Influence of non-reactive particle cloud on heterogeneous detonation propagation

An interaction of a detonation wave propagating in the cellular detonation mode with a cloud of inert particles is investigated numerically. The analysis of results allows the regimes of propagation of the heterogeneous plane Chapman–Jouguet and cellular detonations and their suppression to be identified. The influence of various parameters of the inert cloud is demonstrated. The critical length of the cloud sufficient for detonation suppression is determined. It is shown that the disperse composition and the nonuniform distribution of particles in the cloud are important parameters affecting the detonation propagation mode.     

Modeling of a cryogenic liquid pool boiling by CFD simulation

A boiling model is developed by Computational Fluid Dynamics (CFD) code to calculate the source term of a cryogenic liquid spill. The model includes the effect of the changing ground temperature on the vaporization rate of the cryogenic liquid. Simulations are performed for liquid nitrogen. The model can describe different boiling regimes (film, transition and nucleate). The heat flux calculated for each boiling regimes are compared to the experimental data from literature. The developed numerical model seems to have a good ability to predict the heat flux for the film boiling stage. Model development is still necessary to improve the prediction of the nucleate boiling regime. Overall, the approach shows very promising results to model the complex physical phenomena involved in in the vaporization of cryogenic liquid pool spilled on ground.     

Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R²), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R² value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.     

Expanding Kenya's protected areas under the Convention on Biological Diversity to maximize coverage of plant diversity

Biodiversity is highly valuable and critically threatened by anthropogenic degradation of the natural environment. In response, governments have pledged enhanced protected‐area coverage, which requires scarce biological data to identify conservation priorities. To assist this effort, we mapped conservation priorities in Kenya based on maximizing alpha (species richness) and beta diversity (species turnover) of plant communities while minimizing economic costs. We used plant‐cover percentages from vegetation surveys of over 2000 plots to build separate models for each type of diversity. Opportunity and management costs were based on literature data and interviews with conservation organizations. Species richness was predicted to be highest in a belt from Lake Turkana through Mount Kenya and in a belt parallel to the coast, and species turnover was predicted to be highest in western Kenya and along the coast. Our results suggest the expanding reserve network should focus on the coast and northeastern provinces of Kenya, where new biological surveys would also fill biological data gaps. Meeting the Convention on Biological Diversity target of 17% terrestrial coverage by 2020 would increase representation of Kenya's plant communities by 75%. However, this would require about 50 times more funds than Kenya has received thus far from the Global Environment Facility.     

Assessing the population-level conservation effects of marine protected areas

Marine protected areas (MPAs) cover 3–7% of the world's ocean, and international organizations call for 30% coverage by 2030. Although numerous studies show that MPAs produce conservation benefits inside their borders, many MPAs are also justified on the grounds that they confer conservation benefits to the connected populations that span beyond their borders. A network of MPAs covering roughly 20% of the Channel Islands National Marine Sanctuary was established in 2003, with a goal of providing regional conservation and fishery benefits. We used a spatially explicit bioeconomic simulation model and a Bayesian difference-in-difference regression to examine the conditions under which MPAs can provide population-level conservation benefits inside and outside their borders and to assess evidence of those benefits in the Channel Islands. As of 2017, we estimated that biomass densities of targeted fin-fish had a median value 81% higher (90% credible interval: 23–148) inside the Channel Island MPAs than outside. However, we found no clear effect of these MPAs on mean total biomass densities at the population level: estimated median effect was –7% (90% credible interval: –31 to 23) from 2015 to 2017. Our simulation model showed that effect sizes of MPAs of <30% were likely to be difficult to detect (even when they were present); smaller effect sizes (which are likely to be common) were even harder to detect. Clearly, communicating expectations and uncertainties around MPAs is critical to ensuring that MPAs are effective. We provide a novel assessment of the population-level effects of a large MPA network across many different species of targeted fin-fish, and our results offer guidance for communities charged with monitoring and adapting MPAs.     

