 姚毅, 等. 基于FDS的公路隧道大型客车火灾数值模拟及分析[J]. 公路工程, 2020, 45(6): 231–237.

[196] Rengel B, Mata C, Pastor E, et al. A priori validation of CFD modelling of hydrocarbon pool fires[J]. Journal of Loss Prevention in the Process Industries, 2018, 56: 18–31.

[197] Chaudhary A, Gupta A, Kumar S, et al. Pool fires of jatropha biodiesel and their blends with petroleum diesel[J]. Experimental Thermal and Fluid Science, 2019, 101: 175–185.

[198] Siddapureddy S, Prebhu S. Experimental and numerical simulation studies on heat transfer to calorimeters engulfed in diesel pool fires[J]. Journal of Fire Sciences, 2017, 32(5): 156–176.

[199] Yuan L, Zhou L, Smith A. Modeling carbon monoxide spread in underground mine fires [J]. Applied Thermal Engineering, 2016, 100: 1319–1326.

[200] Ji J, Zhong W, Li KY, et al. A simplified calculation method on maximum smoke temperature under the ceiling in subway station fires[J]. Tunnelling and Underground Space Technology, 2011, 26: 490–496.

[201] Gao Z, Li L, Zhong W, et al. Characterization and prediction of ceiling temperature propagation of thermal plume in confined environment of common services tunnel[J]. Tunnelling and Underground Space Technology, 2021, 110: 103714.

[202] 冯茜, 李擎, 全威, 等. 多目标粒子群优化算法研究综述[J]. 工程科学学报, 2021, 43(6): 745–753.

[203] 邓军, 雷昌奎, 曹凯, 等. 采空区煤自燃预测的随机森林方法[J]. 煤炭学报, 2018, 43(10): 2800–2808.

[204] 闫鹏程, 尚松行, 张超银, 等. 改进BP神经网络算法对煤矿水源的分类研究[J]. 光谱学与光谱分析, 2021, 41(7): 2288–2293.