A CFD based explosion risk analysis methodology using time varying release rates in dispersion simulations

A full probabilistic Explosion Risk Analysis (ERA) is commonly used to establish overpressure exceedance curves for offshore facilities. This involves modelling a large number of gas dispersion and explosion scenarios. Capturing the time dependant build up and decay of a flammable gas cloud size along with its shape and location are important parameters that can govern the results of an ERA. Dispersion simulations using Computational Fluid Dynamics (CFD) are generally carried out in detailed ERA studies to obtain these pieces of information. However, these dispersion simulations are typically modelled with constant release rates leading to steady state results. The basic assumption used here is that the flammable gas cloud build up rate from these constant release rate dispersion simulations would mimic the actual transient cloud build up rate from a time varying release rate. This assumption does not correctly capture the physical phenomena of transient gas releases and their subsequent dispersion and may lead to very conservative results. This in turn results in potential over design of facilities with implications on time, materials and cost of a project.In the current work, an ERA methodology is proposed that uses time varying release rates as an input in the CFD dispersion simulations to obtain the fully transient flammable gas cloud build-up and decay, while ensuring the total time required to perform the ERA study is also reduced. It was found that the proposed ERA methodology leads to improved accuracy in dispersion results, steeper overpressure exceedance curves and a significant reduction in the Design Accidental Load (DAL) values whilst still maintaining some conservatism and also reducing the total time required to perform an ERA study.     

An integrated model for vent area design of hydrocarbon-air mixture explosion inside cubic enclosures with obstacles

This study aims to develop an integrated model - NFPA-68-BRANN model, which can be used to calculate the vent areas of cubic enclosures with obstacles. Seven experiments regarding vented explosion inside the obstructed enclosure are reviewed and applied to check the accuracy of two existing standards, i.e. the NFPA-68 2018 and the BS EN 14994:2007. Accordingly, the parameters to describe the flame development in the NFPA-68 2018 are amended by adopting the Bauwens model. Bayesian Regularization Artificial Neuron Network (BRANN) model presenting the non-linear relationship between the turbulent flame enhancement factor X and its affecting factors is subsequently developed. Eventually, the NFPA-68-BRANN model is generated by incorporating the BRANN model into the modified NFAP-68 2018. The accuracy of the NFPA-68-BRANN model is validated by using a series of the New Baker Test data.     

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.     

基于CFD的深水钻井溢油事故定量风险评估

针对深水钻井作业过程中的井喷溢油问题,基于计算流体力学(CFD)方法,通过UDF函数给定海流流剖面、波浪入口边界条件和海水静压分布情况,结合标准k-ε方程,采用VOF模型实现对油、气、水三相自由面的追踪,建立了溢油扩散事故数值仿真模型,评估深水条件下溢油扩散危害区域,研究海流流速、溢油量对原油扩散的影响。结果表明,海流流速和溢油量是原油扩散行为和危害区域分布范围的重要影响因素。     

Real-time risk assessment of explosion on offshore platform using Bayesian network and CFD

Combustion or explosion accident resulting from accidental hydrocarbon release poses a severe threat to the offshore platform's operational safety. Much attention has been paid to the risk of an accident occurring over a long period, while the real-time risk that escalates from a primary accident to a serious one was ignored. In this study, a real-time risk assessment model is presented for risk analysis of release accidents, which may escalate into a combustion or explosion. The proposed model takes advantage of Fault Tree-Event Tree (FT-ET) to describe the accident scenario, and Bayesian network (BN) to obtain the initial probability of each consequence and describe the dependencies among safety barriers. Besides, Computational Fluid Dynamics (CFD) is applied to handle the relationship between gas dispersion and time-dependent risk. Ignition probability model that considering potential ignition sources, gas cloud, and time series are also integrated into this framework to explain the likelihood of accident evolution. A case of release accidents on a production platform is used to test the availability and effectiveness of the proposed methodology, which can be adopted for facilities layout optimization and ignition sources control.     

Accident modeling of toxic gas-containing flammable gas release and explosion on an offshore platform

Toxic gas-containing flammable gas leak can lead to poisoning accidents as well as explosion accidents once the ignition source appears. Many attempts have been made to evaluate and mitigate the adverse effects of these accidents. All these efforts are instructive and valuable for risk assessment and risk management towards the poisoning effect and explosion effect. However, these analyses assessed the poisoning effect and explosion effect separately, ignoring that these two kinds of hazard effects may happen simultaneously. Accordingly, an integrated methodology is proposed to evaluate the consequences of toxic gas-containing flammable gas leakage and explosion accident, in which a risk-based concept and the grid-based concept are adopted to combine the effects. The approach is applied to a hypothetical accident scenario concerning an H₂S-containing natural gas leakage and explosion accident on an offshore platform. The dispersion behavior and accumulation characteristics of released gas as well as the subsequent vapor cloud explosion (VCE) are modeled by Computational Fluid Dynamics (CFD) code Flame Acceleration Simulator (FLACS). This approach is concise and efficient for practical engineering applications. And it helps to develop safety measures and improve the emergency response plan.     

