تجزیه و تحلیل حساسیت از مدل پراکندگی جوی Phast برای سه مواد سمی (اکسید نیتریک، آمونیاک، کلر)
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|26559||2012||13 صفحه PDF||سفارش دهید||9232 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Loss Prevention in the Process Industries, Volume 25, Issue 1, January 2012, Pages 20–32
We present the results of a parametric sensitivity analysis of a widely used model for atmospheric dispersion of toxic gases, in order better to understand the influence of user-adjustable parameters on model outputs. We have studied 60 min continuous release scenarios for three different products (nitric oxide, ammonia and chlorine), chosen to cover a range of physical characteristics and storage conditions. For each product, we have broken down base-case scenarios into a number of sub-scenarios corresponding to different release conditions which determine physical phenomena (flow rate, release angle, release elevation and atmospheric stability class). The use of statistical tools to analyze the results of a large number of model executions allows us to rank model parameters according to their influence on the variability of a number of model outputs (distances and concentrations), on a per-scenario and per-product basis. Analysis of the results allows us to verify our understanding of the modeling of cloud dispersion.
The prevention of technological risks requires industrial sites to estimate the consequences of different accident scenarios based on a probabilistic risk assessment. An important contribution to the calculation of the consequences comes from the modeling of atmospheric dispersion, particularly of the accidental release of toxic products. Given the implications in terms of land-use planning, it is important that the calculations be based on the best scientific knowledge available. This subject has motivated numerous studies since the early 1980s through the development of numerical models which are currently used for loss prevention purposes in chemical process engineering. In order to increase confidence in these programs, a subsequent effort has been spent to validate dispersion models by comparing measured and computed data (Calay and Holdo, 2008, Hanna et al., 2009, Kisa and Jelemensky, 2009, Luketa-Hanlin et al., 2007 and Middha et al., 2009) according to recommendations edited by ASTM (2005) for example. Dispersion models can be classified into three categories, which are, from the less to the more complex, Gaussian models, integral-type models and 3D or computational fluid dynamics (CFD) models. Gaussian models are derived from the diffusion equation and from observations made by experimental work, i.e. the pollutant concentration follows a Gaussian distribution, whose standard deviations are dependent on the atmospheric turbulence and the distance from the source or the duration since the beginning of the release. These models are appropriate for passive clouds and therefore for the last stage of heavy-gas dispersion (passive dispersion). Integral-type models are simplifications of the conservation equations for mass, momentum and energy. They model the transitions between different stages of heavy-gas dispersion: slumping and creeping, transition phase and finally passive dispersion. These box models provide relatively easy and fast dispersion estimations. Some of them, like ALOHA, DEGADIS, HEGADAS and Phast’s UDM, are among the most popular and widely used in safety engineering applications. Despite the convenience they offer in their application, they appear to have some drawbacks: some physical phenomena use semi-empirical relationships whose parameters have been tuned on field test data; since the trials usually do not include obstacles, they can provide reliable results only in open field conditions. In order to analyze the effect of terrain and of large obstacles on gas dispersion, CFD codes (such as CFX, FLUENT, FLACS and FDS) have been developed. This approach (simultaneous resolution of balance equations of mass, momentum and energy) allows a full three-dimensional analysis to be performed. The wind velocity is completely resolved, unlike simpler models where velocity is a single value or a function of height. They can deal with heavy, neutral or light gas dispersion. While providing more detailed results, they require more computational resources and analyst skill. They are starting to play an important role in risk assessments for the process industry because they have the potential better to assess the impact of certain barrier systems and of terrain effects. In this context, a number of recent papers have compared simulation results obtained by CFD and integral models (Fiorucci et al., 2008, McBride et al., 2001, Mouilleau and Champassith, 2009, Pontiggia et al., 2010 and Riddle et al., 2004) or aimed to improve turbulence modeling (Pontiggia et al., 2009 and Sklavounos and Rigas, 2004), often the main cause of gaps between observations and numerical simulations. The modeling of the impact generated by an accidental release of hazardous chemical depends on a number of parameters related to the release type, to the product and to the software itself: conditions under which the dispersion occurs (meteorological and environmental), physical properties and toxicity of the chemical and internal parameters of the modeling tool. Simulation results may depend quite strongly on the values chosen for some of these