Numerical simulation of far-field blast loads arising from large TNT equivalent explosives

Large TNT equivalent explosions usually arise from accidents occurring during the transportation, storage, and manufacturing of chemicals relevant to process industries. The blast wave generated by the explosion will spread and interact with the surrounding factories and storehouses, damaging the building structures within several kilometers and causing significant casualties and property losses. This study aims to develop an efficient numerical simulation method to predict blast loads to estimate the consequences of accidents involving far-field free air bursts or surface burst explosions. Before its interaction with the interested target, a blast wave is generated in the numerical model by specifying the initial and boundary conditions of the disturbed air. Based on empirical data of incident overpressure, an explicit formula to calculate the air particle velocity is derived from the governing equations of a perfect inviscid gas. A simplified path line method is proposed to calculate the air density. The proposed method is applied to the LS-DYNA CESE solver to simulate the blast loads on building structures in the far field. Validations against empirical data and experiments indicate that the proposed method is sufficiently accurate for engineering applications and, through a case study, presents a more efficient performance than the LOAD_BLAST_ENHANCED (LBE) and mapping methods.     

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

Stakeholder support is vital for achieving conservation success, yet there are few reliable mechanisms to monitor stakeholder attitudes toward conservation. Approaches used to assess attitudes rarely account for bias arising from reporting error, which can lead to falsely reporting a positive attitude toward conservation (false-positive error) or not reporting a positive attitude when the respondent has a positive attitude toward conservation (false-negative error). Borrowing from developments in applied conservation science, we used a Bayesian hierarchical model to quantify stakeholder attitudes as the probability of having a positive attitude toward wildlife notionally (or in abstract terms) and at localized scales while accounting for reporting error. We compared estimates from our model, Likert scores, and naïve estimates (i.e., proportion of respondents reporting a positive attitude in at least 1 question that was only susceptible to false-negative error) with true stakeholder attitudes through simulations. We then applied the model in a survey of tea estate staff on their attitudes toward Asian elephants (Elephas maximus) in the Kaziranga–Karbi Anglong landscape of northeast India. In simulations, Bayesian model estimates of stakeholder attitudes toward wildlife were less biased than naïve estimates or Likert scores. After accounting for reporting errors, we estimated the probability of having a positive attitude toward elephants notionally as 0.85 in the Kaziranga landscape, whereas the proportion of respondents who had positive attitudes toward elephants at a localized scale was 0.50. In comparison, without accounting for reporting errors, naïve estimates of proportions of respondents with positive attitudes toward elephants were 0.69 and 0.23 notionally and at local scales, respectively. False (positive and negative) reporting probabilities were consistently not 0 (0.22–0.68). Regular and reliable assessment of stakeholder attitudes–combined with inference on drivers of positive attitudes–can help assess the success of initiatives aimed at facilitating human behavioral change and inform conservation decision making.     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

How can process safety and a risk management approach guide pandemic risk management?

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.     

The role of agri‐environment schemes in conservation and environmental management

Over half of the European landscape is under agricultural management and has been for millennia. Many species and ecosystems of conservation concern in Europe depend on agricultural management and are showing ongoing declines. Agri‐environment schemes (AES) are designed partly to address this. They are a major source of nature conservation funding within the European Union (EU) and the highest conservation expenditure in Europe. We reviewed the structure of current AES across Europe. Since a 2003 review questioned the overall effectiveness of AES for biodiversity, there has been a plethora of case studies and meta‐analyses examining their effectiveness. Most syntheses demonstrate general increases in farmland biodiversity in response to AES, with the size of the effect depending on the structure and management of the surrounding landscape. This is important in the light of successive EU enlargement and ongoing reforms of AES. We examined the change in effect size over time by merging the data sets of 3 recent meta‐analyses and found that schemes implemented after revision of the EU's agri‐environmental programs in 2007 were not more effective than schemes implemented before revision. Furthermore, schemes aimed at areas out of production (such as field margins and hedgerows) are more effective at enhancing species richness than those aimed at productive areas (such as arable crops or grasslands). Outstanding research questions include whether AES enhance ecosystem services, whether they are more effective in agriculturally marginal areas than in intensively farmed areas, whether they are more or less cost‐effective for farmland biodiversity than protected areas, and how much their effectiveness is influenced by farmer training and advice? The general lesson from the European experience is that AES can be effective for conserving wildlife on farmland, but they are expensive and need to be carefully designed and targeted.     

