Validation of geometry modelling approaches for offshore gas dispersion simulations

Computational Fluid Dynamics (CFD) codes are widely used for gas dispersion studies on offshore installations. The majority of these codes use single-block Cartesian grids with the porosity/distributed-resistance (PDR) approach to model small geometric details. Computational cost of this approach is low since small-scale obstacles are not resolved on the computational mesh. However, there are some uncertainties regarding this approach, especially in terms of grid dependency and turbulence generated from complex objects. An alternative approach, which can be implemented in general-purpose CFD codes, is to use body-fitted grids for medium to large-scale objects whilst combining multiple small-scale obstacles in close proximity and using porous media models to represent blockage effects. This approach is validated in this study, by comparing numerical predictions with large-scale gas dispersion experiments carried out in DNV GL's Spadeadam test site. Gas concentrations and gas cloud volumes obtained from simulations are compared with measurements. These simulations are performed using the commercially available ANSYS CFX, which is a general-purpose CFD code. For comparison, further simulations are performed using CFX where small-scale objects are explicitly resolved. The aim of this work is to evaluate the accuracy and efficiency of these different geometry modelling approaches.     

建筑施工事故非线性灰色伯努利模型预测

为提高建筑施工事故灰色预测模型精度,在传统GM(1,1)模型基础上,建立非线性灰色伯努利模型(NGBM),并采用粒子群优化(PSO)算法对参数进行优选。以2001—2011年全国建筑事故死亡人数统计数据为基础,运用该模型对2012—2013年的相应人数进行预测,并与GM(1,1)模型和灰色Verhulst模型的结果相对比。结果表明,NGBM拟合精度最好,平均相对误差仅为2.65%,验证了模型的可行性和准确性。     

基于组合赋权的混合粒子群优化支持向量机的岩爆倾向性预测

为有效预测岩爆灾害发生烈度,提出一种基于组合赋权的混合粒子群优化支持向量机(H-PSO-SVM)岩爆倾向性预测模型。根据岩爆发生机制,在分析岩爆发生的主要影响因素的基础上确定出评判指标;综合考虑模糊层次分析法(FAHP)所得主观权重和熵权法所得客观权重,应用调和平均数概念,构建组合赋权准则;引入遗传算法交叉、变异操作改进传统粒子群(PSO)极值跟踪和粒子更新方法,建立H-PSO-SVM岩爆倾向性预测模型。利用国内外已有工程实例数据进行50次随机抽样试验,对比分析H-PSO-SVM模型和PSO-SVM模型等预测结果。结果表明:H-PSO-SVM模型应用于岩爆工程实例预测具有可行性和适应性,模型预测的准确率高于其他模型,且预测结果更稳定。     

An improved methodology for assessing risk in aircraft operations at airports, applied to runway overruns

The paper addresses the need for models to assess risk at any particular airport, based on risk management principles used by the present risk management process, that use all available data on previous accidents. The case of runway overruns is taken as an example application, because new regulations require the provision of much longer Runway End Safety Areas than had previously been the norm. The paper presents models for overruns arising from both landings and aborted takeoffs. In each case models of overrun risk, of wreckage location and of the consequences are detailed. An example application of the models is then given in a hypothetical risk assessment.The models, though adding value to existing methods of assessing risk, are not as good as they could be, due to the lack of data on normal operations. It was therefore possible only to relate the rate of overruns to the rate of occurrence of the possible driving factors for a few factors where such comparable data on normal operations existed. It is recommended that effort be put into the collection of data to allow a more comprehensive analysis.     

Prediction of methanol loss in liquid hydrocarbon phase during natural gas hydrate inhibition using rigorous models

Methanol is the most widely used natural gas hydrate inhibitor and it is only effective as a hydrate inhibitor in the aqueous phase. Methanol is not regenerated in natural gas inhibition process due to its intermittent application in most cases. However, a significant cost is associated with the process because of methanol loss while utilizing this inhibitor. In this work, several intelligent models along with a new mathematical correlation are presented in terms of methanol concentration in aqueous phase and temperature to precisely forecast the methanol loss in the saturated hydrocarbons phase. An excellent match was noticed between the calculated results and literature data.     