parameters. While flexibility in the parameter choice is useful, it can lead to effect distances that vary considerably when different experts study the same scenario (CCPS, 1996 and MEEDDAT, 2008). An important technique for developing confidence in one’s understanding of a model is sensitivity analysis, which evaluates how variations in a model’s outputs can be apportioned to variations in the inputs. The most basic sensitivity analysis methods consist of varying input parameters one at a time (“OAT”) while holding other parameters at central values, so the sensitivity indices derived are dependent on these central values. More sophisticated sensitivity analysis techniques examine the global response (averaged over the variation of all parameters) of model outputs by exploring the entire input space: these are global sensitivity analysis methods (Saltelli, Chan, & Scott, 2004). Previous work on sensitivity analysis of atmospheric dispersion models has been limited to OAT methods. In 1989, Kakko published a quantitative sensitivity analysis of the RISKIT program by varying source term parameters, surface roughness and local weather characteristics. Kok, Eleveld, and Twenhöfel (2004) have carried out a sensitivity analysis of NPK-PUFF, a Lagrangian code used to model release scenarios of radioactive contaminants. Ferenczi (2005) undertook an OAT sensitivity analysis of a RIMPUFF code which models radioactive pollutant dispersion. Bubbico and Mazzarotta (2008) have applied an OAT method to 15-min accidental toxic release scenarios of hydrogen chloride, ammonia, trimethylamine and bromine using ALOHA and Trace 9.0 software tools. More recently, Cormier, Qi, Yun, Zhang, and Mannan (2009) have carried out a sensitivity analysis of the CFX CFD code concerning a limited number of parameters (turbulence models, source term and meteorological conditions) to assess the effects on the distance to Lower Flammability Limit and the concentration levels of LNG releases. This paper presents our work on a global parametric sensitivity analysis of the Unified Dispersion Model (UDM) of Phast, one of the most comprehensive computer programs for the modeling of accidental releases, used by companies and the competent authorities. We present results concerning three materials which are relevant for safety reports: nitric oxide (NO), ammonia (NH3) and chlorine (Cl2).
نتیجه گیری انگلیسی
In this paper, we have carried out a parametric sensitivity analysis of Phast’s Unified Dispersion Model in order better to understand the influence of user-adjustable input parameters on model outputs. The EFAST variance-based global method used has provided first-order sensitivity indices, to characterize the intrinsic influence of each parameter, and total sensitivity indices which represent a parameter’s influence including its joint interaction with all other input parameters. We have examined 1 h continuous release scenarios relative to three toxic materials important for safety studies: nitric oxide (passive gas stored as a pressurized gas), ammonia (lighter than air stored as liquefied gas) and chlorine (denser than air stored as liquefied gas). The numerous input variables and parameters of Phast have been distributed into four classes: (A) source term and weather conditions, (B) model selection parameters, (C) internal model parameters and (D) numerical resolution parameters. For each material, the base-case scenario is divided into representative sub-scenarios according to the release conditions which determine physical phenomena (flow rate, release angle, release elevation and atmospheric stability class). A number of relevant outputs related to toxic threshold values have been selected (distances and concentrations). For each sub-scenario, sensitivity analyses have been undertaken by simultaneously varying the parameters of class B, C and D over wide input ranges. The results have allowed ranking model parameters according to their direct influence on the variability of selected model outputs, per-scenario and per-product. Moreover, interpretations of the results have been proposed by considering the specific physico-chemical properties of the selected materials which exhibit different behavior during dispersion. Parameters’ impact is studied separately for the near, intermediate and far field, allowing us to verify our understanding of how the cloud disperses. This work provides users of Phast a better understanding of the influence of a number of parameters on the simulation of accidental dispersion scenarios, which is particularly interesting considering its implication in the calculation of safety perimeters and consequently in land-use planning. Note that in ordinary use of Phast, only variables in class A would be set by the user, with other parameters left at their default setting. Parameters in classes B, C and D are occasionally changed by users better to match specific features of the release being modeled, or to overcome convergence problems. From the point of view of Phast developers, it helps to distinguish between adjustable parameters whose influence is significant for certain products and release conditions (and which may deserve further investigation), and parameters whose influence is negligible. In the continuity of this study, we will analyze the sensitivity of Phast parameters in a narrow range around their default values, which better corresponds to ordinary use of the tool.