Comparison of explosion models for detonation onset estimation in large-scale unconfined vapor clouds

Although industrial denotations in semi-open and congested geometries are often neglected by many practitioners during risk assessment, recent studies have shown that industrial detonations might be more common than previously believed. Therefore, from the explosion safety perspective, it becomes imperative to better assess industrial detonation hazards to improve robustness of explosion mitigation design, emergency response procedures, and building siting evaluation. Having that in mind, this study aims to review current empirical vapor cloud explosion models, understand their limitations, and assess their capability to indicate detonation onset for elongated vapor clouds. Six models were evaluated in total: TNO Multi-Energy, Baker-Strehlow-Tang (BST), Congestion Assessment Method (CAM), Quest Model for Estimation of Flame Speed (QMEFS), Primary Explosion Site (PES), and Confinement Specific Correlation (CSC). Model estimations were compared with large-scale test data available in the open literature. The CAM model demonstrated good performance in indicating deflagration-to-detonation transition (DDT) for test conditions experiencing detonation onset without any modification in the methodology. Some suggestions are provided to improve simulation results from PES, BST and QMEFS.     

A stochastic approach for risk analysis in vapor cloud explosion

A stochastic approach for evaluating the risk of vapor cloud explosions is proposed in this work. The proposed methodology aims to incorporate the effect of uncertainty into the risk analysis to produce a better overall view for the risk. Some stochastic variables are used to estimate the probability of vapor cloud explosions: frequency of the release, the probability of not having an immediate ignition, the probability of delayed ignition and the probability of a vapor cloud explosion given a delayed ignition, as well as different possible meteorological conditions. These stochastic variables are represented with probability distribution curves. Different curves for the frequencies of releases from process equipment types (steel process pipes, flanges, manual valves, actuated valves, etc.), different equipment diameters and different leak sizes are also used in this analysis. Monte Carlo simulation is performed to obtain the risk as a probability distribution using the Analytic Solver Platform. Then the risk distribution curve obtained by Monte Carlo simulation is used to estimate the probability of satisfying the risk tolerance criterion.     

Explosion of solvent vapor in a ring partition of the floating roof

The relative importance of the vapor cloud explosion (VCE) hazard has grown in recent years. Many of large disasters were attributed to the VCE. This article introduced an explosion accident of solvent vapor in a ring partition of floating roof in detail. Source of explosive materials and ignition reason were analyzed, and the blast equivalency in ring partition was estimated in the specific conditions. The case would provide a reference for preventing the similar accident.     

Effect of separation distance on gas dispersion and vapor cloud explosion in a storage tank farm determined using computational fluid dynamics

Storage tank separation distance, which considerably affects forestalling and mitigating accident consequences, is principally determined by thermal radiation modeling and meeting industry safety requirements. However, little is known about the influence of separation distance on gas dispersion or gas explosion, which are the most destructive types of accidents in industrial settings. This study evaluated the effect of separation distance on gas dispersion and vapor cloud explosion in a storage tank farm. Experiments were conducted using Flame Acceleration Simulator, an advanced computational fluid dynamics software program. Codes governing the design of separation distances in China and the United States were compared. A series of geometrical models of storage tanks with various separation distances were established. Overall, increasing separation distance led to a substantial reduction in vapor cloud volume and size in most cases. Notably, a 1.0 storage diameter separation distance appeared to be optimal. In terms of vapor cloud explosion, a greater separation distance had a marked effect on mitigating overpressure in gas explosions. Therefore, separation distance merited consideration in the design of storage tanks to prevent gas dispersion and explosion.     