大气扩散计算流体力学模型综合评估方法研究

模型评估方法研究是模型研发工作的重要组成部分。基于科学性验证与统计性确认提出了大气扩散CFD模型的综合确认评估方法,通过示例的方式展示了方法的使用效果。综合确认评估方法可以提高模型筛选的速度,降低统计性评估对确认性试验数据的需求量,从总体上提高模型确认的效率。评估理论的研究有助于提高基于模型的大气扩散研究的准确性,也有利于高精度模型和试验的设计、开发与遴选。     

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.     

Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform

Computational Fluid Dynamics (CFD) is routinely used in Explosion Risk Analysis (ERA), as CFD-based ERA offers a good understanding of underlying physics accidental loads. Generally, simplifications were incorporated into CFD-based ERA to limit the number of simulations. Frozen Cloud Approach (FCA) is a frequently used simplification in the dispersion part of the CFD-based ERA procedure. However, its accuracy is questionable in the complex and congested environment such as offshore facility. Furthermore, in explosion part, some specific techniques, e.g. linear/double bin-interpolated techniques have been proposed while the corresponding accuracy is still unknown since the developers did not yet check their accuracy by considering the explosion computational data as the benchmark.This study presents a more accurate algorithm, namely Bayesian Regularization Artificial Neural Network (BRANN) and accordingly proposes the frameworks regarding BRANN-based models for the CFD-based ERA procedure. Firstly, the framework is proposed to develop the Transient-BRANN (TBRANN) model for transient dispersion study. In addition, the framework to determine the BRANN model for explosion study is developed. The proposed frameworks are explained by a case study of the fixed offshore platform. Consequently, this study confirms the more accuracy of the TBRANN model over FCA and the accuracy of BRANN model for CFD-based ERA.     

Phast validation of discharge and atmospheric dispersion for pressurised carbon dioxide releases

The consequence modelling package Phast examines the progress of a potential incident from the initial release to the far-field dispersion including the modelling of rainout and subsequent vaporisation. The original Phast discharge and dispersion models allow the released substance to occur only in the vapour and liquid phases. The latest versions of Phast include extended models which also allow for the occurrence of fluid to solid transition for carbon dioxide (CO₂) releases.As part of two projects funded by BP and Shell (made publicly available via CO2PIPETRANS JIP), experimental work on CO₂ releases was carried out at the Spadeadam site (UK) by GL Noble Denton. These experiments included both high-pressure steady-state and time-varying cold releases (liquid storage) and high-pressure time-varying supercritical hot releases (vapour storage). The CO₂ was stored in a vessel with attached pipework. At the end of the pipework a nozzle was attached, where the nozzle diameter was varied.This paper discusses the validation of Phast against the above experiments. The flow rate was predicted accurately by the Phast discharge models (within 10%; considered within the accuracy at which the BP experimental data were measured), and the concentrations were found to be predicted accurately (well within a factor of two) by the Phast dispersion model (UDM). This validation was carried out with no fitting whatsoever of the Phast extended discharge and dispersion models.     

Risk assessment methodology for high-pressure CO2 pipelines incorporating topography

This paper presents a risk assessment methodology for high-pressure CO₂ pipelines developed at the Health and Safety Laboratory as part of the EU FP7 project CO2Pipehaz.Traditionally, consequence modelling of dense gas releases from pipelines at major hazard impact levels is performed using integral models with limited or no consideration being given to weather bias or topographical features of the surrounding terrain. Whilst dispersion modelling of CO₂ releases from pipelines using three-dimensional CFD models may provide higher levels of confidence in the predicted behaviour of the cloud, the use of such models is resource-intensive and usually impracticable. An alternative is to use more computationally efficient shallow layer or Lagrangian dispersion models that are able to account for the effects of topography whilst generating results within a reasonably short time frame.In the present work, the proposed risk assessment methodology for CO₂ pipelines is demonstrated using a shallow-layer dispersion model to generate contours from a sequence of release points along the pipeline. The simulations use realistic terrain taken from UK topographical data. Individual and societal risk levels in the vicinity of the pipeline are calculated using the Health and Safety Laboratory's risk assessment tool QuickRisk.Currently, the source term for a CO₂ release is not well understood because of its complex thermodynamic properties and its tendency to form solid particles under specific pressure and temperature conditions. This is a key knowledge gap and any subsequent dispersion modelling, particularly when including topography, may be affected by the accuracy of the source term.     

