大型强激光系统风险评价方法

对于大型强激光系统 ,要求进行安全性分析。给出了大型强激光系统风险评价的定性定量评估方法     

Oxidation of SO2 in Clouds at Whiteface Mountain

In-cloud oxidation of SO₂ byH₂O₂ was investigated using a tracertechnique based on SO^2– ₄/Se ratios atWhiteface Mountain, New York during summer months from1990 to 1998. Cloud water samples collected at themountain's summit (1.5 km above mean sea level) andaerosols at a below cloud site (Lodge) located at 0.6km amsl and in cloud interstitial air at the summitwere analyzed for SO^2– ₄ and selectedtrace elements. Gaseous SO₂ andH₂O₂were measured in realtime. Cloud water pH wasgenerally below 5.0 with a mean value of 3.6. Theresults show that significant in cloud oxidation occursin clouds during summer months varying from belowdetection to 62% with on average approximately 24% ofthe cloud water SO^2– ₄ produced from in-situ SO₂ oxidation. During summer the clouds wereoxidant limited for approximately one third of thetime.     

Collision of convex objects for calculation of porous mesh in gas explosion simulation

We investigate the coupling of the flamelet combustion model with the collision distance algorithm based on Minkowski addition. The collision algorithm is coded to calculate the porosity of the geometry based on the PDR (Porosity Distributed Resistance) approach for modelling of complex geometries. The turbulent field generated by the interaction of the flow with the porous objects is used to calculate the wrinkling length scale of the flame via the fluctuating velocities. The turbulent fluxes are amended in accordance with assigned porosities at the cell faces. The combustion and porosity models are implemented in the framework of an in house Fortran code that solves the full set of Navier-Stokes equations. The code was named STOKES - Shock Towards Kinetic Explosion Simulator). Results are presented for non-reacting flows and reacting flows over different geometries. Numerical findings are compared with standard commercial CFD tools.     

Effect of scale on freely propagating flames in aluminum dust clouds

The majority of experimental tests done on combustible dusts are performed in constant volume vessels that have limited or no optical access. Over the years, McGill University has been developing alternative experimental techniques based on direct observation of dust flames, yielding reliable fundamental parameters such as flame burning velocity, temperature and structure. The present work describes two new experimental set-ups allowing direct observation of isobaric and freely propagating dust flames at two sufficiently different scales to test the influence of scale on dust flame phenomena. In the laboratory-scale experiments, a few grams of aluminum powder are dispersed in transparent, 30 cm diameter latex balloons that allow for full visualization of the spherical flame propagation. In the field experiments, about 1 kg of aluminum powder is dispersed by a short pulse of air, forming a conical dust cloud with a total volume of about 5 m³. High-speed digital imaging is used to record the particle dispersal and flame propagation in both configurations. In the small-scale laboratory tests, the measured flame speed is found to be about 2.0 ± 0.2 m/s in fuel-rich aluminium clouds. The burning velocity, calculated by dividing the measured flame speed by the expansion factor deduced from thermodynamic equilibrium calculations, correlates well with the previously measured burning velocity of about 22–24 cm/s from Bunsen dust flames. Flame speeds observed in field experiments with large-scale clouds, however, are found to be much higher, in the range of 12 ± 2 m/s. Estimations are presented that show that the presumably greater role of radiative heat transfer in larger-scale aluminium flames is insufficient to explain the six-fold increase in flame speed. The role of residual large-eddy turbulence, as well as the frozen-turbulence effect leading to large-scale dust concentration fluctuations that cause flame folding, are discussed as two possible sources for the greater flame speed.     

基于云化物元耦合模型的岩质高边坡工程爆破施工安全风险评估

针对岩质高边坡工程爆破施工安全风险评估过程中存在模糊性与随机性的特点,提出采用云模型改进传统物元理论,采用物元法和云模型相耦合的评估模型来处理评估中的不确定性问题。该云化物元耦合模型方法采用云模型的数字特征值代替物元基本元中的特征量值,通过云模型的相关算法来计算风险评估指标的云关联度;同时,采用基于改进CRITIC的客观赋权法确定评估指标权重,进而根据加权平均法求得评估事件的综合评估分数,并以此来判定其安全风险等级。案例应用研究表明:(1)基于CRITIC的客观赋权法不仅计算简便,而且其通过挖掘指标的信息量来确定评估指标权重,使得评估结果更具有可信性;(2)该云化物元耦合模型方法的评估结果与其他3种常用方法的评估结果基本吻合,且各评估案例的置信度因子均小于0.05,说明云化物元耦合模型方法应用于岩质高边坡工程爆破施工安全风险评估是合理、有效的;(3)该云化物元耦合模型方法通过采用综合评估分数来判定安全风险等级,相较于其他评估方法,能够提供评估案例更多的综合信息,且比其他方法更加全面地反映了评估指标间包含的复杂不确定关系,使得评估结果具有更高的可靠性。     

基于TUV模式的对流层光解速率影响因子的研究

应用TUV辐射传输模式进行了一系列的敏感性试验,以期确定影响对流层O₃和NO₂光解速率的关键性因子.结果表明,气溶胶的光学性质对光解速率的影响存在明显差异.在气溶胶光学厚度（AOD）一定的情况下,散射性越强,近地面光解速率越大;当AOD从0.5增加至2.5,J[O¹D]和J[NO₂]极大值分别下降30.3%和13.1%.光解速率对较小的云光学厚度的变化比较敏感.云对J[NO₂]的影响存在明显的时间差异,在早晨和傍晚,J[NO₂]的衰减可以达到12%,而午时,J[NO₂]的衰减不足4%;在垂直方向上,云层的存在能够减小通过云层的光化辐射通量,有效降低云下光解速率,而云滴的后向散射特性能增大云上的光解速率.臭氧能够吸收300nm左右的紫外辐射,因而臭氧柱浓度变化对J[O¹D]有显著的影响,臭氧柱浓度从200DU增加至400DU,J[O¹D]极大值下降了53.1%,J[NO₂]极大值仅降低了1.0%.同时发现,气溶胶和云相对位置的改变对光解速率的垂直分布有较大的影响,气溶胶在云上时,高层的光解速率明显增大,且气溶胶的散射性越强,光解速率的增幅越大;当吸收性气溶胶位于云上时,使得高层光化辐射通量大量衰减,此时云层对于光解速率的影响比较微弱.     