消毒副产物生成的温度影响和动力学模型

在水厂和供水管网中,加氯消毒副产物(DBPs)的生成浓度会随着季节转换和水温变化而出现明显波动.为了探究不同温度条件下DBP生成浓度的变化规律,本研究利用腐殖酸(HA)作为有机前体物进行加氯消毒,参照DBP统一生成条件(uniform formation condition,UFC),在不同水温下监测DBP[包括三卤甲烷(THM)和卤乙酸(HAA)]的生成浓度,并从动力学角度对该生成反应的机制做出分析,进而建立初步的浓度预测模型.结果表明,对于检测到的3种典型DBPs[三氯甲烷(CHCl3)、二氯乙酸(DCAA)和三氯乙酸(TCAA)],水温升高均能够明显提高其生成速率和最大生成浓度,其中后者随着温度的升高近似呈指数增长(R2>0.90).根据一级反应动力学模型对各组温度条件下的DBP实测值进行拟合,得到了较好的相关性(R2>0.94).为了进一步验证该动力学模型的准确性和可靠性,在20℃和30℃条件下分别采用该模型对DBP的实际生成浓度进行预测,并与实测值进行比较,均得到了良好的预测结果.为定量研究温度对DBP生成速率的影响规律,利用阿伦尼乌斯公式计算得到CHCl3、DCAA和TCAA的表观反应活化能分别为22.3、25.5和40.8 kJ.mol-1.     

基于IRML网络模型的LNG卸料系统故障传播分析

为研究陆地LNG卸料系统的物理设备、信息网络及人员操作的依赖关系和信息层、人员层对设备层故障传播的影响,基于面向基础设施弹性建模语言(Infrastructure Resilience-Oriented Modelling Language,IRML),从单层网络静态风险分析和多层依赖网络的动态传播2个方面,提出LNG...     

罐区灾害场景建模及应急处置仿真实训系统研究

针对目前应急消防训练中事故场景感知性、交互性不足等问题,研究了罐区灾害场场景建模方法及人体伤害建模方法,研发了基于可穿戴式体感装备的虚拟现实应急处置实训系统,构建了虚实结合的危险化学品火灾事故应急救援仿真训练环境,使应急人员能够最大程度地贴近实战环境,完成仿真训练,提高标准化操作能力及事故应急处置能力。     

昆明市老运粮河污染治理的模拟研究

在快速城市化进程中,城市的建设和发展受到黑臭水体问题的挑战.以昆明市老运粮河为例,根据质量守恒原理,构建了昆明市老运粮河污染治理效果模拟系统,通过模拟,计算了老运粮河的入滇污染负荷,以及不同污染治理措施的治理效果,并对模拟系统的不确定性进行了初步分析.结果表明,老运粮河的年入滇总水量为9565.9×10~4t,COD、TN、TP和NH_3-N的年入滇总负荷分别为26250、3232、415.9和2135.5 t;区域内产生的点源污染负荷远大于非点源污染负荷;污水厂处理是去除污染物的主要途径,其中,旱季污水的处理对于全年的污染物削减起到最主要的作用;雨季源头削减措施的应用对雨季污水处理有很大作用;影响模拟结果的不确定性因素很多,分析认为由此带来的风险问题不可忽视.     

应用ANFIS预测蠕虫反应器的污泥减量速率并优化其运行条件

利用水生蠕虫的捕食作用可以有效地实现污泥减量.为了研究环境条件波动对蠕虫捕食污泥减量效率的影响,应用自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN)模型分别预测蠕虫反应器的污泥减量速率.结果表明,溶解氧浓度(D0)、温度(T)、蠕虫密度和污泥负荷是蠕虫捕食过程的主要影响因素,通过性能比较得出ANFIS模型预测值与实验测定值间具有更好的一致性,其相关系数(r)为0.82,绝对平均误差百分比(MAPE)为71.5％,均方根误差(RMSE)为16.7.根据ANFIS模型的预测结果,得出蠕虫反应器的最适运行条件为:DO 1.8 ～3.1mg·L-1,温度18.4～21.7℃,蠕虫密度低于1.7 g·cm-2(以湿重计),污泥负荷563～734 mg·g-1(以TSS计),在此操作条件下获得的污泥减量速率均高于100 mg· g-1·d-1.