發(fā)布時(shí)間：2018-05-01 18:54

  本文選題：公路隧道 + 送排式縱向通風(fēng)��； 參考：《北京交通大學(xué)》2015年博士論文

【摘要】：公路隧道作為封閉的地下空間,正常運(yùn)營(yíng)時(shí)需要通風(fēng)來(lái)稀釋和排出機(jī)動(dòng)車(chē)尾排污染物,為駕乘人員提供安全、舒適和衛(wèi)生的行車(chē)環(huán)境;火災(zāi)時(shí)需要通風(fēng)來(lái)控制煙氣流向并提供相應(yīng)的新風(fēng),為駕乘和救援人員提供有利的逃生與救援環(huán)境。在我國(guó),長(zhǎng)度大于5000m的高速公路隧道通常采用通風(fēng)井送排式+射流風(fēng)機(jī)的組合縱向通風(fēng)方式,其通風(fēng)效能的發(fā)揮需要射流風(fēng)機(jī)群與送排風(fēng)機(jī)的聯(lián)合作用,正常運(yùn)營(yíng)和火災(zāi)工況時(shí)如何控制通風(fēng)系統(tǒng)的運(yùn)轉(zhuǎn),這成為制約長(zhǎng)大公路隧道建設(shè)與運(yùn)營(yíng)的一項(xiàng)重要課題。本文依托張涿高速公路分水嶺特長(zhǎng)公路隧道工程,應(yīng)用流體力學(xué)和空氣動(dòng)力學(xué)理論,通過(guò)模型試驗(yàn)、數(shù)值模擬和現(xiàn)場(chǎng)測(cè)試的綜合研究手段,結(jié)合人工智能技術(shù),對(duì)公路隧道豎井送排式縱向通風(fēng)系統(tǒng)性能、火災(zāi)條件下洞內(nèi)溫度及煙氣濃度特征、公路隧道運(yùn)營(yíng)期正常運(yùn)營(yíng)及火災(zāi)工況通風(fēng)系統(tǒng)智能優(yōu)化控制等展開(kāi)深入研究。取得主要成果如下:(1)依據(jù)流體動(dòng)力學(xué)及相似模型試驗(yàn)理論,研制加工了分水嶺特長(zhǎng)隧道單斜井送排式通風(fēng)系統(tǒng)物理模型試驗(yàn)臺(tái),并對(duì)模型試驗(yàn)系統(tǒng)基本性能進(jìn)行了測(cè)試,驗(yàn)證了模型試驗(yàn)臺(tái)的可靠性,采用該物理模型進(jìn)行了豎井送排式機(jī)械通風(fēng)系統(tǒng)影響試驗(yàn)、分水嶺隧道正常運(yùn)營(yíng)工況風(fēng)機(jī)梯次關(guān)停試驗(yàn)、隧道火災(zāi)工況煙氣模擬試驗(yàn)等,為后續(xù)系統(tǒng)優(yōu)化控制提供理論基礎(chǔ)。(2)基于人工智能與現(xiàn)代優(yōu)化理論,提出了隧道運(yùn)營(yíng)交通量預(yù)測(cè)的粒子群優(yōu)化與高斯過(guò)程回歸耦合(PSO-GPR)模型,對(duì)隧道短期交通量預(yù)測(cè)平均相對(duì)誤差僅為1.96%,最大不到5%,該模型可以應(yīng)用于隧道運(yùn)營(yíng)期間短期交通量的預(yù)測(cè),為隧道運(yùn)營(yíng)期通風(fēng)系統(tǒng)前饋控制提供實(shí)時(shí)交通量數(shù)據(jù)。(3)在交通量預(yù)測(cè)的基礎(chǔ)上,基于分水嶺隧道正常運(yùn)營(yíng)工況三維數(shù)值模擬結(jié)果,以洞內(nèi)中軸線(xiàn)1.8m高處縱截面CO最高濃度為控制目標(biāo),建立了隧道運(yùn)營(yíng)期正常運(yùn)營(yíng)工況下通風(fēng)系統(tǒng)優(yōu)化控制的進(jìn)化支持向量回歸模型,利用該模型可以實(shí)現(xiàn)不同行車(chē)速度和不同控制指標(biāo)情況下通風(fēng)系統(tǒng)的優(yōu)化控制,并采用現(xiàn)場(chǎng)實(shí)測(cè)數(shù)據(jù)對(duì)優(yōu)化控制模型進(jìn)行了驗(yàn)證。(4)基于分水嶺特長(zhǎng)隧道運(yùn)營(yíng)期火災(zāi)工況下的三維數(shù)值模擬,引入進(jìn)化高斯過(guò)程回歸算法,提出了隧道運(yùn)營(yíng)期火災(zāi)工況下通風(fēng)系統(tǒng)優(yōu)化的有約束多目標(biāo)規(guī)劃模型,即火災(zāi)發(fā)生時(shí),在保證煙氣不回流的前提下,盡量減少火源下游30米1.8米高處煙氣的溫度和濃度。(5)依據(jù)現(xiàn)代優(yōu)化理論,建立了基于罰函數(shù)法的火災(zāi)工況下隧道通風(fēng)系統(tǒng)優(yōu)化的PSO與BP神經(jīng)網(wǎng)絡(luò)耦合求解模型,并與火災(zāi)數(shù)值模擬相結(jié)合,實(shí)現(xiàn)了火災(zāi)工況下隧道通風(fēng)系統(tǒng)的有約束多目標(biāo)規(guī)劃,為火災(zāi)工況時(shí)通風(fēng)系統(tǒng)的智能控制提出了新的解決方法。
[Abstract]:As a closed underground space, highway tunnel needs ventilation to dilute and discharge vehicle exhaust pollutants in normal operation to provide safe, comfortable and hygienic driving environment for drivers. In case of fire, ventilation is needed to control the flow of smoke and provide the corresponding fresh air to provide a favorable escape and rescue environment for drivers and rescuers. In our country, the combined longitudinal ventilation of jet fan is usually used in freeway tunnel with length more than 5000m, and its ventilation efficiency needs the combined action of jet fan group and exhaust fan. How to control the operation of ventilation system in normal operation and fire condition has become an important problem that restricts the construction and operation of long road tunnel. Based on the Zhangzhuo Expressway Dianshui Extra-long Highway Tunnel Project, this paper applies the theories of fluid dynamics and aerodynamics, and