基于葵花影像中国地区夜间云识别方法研究

目的实现适用于中国地区的夜间云识别。方法根据红外波段云与非云的亮温差异,采用亮温阈值法研究适用于中国地区的夜间云识别算法。中国地形高程差异明显,其地表辐射能量也存在较大差异,影响云检测结果精度,因而提出基于三个高程阶梯的云识别方法。由于云检测结果验证缺少可见光波段,使用MODIS云数据产品和雷达数据CALIPSO分别做定性和定量验证。结果云检测区域与MODIS的MYD06云产品基本一致,CALIPSO雷达数据四个月的平均验证结果为:非云区域的提取精度约77.86%,云区域的提取精度为79.67%,将有云区域错提为非云的误差率为2.76%,而将非云区域误提取为有云区域的错误率为12.31%。结论利用日本静止气象卫星Himawari-8影像数据,根据阈值法提出的基于三个高程阶梯的云检测算法,较好地实现了适用于中国地区的夜间云识别。     

2016年冬季北京地区一次重污染天气过程边界层特征

利用中国气象局地面常规观测资料、微脉冲激光雷达（MINI-MPL）、风廓线雷达资料、生态环境部大气成分等资料,对2016年12月16~21日京津冀多地污染过程的生消特征、与气象条件的关系以及边界层的结构特点进行了分析.结果表明：大气处于静稳状态,低层大气盛行偏南气流,大气湿度持续增加,加之北京三面环山不利于污染物扩散的特殊地形是造成北京此次严重空气污染的重要因素.重污染期间,污染物主要聚集在800m高度以下,严重污染时,污染物高度甚至仅有400m左右.风廓线雷达反演风场显示：2次PM_2.5浓度快速上升阶段低层伴随持续偏南风或偏东风.污染过程期间,逆温结构明显,两次污染快速发展阶段恰好出现在两次逆温最强时段.此次污染天气过程,激光雷达退偏振比总体小于0.25,反映污染主要是人类活动产生气溶胶,前期以一次排放颗粒物为主,后期以二次转化颗粒物为主.退偏振比污染过程前期呈明显日变化特征,且白天退偏比比夜间高.     

云水和云下冲刷颗粒物与气态污染物对降水中硫酸盐和硝酸盐的贡献

为定量云水和云下冲刷分别对降水中SO₄2-、NO₃-的贡献,并进一步解析云下冲刷颗粒相和气相物质分别对降水样品中SO₄2-、NO₃-的贡献,于2016年4月-2017年2月采用APS-3A型降水自动采样仪对降水进行分段采集.采用离子色谱检测分段降水样品的ρ（SO₄2-）、ρ（NO₃-）,分析其变化规律;在降水前、降水中及降水后同步采集并检测大气颗粒相ρ（SO₄2-）、ρ（NO₃-）和气相ρ（SO₂）、ρ（NO₂）,分析颗粒相中ρ（SO₄2-）、ρ（NO₃-）和气相中ρ（SO₂）、ρ（NO₂）的变化与分布.结果表明:①ρ（SO₄2-）、ρ（NO₃-）在同一场降水的分段样品中呈逐渐降低至后期趋于平稳的趋势,说明降水对空气中污染物的冲刷使空气逐渐清洁,后期冲刷作用有限使得降水中离子质量浓度趋稳.②颗粒相中ρ（SO₄2-）、ρ（NO₃-）与气相中ρ（SO₂）、ρ（NO₂）在降水前较高,在降水中减小,并在降水后回升,说明降水对颗粒相SO₄2-、NO₃-和气相SO₂、NO₂均有清除作用,降水结束后无云下冲刷作用,污染物质量浓度逐步回升.③云水对降水中ρ（SO₄2-）、ρ（NO₃-）的贡献率分别为22%~56%（平均值为35%）、9%~49%（平均值为29%）,云下冲刷颗粒相SO₄2-、NO₃-对降水中ρ（SO₄2-）、ρ（NO₃-）的贡献率分别为39%~69%（平均值为55%）、43%~73%（平均值为56%）,云下冲刷气相SO₂、NO₂对降水中ρ（SO₄2-）、ρ（NO₃-）的贡献率分别为5%~17%（平均值为10%）、5%~19%（平均值为15%）.研究显示,降水中SO₄2-、NO₃-主要来源于云水和云下冲刷颗粒相SO₄2-、NO₃-,而来源于云下冲刷气相SO₂、NO₂较少.      

基于云模型的水体富营养化程度评价方法

水体富营养化程度评价对生态环境保护具有重要意义.针对水体富营养化程度评价不可避免的模糊性和随机性的特点,本文引入云模型的方法,对我国14个代表性湖库的富营养化程度进行评价.根据确定的评价标准,生成各评价因子隶属于各级别的综合云模型,并针对湖库实测资料,得到各评价物的确定度,由最大确定度决定湖库营养化级别.将该法所得结果与湖库实际水质状况、国内外应用较为广泛且简便的营养状态指数、评分公式法及模糊综合评价法所得结果进行对比,发现评价结果真实可靠,计算过程简洁明了,验证了该方法的可行性和有效性.