In order to describe an investigation of the flow around high-speed train on a bridge under cross winds using detached-eddy simulation(DES), a 1/8th scale model of a three-car high-speed train and a typical bridge mod...In order to describe an investigation of the flow around high-speed train on a bridge under cross winds using detached-eddy simulation(DES), a 1/8th scale model of a three-car high-speed train and a typical bridge model are employed, Numerical wind tunnel technology based on computational fluid dynamics(CFD) is used, and the CFD models are set as stationary models. The Reynolds number of the flow, based on the inflow velocity and the height of the vehicle, is 1.9×10~6. The computations are conducted under three cases, train on the windward track on the bridge(WWC), train on the leeward track on the bridge(LWC) and train on the flat ground(FGC). Commercial software FLUENT is used and the mesh sensitivity research is carried out by three different grids: coarse, medium and fine. Results show that compared with FGC case, the side force coefficients of the head cars for the WWC and LWC cases increases by 14% and 29%, respectively; the coefficients of middle cars for the WWC and LWC increase by 32% and 10%, respectively; and that of the tail car increases by 45% for the WWC whereas decreases by 2% for the LWC case. The most notable thing is that the side force and the rolling moment of the head car are greater for the LWC, while the side force and the rolling moment of the middle car and the tail car are greater for the WWC. Comparing the velocity profiles at different locations, the flow is significantly influenced by the bridge-train system when the air is close to it. For the three cases(WWC, LWC and FGC), the pressure on the windward side of train is mostly positive while that of the leeward side is negative. The discrepancy of train's aerodynamic force is due to the different surface area of positive pressure and negative pressure zone. Many vortices are born on the leeward edge of the roofs. Theses vortices develop downstream, detach and dissipate into the wake region. The eddies develop irregularly, leading to a noticeably turbulent flow at leeward side of train.展开更多
Gravity waves with periods close to the Brunt-V(a|¨)is(a|¨)l(a|¨) period of the upper troposphere are often observed at mesopause altitudes as short period,quasi-monochromatic waves.The assumption that ...Gravity waves with periods close to the Brunt-V(a|¨)is(a|¨)l(a|¨) period of the upper troposphere are often observed at mesopause altitudes as short period,quasi-monochromatic waves.The assumption that these short period waves originate in the troposphere may be problematic because their upward propagation to the mesosphere and lower thermosphere region could be significantly impeded due to an extended region of strong evanescence above the stratopause.To reconcile this apparent paradox,an alternative explanation is proposed in this paper.The inclusion of mean winds and their vertical shears is sufficient to allow certain short period waves to remain internal above the stratopause and to propagate efficiently to higher altitudes.A time-dependent numerical model is used to demonstrate the feasibility of this and to determine the circumstances under which the mesospheric wind shears play a role in the removal and directional filtering of short period gravity waves. Finally this paper concludes that the combination of the height-dependent mean winds and the mean temperature structure probably explains the existence of short period,quasi-monochromatic structures observed in airglow images of mesopause region.展开更多
In order to consider the influence of steel pole on the measurement of wind speed sensors and determinate the installation position of wind speed sensors, the flow field around wind speed sensors was investigated. Bas...In order to consider the influence of steel pole on the measurement of wind speed sensors and determinate the installation position of wind speed sensors, the flow field around wind speed sensors was investigated. Based on the three-dimensional steady Reynolds-averaged Navier-Stokes equations and k-ε double equations turbulent model, the field flow around the wind speed sensor and the steel pole along a high-speed railway was simulated on an unstructured grid. The grid-independent validation was conducted and the accuracy of the present numerical simulation method was validated by experiments and simulations carried out by previous researchers. Results show that the steel pole has a significant influence on the measurement results of wind speed sensors. As the distance between two wind speed sensors is varied from 0.3 to 1.0 m, the impact angles are less than ±20°, it is proposed that the distance between two wind speed sensors is 0.8 m at least, and the interval between wind speed sensors and the steel pole is more than 1.0 m with the sensors located on the upstream side.展开更多