障碍物排列方式对海洋平台蒸气云爆炸的影响

为了了解障碍物排列方式对海洋平台蒸气云爆炸的影响,基于CFD方法建立蒸气云爆炸计算模型,选用国外MERGE项目的系列爆炸实验进行模型验证,提出用于衡量障碍物排列不均匀度的量化参数,针对海洋平台典型结构形式,分析障碍物排列方式对爆炸强度的影响。研究结果表明:蒸气云爆炸后果对于结构排列方式比较敏感,结构障碍物间隔均匀排列的形式造成的爆炸冲击作用最大;在爆炸发展初期阶段,障碍物阻塞程度对超压产生和发展影响更加显著。最后,基于研究结果,给出在海洋平台油气泄漏危险区将管线沿甲板非均匀排列布置等防控建议,为海洋平台蒸气云爆炸安全防控提供理论指导。     

Advanced multi-perspective computer simulation as a tool for reliable consequence analysis

Major accidents involving hazardous materials are a crucial issue for the chemical and process industries. Many accidental events taken place in the past showed that dangerous substances may pose a severe threat for people and property. Aiming at loss prevention, a series of actions have been instituted through international regulations concerning hazardous installations safety preparedness. These actions involve efficient land-use planning, safety studies execution, as well as emergency response planning drawing up. A key factor for the substantial consideration of the above is the effective prediction of possible accident forms and their consequences, for the estimation of which, a number of empirical models have been developed so far. However, (semi-)empirical models present certain deficiencies and obey to certain assumptions, thus leading to results of reduced accuracy. Another approach that could be used for this purpose and it is discussed in this work, is the utilization of advanced computational fluid dynamics (CFD) techniques in certain accident forms modeling. In particular, composite CFD-based models were developed for the simulation of several characteristic accident forms involving isothermal and non-isothermal heavy gas dispersion, confined and unconfined explosion in environment of complex geometry, as well as flammable cloud fire. The simulation cases were referred to real-scale trials allowing us to conclude about the validity of the quantitative results. Comparisons of the computational predictions with the experimental observations showed that obtained results were in good agreement with the experimental ones, whereas the evaluation of statistical performance measures proved the simulations to be statistically valid.     

Evaluation of a CFD porous model for calculating ventilation in explosion hazard assessments

In the past, gas explosion assessment relied on worst case scenarios. A more realistic approach is to look at the probability of explosions and their likely severity. The most flexible way of investigating many different scenarios is to estimate a ventilation flow, feed this into a flammable volume calculation and then calculate the explosion severity. The procedure allows many parameters to be varied efficiently. A Computational Fluid Dynamics porous model is evaluated for modelling the ventilation flow through congested regions, including a new method that has been developed to derive the resistance. Comparison with velocity measurements from a large scale model of an offshore module showed that overall the CFD model performs very well, especially considering that the homogenous porosity block does not model any of the internal obstructions and therefore would not predict any local flow effects. This gives confidence that the overall flow pattern is sufficiently close to the local flow patterns, to be used in explosion assessments. The porous approximation in CFX is found to underpredict the turbulence intensity in the obstacle array compared to the explosion model EXSIM. Improving the turbulence prediction in the porous model would be valuable, so a relatively simple method of increasing the turbulence in porous regions is proposed. The CFD model will provide the non-uniform natural ventilation flowfields of complex regions for future explosion assessments at a hierarchy of levels.     

Fuzzy logic approach to calculation of thermal hazard distances in process industries

In this paper, a general procedure to deal with uncertainties in each stage of consequence modeling is presented. In the first part of the procedure, the sources of uncertainty are identified and confirmed by sensitivity analysis for the source term, dispersion, physical effects and consequence analysis. While the second part comprises an application of the fuzzy logic system to each step of the consequence modeling. The proposed procedure is verified by the case study for a pool fire liquefied natural gas (LNG) on water. The results in terms of thermal radiation distances are compared with calculations obtained using the Monte Carlo method and with experimental data. The consequence model based on fuzzy logic approach provides less uncertain and more precise results in comparison to the deterministic consequence model.     