IAFSA-GRNN在油田集输管道CO2腐蚀速率预测中的应用

为提高油田集输管道CO₂腐蚀速率预测的准确性,针对原始广义回归神经网络(GRNN)预测精度低的问题,提出改进的群智能算法优化原始GRNN的预测模型;分别使用GRNN模型、人工鱼群算法(AFSA)优化的GRNN(AFSA-GRNN)模型和自适应改进的AFSA-GRNN(IAFSA-GRNN)模型预测X65管线钢的CO₂腐蚀速率。结果表明:采用AFSA和IAFSA优化光滑因子S后,能大大提高GRNN模型的预测精度,预测结果的平均相对误差由36.09%分别减小至7.20%和6.90%;与AFSA相比,IAFSA优化的GRNN不仅具有更高的预测精度,还具有更快的收敛速度。AFSA-GRNN在第164次迭代计算时收敛,而IAFSA-GRNN在第109次迭代计算时收敛,说明AFSA经自适应优化能提高优化过程的收敛速度和GRNN的预测精度。     

基于FA-BAS-ELM的海洋油气管道外腐蚀速率预测

为提高海洋油气管道外腐蚀速率预测的精度和效率,建立基于因子分析(FA)和天牛须搜索算法(BAS)的极限学习机(ELM)腐蚀速率预测模型。利用FA对影响因素数据集进行降维处理,确定预测模型的输入变量;建立ELM预测模型,并采用BAS对ELM模型的参数进行优化,避免参数取值随机性对模型预测性能的影响;以实海挂片试验为例,通过建模仿真评价模型的预测性能,并与其他模型进行对比分析。结果表明：FA-BAS-ELM预测模型的平均绝对误差(MAPE)仅为1.92%,决定系数R²高达0.994 9,相比于其他模型,该模型具有更优的预测性能。     

铁路行车事故预测方法分析与比较

对铁路行车事故的特点和类型进行分析;根据美国铁路2005年安全年报提供的数据,运用灰色系统理论和BP神经网络方法建立铁路行车事故的预测模型;利用MATLAB软件进行预测仿真,比较和分析两种预测方法的精度及特点。结果表明:灰色系统理论预测结果固定,短期效果比较好;BP神经网络预测具有适应性和灵活性,适用于长期预测。采用灰色系统理论和BP神经网络进行铁路行车事故的预测,克服了传统数学统计预测方法中建立复杂的数学模型,预测准确性低的缺点,对预防和控制铁路事故的发生,降低事故损失具有现实意义。     

A statistical model to estimate the probability of slip and fall incidents

A statistical model to estimate the probability of slip and fall incidents is presented. In estimating such an event, the available friction is usually compared with the required friction for the activities. In the models available in the literature, only mean values were compared or a minimum threshold was used to represent the required friction. In the current model, it is assumed that both the available and required friction coefficients have stochastic distributions. Formulations for a general distribution were developed and are presented. The results generated with the current model are compared with those based on other models using a normal distribution. The results indicate that there could be significant differences between the current model and the existing models in estimating the probability of a slip incident.     

高精度格式在河道水温模拟中的应用

水温是水环境质量评价的一项基本因素,准确的水温预测是许多水质模型的基础.水温变化过程可用对流扩散方程来描述,其模拟精度直接关系到水温模型的精度.结合交错网格,将高精度HAUC2格式应用到河道水温预测中,建立了一维河道水温模型,最后将模型应用于汉江皇庄--仙桃河段水温纵向输移扩散模拟中,取得良好的效果.     