开敞空间可燃气云爆炸冲击波超压及灾害动力响应研究评述

为了进一步梳理和分析开敞空间可燃云爆炸冲击波超压传播规律及灾害动力响应方面的各项研究成果,推进可燃气体爆炸安全防控,减少人员伤亡和经济损失。在分析现有研究的基础上,总结开敞空间可燃气云爆炸冲击波超压传播规律及灾害动力响应研究等方面存在的不足,提出开敞空间多元混合气体爆炸冲击波超压传播规律研究、多影响参数下可燃气云爆炸冲击波超压传播规律定量分析、基于可燃气云爆炸冲击波超压作用下的承载体动力响应等未来研究的关键技术问题。     

A quantitative correlation of evaluating the flame speed for the BST method in vapor cloud explosions

In recent decades, vapor cloud explosions (VCEs) have occurred frequently and resulted in numerous personnel injuries and large property losses. As a main concern in the petrochemical industry, it is of great importance to assess the consequence of VCEs. Currently, the TNT equivalency method (TNT EM), the TNO multi-energy method (TNO MEM), and the Baker-Strehlow-Tang (BST) method are widely used to estimate the blast load from VCEs. The TNO MEM and BST method determine the blast load from blast curves based on the class number and the flame speed, respectively. To quantitatively evaluate the flame speed for the BST method, the experimental data is adopted to validate the confinement specific correlation (CSC) for the determination of the class number in the TNO MEM. As a bridge, a quantitative evaluation correlation (QEC) between CSC correlation and the flame speed is established and the blast wave shapes corresponding to different flame speeds are proposed. CFD software FLACS was used to verify the quantitative correlation with the numerical models of three geometrical scales. It is found that the calculated flame speeds by the QEC are in good agreement with the simulated ones. A petrochemical plant is selected as a realistic scenario to analyze the TNT EM, TNO MEM, BST method and FLACS simulations in terms of the positive-phase side-on overpressure and impulse at different distances. Compared with the flame speed table, the predicted overpressure from BST curves determined by the proposed QEC is closer to that from FLACS and more conservative. Furthermore, the predicted results of different methods are compared with each other. It is found that the estimated positive-phase side-on overpressure and impulse by the TNO MEM are the largest, and the estimated impulse by the TNT EM is the smallest. Moreover, the estimated overpressure and impulse are larger in the higher reactivity gas.     

A multivariable approach for estimation of vapor cloud explosion frequencies for independent congested spaces to be used in occupied building risk assessment

API Recommended Practice 752 is one of the most referenced practices for evaluating vapor cloud explosion (VCE) impacts to site occupied buildings. This reference introduces generic VCE frequencies for different types of process units that are based on VCE incidents database. Although these reported VCE frequencies are not capable of illustrating all parameters that affect explosion likelihood, they are widely used in risk analysis studies and software packages. This paper delineates the structure of a more realistic method for estimation of local VCE frequencies for independent congested spaces or units as a function of process, site, and meteorological variables. Compared to traditional methods for VCE frequency estimation, the new proposed approach is supported by an obviously more populated and precisely categorized database of leakage frequencies and features a multi-variable functionality of process/plant conditions. Contrary to previous procedures that aimed at finding the frequency of occurrence for a single VCE incident, this proposed methodology characterizes each congested space with a local VCE frequency. This frequency is an integration of the frequencies pertaining to VCE's that are likely to be initiated by each congested space. This new VCE frequency can also be used to determine the level of explosion hazard in each unit and in risk matrix analysis.     

基于改进云模型-IAHP的涉爆粉尘企业安全风险评估

为了评估涉爆粉尘企业安全风险,克服传统评估过程中数据随机性和模糊性的缺陷,建立了基于云理论和区间层次分析法(IAHP)的涉爆粉尘企业安全风险评估模型。针对粉尘爆炸事故的特点构建涉爆粉尘企业安全风险指标体系,使用云理论为主客观指标赋值,采用IAHP确定各指标权重并引入可能度修正权重结果,基于改进的云合并算法整合计算结果,以综合评估企业粉尘爆炸风险,并将该模型应用于某金属加工企业。研究结果表明:该企业的粉尘爆炸风险处于一般水平,其工人安全意识薄弱、防尘及防爆措施不完善、生产布局和管理不规范等原因导致了该企业的风险,该模型评估结果与实际情况相符,为提高企业安全管理水平提供依据。     

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.     