An engineer-based methodology to perform Explosion Risk Analyses

Explosion Risk Analyses (ERA) are usually performed as part of the Quantitative Risk Assessment (QRA). The combination of frequencies and associated consequences allow to get a risk picture of the facility and provides decision support to the risk owner. The outcomes of this study allow also to provide, after adequate interpretation, Design Explosion Loads (DEL) to engineering disciplines (e.g. structures, piping and equipment) according to a given Risk Acceptance Criterion (RAC). For most of the offshore applications, the consequence part of the ERA is done with Computational Fluids Dynamics (CFD) to properly handle congestion and confinement effects as simple models cannot. With the increase of computational power, thanks to Moore's Law, there is an increasing trend to perform more and more CFD simulations with the expectation to improve confidence in results while taking more and more probabilistic variables into account. In the early 2000s, it was 10's of simulations, in the 2010s, it was 100's and now it is common to reach 1000's. However, one should remark that there is still a lot of uncertainties behind these studies since the geometry maturity is generally not enough especially at the early stage of detailed engineering when the preliminary Design Explosion Loads (pDEL) should be provided to disciplines. Anticipated congestion is normally put in the model, but it usually put a bias at the beginning of the consequence modelling part. In the risk-based approach, the frequency part is also of major importance. One need to keep in mind that consequence refinement should be done in close relation with the frequency refinement to ensure consistency in the approach. The practical methodology presented in this paper was developed to provide reliable inputs to engineering disciplines, taking into consideration uncertainties and potential spread of results while using a reasonable number of CFD scenarios. Finally, the safety engineers are still the key contributor in the performance of the ERA, and hence brain-based design is kept in the loop while minimizing computer-based design.     

Computational fluid dynamics simulation of fog clouds due to ambient air vaporizers

Ambient air vaporizers (AAVs) are widely used to regasify liquefied industrial gases, which are liquefied for transport and storage. Depending on the conditions (temperature and relative humidity) of ambient air and AAV effluent, the potential exists for the formation of fog as the two fluids mix with each other. This has raised some regulatory and environmental concerns that the fog cloud may impact human activities in the vicinity of the AAV arrays.This paper describes a CFD-based modeling approach to predict the formation, dispersion and dissipation of a fog cloud due to AAV operation. The model uses the psychrometrics equations to determine when saturated air conditions are reached and to calculate mass and energy transfer between the moist air and the fog cloud. A parametric study is presented, based on an array of 6 AAVs, to demonstrate the effects of wind speed and AAV discharge elevation on the behavior of the fog cloud.     

Classification of dispersibility for combustible dust based on Hausner ratio

Mixing of combustible dust and oxidant is one of five essential prerequisites in the dust explosion pentagon, requiring that particles originally in mutual contact within the deposits be separated and suspended in the air. However, dust dispersion never proceeds with 100% efficiency, with inevitable particle agglomeration, and an inherent trend toward settling out of suspension. Dispersibility is defined to describe the ease of dispersion of a dust and the tendency of the particulate matter to remain airborne once a dust cloud has been formed. Pioneers made contributions to classify dust dispersibility by introducing dustiness group (DG), dustability index (DI), NIOSH dispersion chamber and in-situ particle size analysis. Issues remained including the difficulty in comparing results from different methods, as well as the availability of some high-tech testing apparatus.This study aims to provide a quick and universal testing method to estimate the dispersion property of combustible dust. A new dispersibility classification was developed based on dimensionless numbers Hausner ratio and Archimedes number. Four dispersibility classes (DCs) were proposed from one to four, with a larger number meaning better dispersibility. Results for more than a dozen dust samples and mixtures thereof showed the new method is useful in dust explosion research. The consistency in classifying dust dispersion properties between the DC method and previous methods was good. Changes in DC well explained our earlier findings on suppressant enhanced explosion parameter (SEEP) phenomenon attributed to the improvement in dust dispersibility. Hausner ratio and Archimedes number, as easily measured parameters, can be quite advantageous to assess dust dispersibility, permitting a proper risk assessment for the formation of explosible dust clouds.     

Influence of dustiness on small-scale vented dust explosions

A new safety characteristic the “dustiness” according to VDI 2263 – part 9 (Verein Deutscher Ingenieure, 2008) is investigated. Dustiness means the tendency of a dust to form clouds. The paper deals with the physical reasons for the different behavior of dusts, even if they have similar properties such as particle size and density and the influence of the dustiness on dust explosions. In order to study the effects of the dustiness on dust cloud formation for different dispersion methods experiments in a vertical dust dispersion glass tube apparatus were carried out. Furthermore vented dust explosion experiments were done for two different dispersion methods and two static activation pressures.Experiments show that particle size and density are not the only factors which influence dispersibility. Particle shape, specific surface area, flow and dispersion method have an influence which can outweigh size and density. Preliminary explosion experiments showed that the dustiness has an influence on the reduced explosion pressure and flame speed in a vented 75 L test apparatus. In order to verify the results for applications in the process industries further tests with industrial scale experiments are planned.     

