侧向风速作用下聚氨酯泡沫双火源火蔓延研究

为探究环境风作用下逆向双点火源聚氨酯泡沫火蔓延及融合行为，开展多组对照实验并从材料传热机理角度分析侧向风速对火蔓延行为中火羽流形态、质量损失和辐射热流场等特征参数的影响。结果表明:风速与上述参数之间存在非线性关系。环境风效应使火焰被拉长且敷贴于预热区表面，增大预热区面积和热反馈；侧向风速的增加对FPU板材质量损失的影响逐渐弱化，且板材的熔滴率与风速呈正相关；无论侧向风是否存在，两侧逆向火焰融合后均达到整个蔓延过程中的峰值温度；风速的存在限制了火焰温度与辐射热通量峰值，也缩短了温度和辐射峰值出现的时间。     

隧道竖井外流场模拟及风塔外型结构优化研究

为提高长大隧道施工及运营通风过程中竖井通风效率，实现隧道节能通风。依托实际隧道工程，基于Fluent软件，选用RNG k-ε湍流模型，对无竖井风塔、圆柱风塔、矩形风塔和凸台状风塔4种结构、不同环境风速工况下的流场进行数值模拟，并对模拟结果进行交叉比对分析，提出最优化的竖井风塔结构形式。研究结果表明:采用风塔结构竖井出流量要远大于无风塔结构竖井出流量；对于不同竖井风塔外型结构，竖井出流量与环境风速关系呈多项式函数；当倾斜角为45°、圆心角为90°时，凸台结构对应竖井出流量最大，且受环境风影响波动较小，通风稳定性较好。建议竖井出口结构选用矩形或凸台状，实现对环境风的高效利用，提高竖井通风效率。     

街道峡谷内汽车排放污染物浓度分布的观测与数值模拟

在上海某典型街道峡谷内按一定的空间布点,在一定时段内同时对各布点进行采样并做一氧化碳浓度分析,同时记录车辆种类、车流量、气象条件等,分析街道峡谷内污染物浓度的分布.运用风向频率加权(WDFW)方法,结合大气流动和污染物扩散的CFD模型进行数值模拟计算.结果表明,数值模拟结果和现场观测结果较吻合,建筑物低的一侧污染物浓度远高于建筑物高的一侧污染物浓度,两侧的污染物浓度随着高度的增加而降低.     

风蚀条件下人类活动对高寒草地水分保持功能影响的定量模拟

青藏高原是我国重要的生态屏障,具有重要的水源涵养功能。高寒草地生态系统退化将严重地影响高原的水源涵养功能。为了定量研究人类活动对青藏高原高寒草地水分保持功能的影响,采用大型风洞实验,模拟人类不同的干扰方式和干扰程度对高寒草地土壤水分保持功能的影响。实验样品采自青藏高原三个草地类型:高寒草甸、草原化草甸和高寒草原。干扰方式包括破坏草地地上部分和根系,干扰程度包括轻干扰、中度干扰、重度干扰和全部破坏。实验结果表明,地上部分破坏后,土壤含水率均有下降,降幅分别为:QZ1为6.9%～9.2%、QZ2为6.8%～10.1%、QZ3为9%～10%;当根系遭到破坏后土壤含水率的降幅则分别为:QZ1为16%～30%,QZ2为17.25～32.1%,QZ3为22%～50%,显然,根系破坏后土壤含水率降幅远大于地表植被破坏后的情形。因此,植被根系是高寒草地水分保持功能的关键。随着干扰程度的加剧,土壤含水率在迅速下降。从试验模拟结果看,三个草地类型中土壤水分保持功能分别是高寒草甸>草原化草甸>高寒草原。     

北京市与香河县大气臭氧及氮氧化合物的变化特征

于2005年春、夏之交,在北京市及河北省香河县两地同时开展了臭氧和氮氧化合物的连续监测.结果表明,香河县和北京市的臭氧统计日变化形式基本一致,北京市大气臭氧平均浓度比香河低,分别为28×10-9V/V和43×10-9V/V;大气中0x浓度北京市高于香河县,分别为72×10-9V/V和60×10-9V/V.观测期间,两地臭氧的月最高值出现时间一致,其中6月21日北京市的臭氧浓度最高达到200×10-9V/V,香河县也达到149×10-9V/V.在三次臭氧严重超标事件发生的前一天,两地都伴随有氮氧化合物的高浓度积累;南风时北京市和香河县的Ox浓度都比平均值高25%.     

A comparative study of prognostic MM5 meteorological modeling with aircraft, wind profiler, lidar, tethered balloon and RASS data over Philadelphia during a 1999 summer episode

This study presents a comparative evaluation of the prognostic meteorological Fifth Generation NCAR Pennsylvania State University Mesoscale Model (MM5) using data from the Northeast Oxidant and Particle Study (NE-OPS) research program collected over Philadelphia, PA during a summer episode in 1999. A set of model simulations utilizing a nested grid of 36 km, 12 km and 4 km horizontal resolutions with 21 layers in the vertical direction was performed for a period of 101 h from July 15, 1999; 12 UTC to July 19, 1999; 17 UTC. The model predictions obtained with 4 km horizontal grid resolution were compared with the NE-OPS observations. Comparisons of model temperature with aircraft data revealed that the model exhibited slight underestimation as noted by previous investigators. Comparisons of model temperature with aircraft and tethered balloon data indicate that the mean absolute error varied up to 1.5 °C. The comparisons of model relative humidity with aircraft and tethered balloon indicate that the mean relative error varied from –11% to –22% for the tethered balloon and from –5% to –30% for the aircraft data. The mean relative error for water vapor mixing ratio with respect to the lidar data exhibited a negative bias consistent with the humidity bias corresponding to aircraft and tethered balloon data. The tendency of MM5 to produce estimates of very low wind speeds, especially in the early-mid afternoon hours, as noted by earlier investigators, is seen in this study also. It is indeed true that the initial fields as well as the fields utilized in the data assimilation also contribute to some of the differences between the model and observations. Studies such as these which compare the grid averaged mean state variables with observations have inherent difficulties. Despite the above limitations, the results of the present study broadly conform to the general traits of MM5 as noted by earlier investigators.     