combines the artificial intelligence technology with the comprehensive research means of model test, numerical simulation and field test. The performance of vertical ventilation system of highway tunnel shaft, the characteristics of temperature and smoke concentration in tunnel under fire conditions, the intelligent optimization control of ventilation system in normal operation period and fire condition of highway tunnel are studied. The main results are as follows: (1) based on the theory of fluid dynamics and similar model test, the physical model test bench of single inclined shaft feeding ventilation system for long and long tunnel is developed, and the basic performance of the model test system is tested. The reliability of the model test-bed is verified. The physical model is used to carry out the influence test on the mechanical ventilation system of the shaft feeding and discharging type, the wind turbine trapezoidal shutdown test under the normal operating condition of the watershed tunnel, the smoke simulation test under the tunnel fire condition, etc. (2) based on artificial intelligence and modern optimization theory, a PSO -GPRA model for forecasting traffic volume of tunnel operation is proposed, which is based on the regression coupling between particle swarm optimization (PSO) and Gao Si process. The average relative error of short-term traffic volume prediction is only 1.96, and the maximum is less than 5. The model can be used to predict short-term traffic volume during tunnel operation. To provide real-time traffic volume data for feedforward control of ventilation system in tunnel operation period. Based on traffic volume prediction, 3D numerical simulation results of normal operating conditions of watershed tunnel are presented. With the maximum concentration of CO in the longitudinal section of 1.8 m high in the central axis of the tunnel as the control target, an evolutionary support vector regression model for optimal control of ventilation system under normal operating conditions during tunnel operation is established. The model can be used to realize the optimal control of ventilation system with different driving speed and different control index. The optimized control model is verified by the field measured data. Based on the 3D numerical simulation under the fire condition of the watershed super long tunnel, an evolutionary Gao Si process regression algorithm is introduced. A constrained multi-objective programming model for optimization of ventilation system under fire condition in tunnel operation period is proposed, that is, when the fire occurs, the flue gas is not reflux. According to modern optimization theory, a coupling model of PSO and BP neural network for optimization of tunnel ventilation system under fire condition is established based on penalty function method. Combined with numerical simulation of fire, the constrained multi-objective programming of tunnel ventilation system under fire condition is realized, which provides a new solution for intelligent control of ventilation system under fire condition.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：U453.5

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