基于ISM二维云模型的应急管理协同度研究

为研究应急管理系统的内部结构和协同效果，基于ISM二维云模型展开应急管理协同度研究。运用结构解释模型(ISM)剖析应急管理复杂系统内部的层次关系，依据影响因素递阶层次图构建应急管理协同度评价指标体系；提出1种递阶中心度权重法用于求解各指标的权重系数，用中心度与层级系数反映各指标的重要性与结构性；将二维云模型引入到应急管理协同度研究，处理各指标预防阶段和响应阶段的模糊性与随机性问题；应用上述方法对京津冀应急管理协同度进行探索性研究，结果表明:京津冀应急管理综合协同度等级为良，3地各部门之间的横向信息沟通有待加强，需提高应急预案在响应阶段的可行性，同时加大预防阶段的科技支撑能力，研究方法和结论可为以京津冀为代表的城市群的应急管理协同水平的持续提高提供参考和借鉴。     

基于云模型的长时停工隧道衬砌结构质量评价

基于长时停工隧道衬砌结构质量评价中通常存在的定性描述和定量转化问题,引入一种基于云模型理论的长时停工隧道衬砌结构质量评价方法。根据长时停工隧道衬砌结构的特点,选取锚杆数量和衬砌厚度等20项评价指标,建立了长时停工隧道衬砌结构质量分级的评价模型;通过正向正态云发生器生成对应的云模型参数,再结合各评价因子的权重,获得云模型的综合确定度,最后利用最大隶属度原则确定长时停工隧道衬砌结构质量状态等级;将该模型应用于某一长时停工隧道工程实际,评定了该隧道衬砌结构的质量状态等级。结果表明,该方法不仅能给出长时停工隧道衬砌结构的质量等级,还能客观反映长时停工隧道衬砌结构中各因素的合格情况。     

A method for simulation of vapour cloud explosions based on computational fluid dynamics (CFD)

The effectiveness of the application of CFD to vapour cloud explosion (VCE) modelling depends on the accuracy with which geometrical details of the obstacles likely to be encountered by the vapour cloud are represented and the correctness with which turbulence is predicted. This is because the severity of a VCE strongly depends on the types of obstacles encountered by the cloud undergoing combustion; the turbulence generated by the obstacles influences flame speed and feeds the process of explosion through enhanced mixing of fuel and oxidant. In this paper a CFD-based method is proposed on the basis of the author’s finding that among the various models available for assessing turbulence, the realizable k-? model yields results closer to experimental findings than the other, more frequently used, turbulence models if used in conjunction with the eddy-dissipation model. The applicability of the method has been demonstrated in simulating the dispersion and ignition of a typical vapour cloud formed as a result of a spill from a liquid petroleum gas (LPG) tank situated in a refinery. The simulation made it possible to assess the overpressures resulting from the combustion of the flammable vapour cloud. The phenomenon of flame acceleration, which is a characteristic of combustion enhanced in the presence of obstacles, was clearly observed. Comparison of the results with an oft-used commercial software reveals that the present CFD-based method achieves a more realistic simulation of the VCE phenomena.     

煤与瓦斯突出预测的QGA-LSSVM模型

为快速、有效地对煤与瓦斯突出类型作出预测,运用灰色关联和因子分析模型对所选主要的判别指标进行分析提取,利用量子遗传算法(QGA)对最小二乘支持向量机(LSSVM)的参数作寻优处理,最终建立QGA-LSSVM煤与瓦斯突出预测模型。选取从砚石台矿区历史实测的数据,以96∶20的比例对该模型进行训练与测试,并将预测结果与其他预测模型的预测效果进行了比较。研究结果表明:对判别指标进行灰色关联分析可以有效去除对煤与瓦斯突出影响作用小的指标;用因子分析进行公共因子提取,可以有效减少数据信息冗余;利用QGA优化的LSSVM模型能使结果避免陷入局部最优解,用该模型可以有效预测煤与瓦斯突出类型,误判率为0。