WIND GENERATED WAVES IN LAKE OKEECHOBEE1

ABSTRACT: The study of wind generated waves is important because waves affect sediment resuspension in lakes. Measurements of wind velocity and wave elevation were made at three different stations in Lake Okeechobee. Significant wave heights were computed using a direct count from the recorded data, and verified by the root-mean-square value approach. The correlation between wind stress and significant wave height also was analyzed. The data revealed a strong correlation. In addition to field measurements, a Boussinesq-type wind-wave model was developed to simulate wind-generated, long-propagating waves. This model included the effects of wind stress and bottom viscous dissipation. Wave elevation and velocity field were evaluated numerically. A six-day simulation using 1996 wind data was conducted. Simulated significant wave heights were found to agree reasonably well with measured values. A predictive wind-wave model provides information about wind generated waves, which is used to compute bottom shear stresses required for sediment resuspension studies.     

VERIFICATION OF THE RHEA-OROGRAPHIC-PRECIPITATION MODEL1

ABSTRACT: Observed April 1 snowpack accumulations within and near the Gunnison River basin in southwestern Colorado are compared with simulations from the Rhea-orographic-precipitation model to determine if the model simulates reliable magnitudes and temporal and spatial variability in winter precipitation for the basin. Twenty simulations of the Rhea model were performed using‘optimal’parameter sets determined for 10-kilometer (km) grids (10-km by 10-km grid cells) through stochastic calibration. Comparisons of Rhea-model simulations of winter precipitation with April 1 snowpack accumulations at 32 snowcourse stations were performed for the years 1972–1990. For most stations and most years the Rhea model reliably simulates the temporal and spatial variability in April 1 snowpack accumulations. However, in general, the Rhea-model underestimates April 1 snowpack accumulations in the Gunnison River basin area, and the underestimation is greatest for locations that receive the largest amount of snow. A significant portion of the error in Rhea-model simulations is due to the calibration of the Rhea model using gauge-catch precipitation measurements which can be as much as 50 percent below actual snowfall accumulations. Additional error in the Rhea-model simulations is a result of the comparison of gridded precipitation values to observed values measured at points.     

基于机器学习的电热防除冰表面温度变化趋势预测

针对飞机部件周期控制律电加热防除冰的应用,提出基于机器学习以预测电加热防除冰表面温度的变化趋势.依靠大型结冰风洞及其电加热防除冰控制系统完成防除冰试验,获得有效的试验数据,以通、断电周期为分割单元,将试验数据划分成验证集和训练集.根据电热防除冰过程的换热情况,构建样本的特征参数,利用机器学习的有监督学习方式,选择KNN近邻回归算法和局部线性加权回归算法预测温度变化率,再转换为温度,得到的温度变化与测量数据的线性相关性达到80％以上的高相似度结果,表明使用机器学习可快速预测电热防除冰部件的表面温度变化趋势,且不同的回归算法针对模型不同测温点位置的预测效果存在差异.     

广州地区灰霾过程和清洁过程的边界层特征对比分析

利用2014~2016年广州国家基本气象站的微波辐射计、风廓线雷达和地面观测数据,研究广州地区灰霾过程和清洁过程的边界层结构特征.结果表明：（1）灰霾过程中,270m高度以下风速随高度递减,270m高度以上的风速随高度递增,2000m以下的风速增率小于2000m以上的风速增率,盛行风向随高度的增加呈顺时针旋转,510m高度以下风速基本小于3.0m/s,其中08：00至20：00,390m高度以下风速小于2.0m/s;清洁过程中510~1590m和2790~3000m存在风速大于5.0m/s的高值中心,1830m高度以下,清洁过程各层的平均风速明显高于灰霾过程;（2）贴地逆温与能见度总体上呈负相关,与PM_2.5浓度呈正相关,相关系数分别为-0.367和0.455,而当贴地逆温和低空逆温同时存在时,其相关性更高,其相关系数分别为-0.5和0.601,说明多层逆温的存在更容易出现灰霾天气.灰霾过程中,低空逆温与能见度和PM_2.5的相关不明显,而清洁过程中,低空逆温的出现主要与冷空气南下有关,其与能见度呈正相关（0.217）,和PM_2.5浓度呈负相关（-0.64）,低空逆温不利于灰霾天气形成;（3）灰霾过程中,贴地逆温出现频率为60.68%,平均逆温强度为1.38℃/100m,平均逆温厚度为153.20m,明显高于清洁过程;清洁过程中,低空逆温的逆温强度、厚度和出现频率分别为0.27℃/100m、691.07m和64.61%,明显高于灰霾过程.（4）清洁过程的混合层高度明显高于灰霾过程,清洁过程的日均混合层高度（958.92m）是灰霾过程（398.03m）的2.4倍.