双风机并联机站局阻特性研究

为探究双风机并联机站局部阻力特性、完善机站局部阻力及其系数的计算公式,以实际矿井通风机站为工程背景,在分析机站流场能量损失机理的基础上,采用物理模型实验和数值计算相结合的方法,分析了机站入口分流、出口汇流的断面速度不均匀程度与机站阻力损失的关系,同时对不同宽高比的矩形断面进行多工况模拟。结果表明:断面速度不均匀性越大,速度分布重组造成的阻力损失越大;确定了机站局阻计算所需参数的合理测定位置,得出机站局阻系数计算时的综合影响系数Kc随风机工况增大而增大、随断面宽高比A增大而减小,并呈现较好的线性关系。研究结果可为多级机站通风系统通风阻力特性分析以及工程优化设计提供参考。     

考虑爆裂影响的钢筋混凝土梁火灾温度场数值分析

为了描述混凝土高温爆裂对构件热传导过程的影响,结合钢筋混凝土梁火灾试验,考虑爆裂时间和区域的不确定性,建立了火灾下钢筋混凝土梁数值分析模型,分析爆裂深度、爆裂面积比、爆裂位置等爆裂参数对梁跨中截面温度场的影响规律。研究结果表明:当钢筋混凝土梁发生受火爆裂,梁截面的温度显著升高,并随爆裂深度的增大而进一步增强;爆裂面积比对截面温度场影响不显著,当爆裂深度、爆裂位置一定,爆裂面积比增加达到13%左右时,截面温度场基本上不再变化;爆裂深度、爆裂面积比一定,梁跨中爆裂对截面温度场影响最大,但是底部纵筋处温度较顶部纵筋处温度升高较快。     

基于热湿交换理论的巷道风流温、湿度影响因素研究

为了研究巷道风流参数的影响因素、预测风流温湿度的变化规律,结合热湿交换理论,建立了风流与围岩壁面之间热湿交换的数学模型,以及贴体坐标系下围岩内部温度场的导热微分方程;利用数值方法,将围岩内部的导热问题与影响风流参数变化的热湿交换问题耦合起来,并以大柳塔煤矿52505综采工作面为例进行计算,得到了与实测参数较为一致的模拟结果,验证了该数值方法的有效性。研究结果表明:风流温度受原始岩温、入风流温度、局部热源强度等因素的影响,风流相对湿度与入风流温、湿度以及井下湿源的数量和强度有关;巷道壁温作为将围岩温度场与风流参数之间关联起来的主要因素,对模拟结果影响较大,其数值取决于壁面与风流之间热湿交换以及围岩原始岩温。     

非对称开采条件下工作面“多向应力”变化特征研究

在非对称开采条件下,工作面受上覆岩层自重应力、超前支承应力、采空区侧向支承应力和回风巷煤柱应力等“多向应力”叠加影响,使得工作面应力呈“非对称”性。为研究非对称开采条件下工作面“多向应力”变化特征,基于微震监测、应力在线监测和理论计算,对母杜柴登煤矿30202工作面回采过程中所形成的非对称开采条件下的应力变化进行分析;并基于工作面所受应力条件和围岩体结构条件,分析了“多向应力”叠加显现机理。结果表明:30202工作面回采期间,煤柱支承应力沿走向分为应力升高区、应力明显降低区、应力缓慢降低区和应力稳定区,其应力峰值主要集中在工作面前方40 m左右,应力集中系数平均为1.61;在非对称开采阶段,在走向方向工作面超前支承应力影响范围较回采初期增加了100 m左右;在不考虑垂直应力影响的情况下,相邻工作面采空区的侧向应力对30202工作面倾向方向的影响范围为44 m,应力最大值为56.1 MPa。在“多向应力”耦合作用下工作面在回采过程中产生能量集聚,并在采动扰动下发生能量释放,满足了大能量事件发生的基本应力条件;同时在扰动条件下采空区发生高位顶板错动,以及围岩支护薄弱为大能量事件的发生提供了围岩结构条件。研究结果可为工作面非对称开采条件下采场矿压显现规律研究、顶板控制和巷道支护设计提供指导。     

水稻田的甲烷释放特性及其生物学机理

本文报导采用土柱法和田间原位法研究渍水稻田甲烷释放特性的结果。不同生育期的测定表明,早稻以分蘖盛期时释放量最大,以后逐渐减少,晚稻从分蘖始期起逐渐增加,至分蘖盛期、末期时达到最大,随后在孕穗期急剧减少,至生长后期又有所回升。以原位法测定表明,晚间和上午释放的甲烷量较多,中午几乎无甲烷释放,下午又有少量释放。不同施肥区稻田甲烷释放量明显不同,以施猪粪有机肥区为最高,次为施尿素无机氮区,不施肥区最低。但各施肥区水稻不同生育期的甲烷释放趋势完全一致,以分蘖期释放量最大,稻田释放的甲烷主要是稻植株释放的,可占总释放量的85％以上。行株间土壤释放的量不多。田间水释放极少,就植株而言,甲烷释放部位主要是在与土壤密切结合的未扰动的根基和根系部位,水面下茎秆白色部分和水上绿色部分几乎无释放。根系也主要是靠近根基的上半部分,根尖部位甲烷形成活性明显小得多。且研究表明,产甲烷细菌附存于根表而不进入根内组织。根际土壤中产甲烷细菌、厌氧性纤维素分解细菌和甲烷氧化细菌的数量以及总挥发有机酸含量都明显高于和行间土壤。