公交车火灾安全及人员疏散分析

公交车作为最常用的交通工具之一,其空间密闭、人员密度大,一旦发生火灾,极易造成群死群伤事故,社会影响恶劣.首先采用鱼刺图分析法(FBF)分析公交车火灾事故发生的原因;再采用事故树分析法(FTA),得出公交车安全门对公交车内人员安全疏散的重要性;在此基础上,以大容量公交车为研究对象,针对不同开门情况设置4个疏散场景,并运用Building Exodus对车内人员疏散情况进行模拟,提出相应建议.结果表明,公交车右侧前门为1.1m、后门为1.Sm,右侧前、后门均为1.Sm,前门、左右两侧后门均为1.5m3种情况下均较原始设计(右侧前、后门均为1.1m)不同程度缩短了人员疏散时间.     

基于可靠性理论的事件树分析方法研究

事件树分析过程中事件树的编制和分析方法缺乏系统性,导致实际运用中事件树的编制缺乏可操作性。从目标系统的选择、环节事件逻辑关系的确定、定量分析中基本数据的来源和确定方法等三个问题进行了系统研究,并以可靠性的相关理论为基础,提出"从控制和影响初始事件发展演化的角度,调查分析对初始事件做出响应的安全功能,按系统工作原理对目标系统进行重构,构建目标系统的功能结构图,画出系统的可靠性框图"入手,对"传统的事件树绘制程序"进行修正,解决了事件树分析过程中目标系统的选择、环节事件逻辑关系的确定以及定量分析基本数据的确定方法等问题。修正后的方案对实现事件树的正确编制和系统分析逻辑更清晰,操作指导性更强。     

中小型在役桥梁安全预警研究

为了准确评估中小型在役桥梁安全状况,提出利用灰色关联故障树分析法对中小型在役桥梁进行安全预警研究。首先建立中小型在役桥梁安全预警故障树;其次采用专家打分法确定故障树各底事件的不可靠概率,得到各底事件的重要度,并利用灰色关联理论求出故障树各底事件的关联度;根据预警理论将各底事件的关联度与各底事件的重要度加权平均确定预警值所处的警限区间,从而确定警度及需要发出的预警信号灯,完成预警工作,并提出相应的预控对策。最后通过实例分析,结果表明该方法具有可行性与实用性。     

连续型空间故障树中因素重要度分布的定义与认知

为了解工作环境条件因素对系统故障概率的影响的特征,同时丰富连续型空间故障树(CSFT)的理论框架,提出因素重要度分布的概念。因素重要度分布从经典故障树的概率重要度发展而来,目的是研究系统所处环境因素变化导致系统可靠性变化的程度。给出元件和系统的因素重要度分布概念、公式及所需基础数据,并分析因素重要度分布的正负分布特点。使用该概念研究元件X1和系统T的因素t重要度分布和因素c重要度分布。结果表明:在不同环境中,对于因素t或c变化影响元件或系统的故障概率变化程度是不同的。因素重要度分布有效地表达了t和c对元件或系统的故障概率影响特征。     

航天器总装事故原因事件重要度分析

为解决传统安全性定量分析方法不能描述航天器总装这一复杂人-机-环系统的失效数据波动性和非严格逻辑关系的问题,结合生产实际,采用问卷调查的方法找出影响总装事故的风险因素,即导致事故的事件发生可能性、输入事件对输出事件的影响程度等。在验证调查数据的有效性之后,应用基于模糊数的模糊因果图(FCD),计算某航天器与支架车连接作业的事故风险可能性。提出原因事件重要度的计算方法。通过计算发现,人员注意力和发动机保护罩作用是导致发动机损伤的关键事件。与模糊事故树(FFT)、贝叶斯网络(BN)重要度计算结果对比表明,模糊重要度计算结果能反映事故发生可能性对原因事件发生可能性值的增减的敏感度。     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.     

北京10个常绿树种颗粒物吸附能力研究

大气中颗粒物PM(particulate matter)不仅造成环境污染,还对人体造成严重的危害.城市绿色植物作为大气过滤器,能够有效地提高城市空气质量,保护人体健康.因此了解不同树种对空气颗粒物的吸附滞纳作用是必要的.以北京植物园10种常绿植被为研究对象,应用空气气溶胶再发生器(QRJZFSQ-I)测定了北京市常见6种乔木和4种灌木叶片对空气总悬浮颗粒物(TSP)、PM10、PM2.5和PM1.0的吸附能力.结果表明:1不同树种叶片表面附着颗粒物的能力差异明显,最高的是雪松(Cedrus deodara)和油松(Pinus tabuliformis),吸附量分别是(18.95±0.71)μg·cm-2和(14.61±0.78)μg·cm-2,冷杉(Abies fabri)最小,为(8.02±0.4)μg·cm-2;2不同树种叶片单位面积对不同颗粒物的附着能力也存在差异,附着PM10能力最强的是油松和雪松,附着PM2.5能力最强的是雪松、铺地柏(Juniperus procumbens)、龙柏(Juniperus chinensis cv.kaizuka)和油松,附着PM1.0能力最强的是雪松、铺地柏、冷杉和油松;3不同月份叶片上附着的各粒级颗粒物(PM10、PM2.5)占TSP的比例不同.其中PM10在4~6月之间主要表现两种变化趋势,一是先上升后下降,主要为灌木树种;二是逐渐上升,主要的树种是乔木树种.而PM2.5则没有这种明显的变化趋势.     

Formation of Two Xylem Frost Injuries in One Annual Ring in Siberian Spruce under Conditions of Western Siberian Forest-Tundra

The phenomenon of the formation of two damaged xylem layers in one frost ring is described in Siberian spruce grown in western Siberian forest-tundra. Temperature conditions providing for the formation of pathological cell and tissue structures are determined. The relationship between the formation of double frost injuries and cold periods is demonstrated.     

秦皇岛市4种常见绿化植物滞尘效应研究

通过对秦皇岛市4种常见绿化植物的叶片滞尘量、单株滞尘能力及相关因素的研究,发现绿化植物单位叶面积的滞尘量最大可以相差2.5倍左右,树种叶片滞尘能力的大小排序为：国槐＞悬铃木＞金银木＞连翘,单株滞尘能力的大小排序为：悬铃木＞国槐＞金银木＞连翘;绿化植物的滞尘量与环境中粉尘含量成正比,树木滞尘能力还与叶片表面性状、叶面积指数及植物所处环境等因素有关。