基于动态风险平衡的海洋平台事故连锁风险研究

针对海洋平台事故风险特点,提出动态风险平衡概念,以此建立事故动力模型,并将该模型运用到墨西哥湾"深水地平线"井喷事故。动态风险平衡表征事故动力与事故阻力之间的动态平衡状态,具有动态性和暂时稳定性。事故动力模型以海洋平台可能发生的重大事故为研究对象,从工艺、技术和管理等角度分析事故可能致因和事故发展可能影响因素。该模型首先分析对象的初始事故动力,建立事故连锁风险图,然后计算初始动力发生情况下,传递动力和传递阻力的概率分布,最后提出相应风险控制措施。实例分析表明,基于动态风险平衡建立的事故动力模型能有效分析海洋平台事故连锁风险。     

PDE accident model from a safety information perspective and its application to Zhangjiakou fire and explosion accident

Accident models can provide theoretical frameworks for determining the causes and mechanisms of accidents, and thus are theoretical bases for accident analysis and prevention. The role of safety information in accident causation is profound. Thus, safety information is an important and essential perspective for developing accident models. This study presents a new accident model developed from a safety information perspective, called the Prediction—Decision—Execution (PDE) accident model. Because the PDE accident model is an emerging accident model that was proposed in 2018, its analysis logic and viability remain to be discussed. Thus, the main contributions of this study include two aspects: (i) detailed explanation of the analysis logic of the PDE accident model, and (ii) case-study examination of the Zhangjiakou fire and explosion accident, a serious accident that occurred in China in 2018, to demonstrate the viability of the PDE accident model. Results show that this is a safety-information-driven accident model that can provide a new and effective methodology for accident analysis and prevention, and safety management.     

Uncertainty techniques in liquefied natural gas (LNG) dispersion calculations

The dynamic development of the LNG sector increases the risk of major accidents. Uncontrolled releases of LNG during the processes of manufacturing, distribution, storage, and regasification can pose a serious threat to people, facilities, and the environment. Therefore, an important goal is to determine hazard zones and the extent of potential consequences associated with a release of LNG. The key issue is to estimate these with the least level of uncertainty. The largest part of uncertainty comes from the modeling of LNG release sources and performing dispersion calculations. It is connected with the application of different mathematical models, the adoption of a number of simplifying assumptions, approximations, empirical relations, constants, and a lack of knowledge.This paper proposes a general procedure for calculating the release rate and duration time of the LNG release, pool spreading, vaporization, as well as dispersion, taking into consideration the uncertainty. The procedure consists of two parts. The first part concerns the sensitivity analysis to identify the most uncertain parameters of the LNG source term and dispersion models. The second part applies to two techniques used to include the uncertainty aspects of fuzzy sets and the Monte Carlo method for calculating hazard zones. In order to provide a basis for comparison between these two approaches, the shape of the membership functions used in the fuzzy methods are the same as the shape of the probability density function used in the Monte Carlo simulation. The case study, concerning an LNG release, illustrates the application of the proposed techniques.     

多元可燃气体爆炸极限理论预测模型研究*

为准确掌握和预测多元可燃气体的爆炸极限,开展2种多元可燃气体爆炸极限的理论预测模型研究。第1种模型针对“多种可燃气体+多种惰性气体”在空气中或氧气中混合,基于求解可燃气体绝热火焰温度的总比热特性方法以及化学平衡反应中的贫燃料(富氧)反应,提出该多元可燃气体的爆炸下限预测模型;第2种模型针对“可燃气体+惰性气体+氧气”混合,基于热平衡方程及混合气体的各组分浓度、淬灭电势及燃烧潜热,提出该多元可燃气体的爆炸极限预测模型。结果表明:在预测多元可燃气体的爆炸极限时,第1种模型具有较广泛的应用性,且表现出较高的准确度;第2种模型具有使用简单的特点,且扩展了LCR（勒夏特列原理）的应用范围。     

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.     

A systems-theoretic approach for two-stage emergency risk analysis

Coronavirus disease (COVID-19) is an infectious disease that has dramatically spread worldwide. Regarding the safety issues of industries, there is a requirement of dealing with the emergency risk in the period of urgent situations. In this work, we proposed a systems-theoretic approach of the two-stage emergency risk analysis (ERA) based on the systems theory, that is the System-Theoretic Accident Model and Processes (STAMP). The two-stage ERA includes the normal to emergency risk analysis (N2E-RA) and emergency to normal risk analysis (E2N-RA). Besides N2E-RA, we advocate that E2N-RA is also an important and indispensable part of ERA. We elaborated the characteristics of N2E-RA and E2N-RA, separately. Eventually, based on our analysis, we provided recommendations for decision makers in preventing and controlling industrial accidents in the period of COVID-19.