Experts,Bayesian Belief Networks,rare events and aviation risk estimates

Bayesian Belief Networks (BBN) are conceptually sensible models for aviation risk assessment. The aim here is to examine the ability of BBN-based techniques to make accurate aviation risk predictions. BBNs consist of a framework of causal factors linked by conditional probabilities. BBN conditional probabilities are elicited from aviation experts. The issue is that experts are not being asked about their expertise but about others’ failure rates. A simple model of expertise, which incorporates the main features proposed by researchers, implies that a best-expert’s estimates of failure rates are based on accessible quantitative data on accidents, incidents, etc. Best-expert estimates will use the best available and accessible data. Depending on the frequency of occurrence, this will be data on similar events, on similar types of event, or general mental rules about event frequencies. These considerations, plus the need to be cautious about statistical fluctuations, limit the accuracy of conditional probability estimates. The BBN framework assumes what is known as the Causal Markov Condition. In the present context, this assumes that there are no hidden common causes for sequences of failure events. Examples are given from safety regulation comparisons and serious accident investigations to indicate that common causes may be frequent occurrences in aviation. This is because some States/airlines have safety cultures that do not meet ‘best practice’. BBN accuracy might be improved by using data from controlled experiments. Aviation risk assessment is now very difficult, so further work on resilience engineering could be a better way of achieving safety improvements.     

大气扩散模型统计性确认评估

使用计算模型预测物理过程之前,必须对该计算模型进行论证,确认评估通过后才能用于物理状态和过程的计算和预测。讨论了基于数据统计属性的确认评估对面向大气扩散物理现象的计算模型之适用性;探究了模型评估工作的基本内容、关键概念和通用原则;给出了确认评估参数的定义、选择原则及示例;分析了评估度量指标的内涵和适用条件;探讨了可接受模型需要满足的指标准则及统计性确认评估指标的选用;最后,指出了基于统计度量的确认评估方法的改进方向。扩散模型评估理论的探讨有助于提高基于模型仿真的大气扩散研究的准确性,也有利于高精度模型和基准试验的设计、开发与遴选。     

铸造起重机多单元装配与耦合建模分析

结构建模分析是工程应用领域中应用较为普遍的一种分析设计方法,对于简单的几何模型实体单元不仅能够得到较高的精度而且能够很好地再现结构受力特性,应力云图具有较高的展示度,而对于复杂的几何模型,实体模型不仅消耗大量的建模时间,而且求解时间也相对较长.文中分析了Solid-Shell-Beam(S-S-B)多单元建模及耦合技术原理,建立了铸造起重机S-S-B多单元装配模型,通过S-S-B多单元模型与实体模型的对比分析,验证了S-S-B单元装配模型计算准确性,最后利用S-S-B单元耦合分析方法计算了铸造起重机在空载、满载、偏载等典型工况下的结构受力情况,为铸造起重机的设计、校核及使用维护提供技术支撑.     

南京市化学原料及化学制品制造业能源消耗预测

化学原料及化学制品制造业作为南京市高能耗、高污染产业,对其能源消耗总量及能源消耗结构进行预测,对节能减排具有重要意义。以2002—2012年能源消耗量弱化之后的数据为原始数据,运用灰色GM(1,1)模型对2013—2020年南京市化学原料及化学制品制造业能源消耗总量以及主要能耗进行了预测。由于部分原始数据序列光滑性较差,所建预测模型的精度较低,使用平均弱化缓冲算子对其进行处理。结果表明,处理之后建模精度提高,预测的结果更加准确。     

External pressures generated by vented gas and dust explosions

Several different data correlations have been developed for the external pressures associated with vented gas explosions and dust explosions. These correlations, which are applicable to external locations in the direct line-of-sight of the enclosure vent, are reviewed here. In addition, the application of spherically symmetric and of ellipsoidal blast wave models is explored as a possible means of calculating external pressures over a wider range of conditions than is possible with the existing data correlations. Results indicate that the spherically symmetric blast wave model can obtain a comparable accuracy (8–9 kPa standard deviation) for line-of-sight locations as the more recent data correlations. In the case of the lower blast pressures at locations perpendicular to the vent line-of-sight, the ellipsoidal blast wave provides significantly better agreement with data (to within 1 kPa standard deviation for the one set of available test data) than the spherically symmetric model.