该文旨在改进风速订正模型,以提高第6代跨学科气候研究模式(Model for Interdisciplinary Research on Climate Version 6,MIROC6)历史时期10 m风速的模拟准确性。研究基于Informer模型,结合多层感知机,构造了非平稳Informer(Ns-Informe...该文旨在改进风速订正模型,以提高第6代跨学科气候研究模式(Model for Interdisciplinary Research on Climate Version 6,MIROC6)历史时期10 m风速的模拟准确性。研究基于Informer模型,结合多层感知机,构造了非平稳Informer(Ns-Informer)10 m风速订正模型。研究提出了一种新的加权趋势均方误差损失函数,以优化模型在高风速条件下的订正性能,选取北京站、拐子湖站、茫崖站、吉安站4个代表站进行验证。结果表明:Ns-Informer在月尺度和年代际尺度上均能还原风速时间分布特征,订正后10 m风速的均方根误差降低20%~50%,在风速超过5 m·s^(-1)时表现最佳。Ns-Informer订正后的月平均10 m风速演变趋势与观测吻合度提高。在夏季和秋季订正效果显著,月平均10 m风速均方根误差降低25%以上。年代际变化趋势的订正表明Ns-Informer能矫正MIROC6对风速长期变化趋势的偏差,订正后的风速序列捕获了不同站点风速长期的上升或下降趋势。未来情景检验进一步表明:Ns-Informer能在SSP1-2.6情景下对高风速阈值的订正稳定性优于MIROC6。Ns-Informer可以有效降低MIROC6的系统偏差,为未来气候变化情景下风速的精确预估提供参考。展开更多
风速和风向是影响高速列车运行安全的重要因素,对高铁沿线的大风风速和风向进行有效预测有助于及时地对列车运行状况进行评估和预警。目前高铁大风领域的研究主要集中在风速的预测,尚未考虑风速风向的联合预测。基于深度循环神经网络—...风速和风向是影响高速列车运行安全的重要因素,对高铁沿线的大风风速和风向进行有效预测有助于及时地对列车运行状况进行评估和预警。目前高铁大风领域的研究主要集中在风速的预测,尚未考虑风速风向的联合预测。基于深度循环神经网络—长短记忆(LSTM)模型,提出独立预测法、分量预测法和多变量预测法等3种风速与风向联合预测方法,并利用兰新高铁大风监测实测数据对沿线多个基站的短期风速和风向进行同步联合预测。首先,通过归一化预处理原始风向和风速序列,并运用控制变量法确定最优时间步长和模型参数。其次,采用BPTT(Backpropagation Through Time)和Adam算法进行迭代训练,并结合早停法控制收敛,得到优化后的网络结构。最后,利用训练好的LSTM网络,采用3种方法对风速和风向进行联合预测。4个基站的实验结果表明,优化后的LSTM模型可以有效提取风速风向时间序列的长期依赖特征,结合联合预测方法能够实现对风速和风向的高精度同步预测;3种联合预测方法都能在较小范围内准确预测风速和风向,除5520基站外,风速预测误差在15%以内,风向预测误差在20%以内,其中多变量预测法表现出最优的整体预测精度,独立预测法次之。本研究为风速风向的联合预测提供了新的视角,对保障高铁列车运行的安全性具有参考价值。展开更多
基金Project(U1534210)supported by the National Natural Science Foundation of ChinaProject(14JJ1003)supported by the Natural Science Foundation of Hunan Province,China+2 种基金Project(2015CX003)supported by the Project of Innovation-driven Plan in Central South University,ChinaProject(14JC1003)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2015T002-A)supported by the Technological Research and Development program of China Railways Cooperation
文摘In order to describe an investigation of the flow around high-speed train on a bridge under cross winds using detached-eddy simulation(DES), a 1/8th scale model of a three-car high-speed train and a typical bridge model are employed, Numerical wind tunnel technology based on computational fluid dynamics(CFD) is used, and the CFD models are set as stationary models. The Reynolds number of the flow, based on the inflow velocity and the height of the vehicle, is 1.9×10~6. The computations are conducted under three cases, train on the windward track on the bridge(WWC), train on the leeward track on the bridge(LWC) and train on the flat ground(FGC). Commercial software FLUENT is used and the mesh sensitivity research is carried out by three different grids: coarse, medium and fine. Results show that compared with FGC case, the side force coefficients of the head cars for the WWC and LWC cases increases by 14% and 29%, respectively; the coefficients of middle cars for the WWC and LWC increase by 32% and 10%, respectively; and that of the tail car increases by 45% for the WWC whereas decreases by 2% for the LWC case. The most notable thing is that the side force and the rolling moment of the head car are greater for the LWC, while the side force and the rolling moment of the middle car and the tail car are greater for the WWC. Comparing the velocity profiles at different locations, the flow is significantly influenced by the bridge-train system when the air is close to it. For the three cases(WWC, LWC and FGC), the pressure on the windward side of train is mostly positive while that of the leeward side is negative. The discrepancy of train's aerodynamic force is due to the different surface area of positive pressure and negative pressure zone. Many vortices are born on the leeward edge of the roofs. Theses vortices develop downstream, detach and dissipate into the wake region. The eddies develop irregularly, leading to a noticeably turbulent flow at leeward side of train.
基金Supported by the National Natural Science Foundation of China(40874100,41174128)
文摘Gravity waves with periods close to the Brunt-V(a|¨)is(a|¨)l(a|¨) period of the upper troposphere are often observed at mesopause altitudes as short period,quasi-monochromatic waves.The assumption that these short period waves originate in the troposphere may be problematic because their upward propagation to the mesosphere and lower thermosphere region could be significantly impeded due to an extended region of strong evanescence above the stratopause.To reconcile this apparent paradox,an alternative explanation is proposed in this paper.The inclusion of mean winds and their vertical shears is sufficient to allow certain short period waves to remain internal above the stratopause and to propagate efficiently to higher altitudes.A time-dependent numerical model is used to demonstrate the feasibility of this and to determine the circumstances under which the mesospheric wind shears play a role in the removal and directional filtering of short period gravity waves. Finally this paper concludes that the combination of the height-dependent mean winds and the mean temperature structure probably explains the existence of short period,quasi-monochromatic structures observed in airglow images of mesopause region.
基金Projects(U1334205,51205418)supported by the National Natural Science Foundation of ChinaProject(2014T002-A)supported by the Science and Technology Research Program of China Railway CorporationProject(132014)supported by the Fok Ying Tong Education Foundation of China
文摘In order to consider the influence of steel pole on the measurement of wind speed sensors and determinate the installation position of wind speed sensors, the flow field around wind speed sensors was investigated. Based on the three-dimensional steady Reynolds-averaged Navier-Stokes equations and k-ε double equations turbulent model, the field flow around the wind speed sensor and the steel pole along a high-speed railway was simulated on an unstructured grid. The grid-independent validation was conducted and the accuracy of the present numerical simulation method was validated by experiments and simulations carried out by previous researchers. Results show that the steel pole has a significant influence on the measurement results of wind speed sensors. As the distance between two wind speed sensors is varied from 0.3 to 1.0 m, the impact angles are less than ±20°, it is proposed that the distance between two wind speed sensors is 0.8 m at least, and the interval between wind speed sensors and the steel pole is more than 1.0 m with the sensors located on the upstream side.
文摘该文旨在改进风速订正模型,以提高第6代跨学科气候研究模式(Model for Interdisciplinary Research on Climate Version 6,MIROC6)历史时期10 m风速的模拟准确性。研究基于Informer模型,结合多层感知机,构造了非平稳Informer(Ns-Informer)10 m风速订正模型。研究提出了一种新的加权趋势均方误差损失函数,以优化模型在高风速条件下的订正性能,选取北京站、拐子湖站、茫崖站、吉安站4个代表站进行验证。结果表明:Ns-Informer在月尺度和年代际尺度上均能还原风速时间分布特征,订正后10 m风速的均方根误差降低20%~50%,在风速超过5 m·s^(-1)时表现最佳。Ns-Informer订正后的月平均10 m风速演变趋势与观测吻合度提高。在夏季和秋季订正效果显著,月平均10 m风速均方根误差降低25%以上。年代际变化趋势的订正表明Ns-Informer能矫正MIROC6对风速长期变化趋势的偏差,订正后的风速序列捕获了不同站点风速长期的上升或下降趋势。未来情景检验进一步表明:Ns-Informer能在SSP1-2.6情景下对高风速阈值的订正稳定性优于MIROC6。Ns-Informer可以有效降低MIROC6的系统偏差,为未来气候变化情景下风速的精确预估提供参考。
文摘风速和风向是影响高速列车运行安全的重要因素,对高铁沿线的大风风速和风向进行有效预测有助于及时地对列车运行状况进行评估和预警。目前高铁大风领域的研究主要集中在风速的预测,尚未考虑风速风向的联合预测。基于深度循环神经网络—长短记忆(LSTM)模型,提出独立预测法、分量预测法和多变量预测法等3种风速与风向联合预测方法,并利用兰新高铁大风监测实测数据对沿线多个基站的短期风速和风向进行同步联合预测。首先,通过归一化预处理原始风向和风速序列,并运用控制变量法确定最优时间步长和模型参数。其次,采用BPTT(Backpropagation Through Time)和Adam算法进行迭代训练,并结合早停法控制收敛,得到优化后的网络结构。最后,利用训练好的LSTM网络,采用3种方法对风速和风向进行联合预测。4个基站的实验结果表明,优化后的LSTM模型可以有效提取风速风向时间序列的长期依赖特征,结合联合预测方法能够实现对风速和风向的高精度同步预测;3种联合预测方法都能在较小范围内准确预测风速和风向,除5520基站外,风速预测误差在15%以内,风向预测误差在20%以内,其中多变量预测法表现出最优的整体预测精度,独立预测法次之。本研究为风速风向的联合预测提供了新的视角,对保障高铁列车运行的安全性具有参考价值。
