Investigating impacts of cost functions to atmospheric dispersion modeling and source term estimation in turbulent condition

doi:10.11949/0438-1157.20191550

Abstract

Abstract:

Hazardous gas release will threaten social security and cause damage to property and environment. Establish an effective emergency response system with monitoring devices can help reduce the potential risk and provide evaluation for release accident emergencies. The gas concentration is detected by the gas concentration sensor arranged in the risk area, and the leakage assessment is carried out according to the meteorological conditions and the forward diffusion model. Because the data of leakage scenarios is not easy to obtain, the large eddy simulation method based on Fires Dynamics Simulator (FDS) is used in this paper to generate date through simulating release scenarios with turbulent condition. On this basis, the impacts of the cost function to the modeling process and source term estimation are studied. The results of source term estimation shows the function of the square sum of concentration deviation is better than other functions. However, the function cannot avoid the condition that the estimation index of some scenes is far from the average value because of turbulent flow. In addition, a combination of the estimation results of different cost functions can improve the situation to a certain extent.

Key words: release, dispersion, model, turbulent condition, source term estimation, cost function

摘要：

化学气体泄漏会对生命财产安全及环境造成重大破坏，建立有效的监测装置及评估系统能够在发生泄漏事故时为应急救援提供辅助决策，有效降低泄漏带来的潜在风险。通常会通过在风险区域排布的气体浓度传感器检测气体浓度，根据气象条件及气体扩散前向分布模型来进行泄漏估计。由于泄漏场景数据不易获得，因此本文基于fires dynamics simulator(FDS)的大涡模拟方法模拟含湍流下的泄漏扩散场景获取数据，在此基础上研究了目标函数对于建模过程及溯源结果的影响。通过溯源结果发现，以浓度偏差平方和为目标函数的溯源结果从总体效果来看比其他目标函数更优，但其仍不能避免受湍流影响在某些场景的溯源指标远离平均值，而综合考虑不同目标的溯源结果在一定程度上能改善该情况。

关键词: 泄漏, 扩散, 模型, 湍流, 溯源, 目标函数

CLC Number:

N 945.12

Jikai DONG, Wenli DU, Bing WANG, Qiaoyi XU. Investigating impacts of cost functions to atmospheric dispersion modeling and source term estimation in turbulent condition[J]. CIESC Journal, 2020, 71(3): 1163-1173.

董吉开, 杜文莉, 王冰, 许乔伊. 湍流状态下化学品扩散溯源中不同目标函数的影响分析[J]. 化工学报, 2020, 71(3): 1163-1173.

Figures/Tables 12

Fig.1 Sketch of release scenario

Fig.2 FDS simulation of release scenario

Table 1 Parameters of Gaussian dispersion coefficients

Model	a	b	c	d
Model 1	0.28065	0.72681	0.38029	0.59995
Model 2	0.30835	0.70756	0.34819	0.61652
Model 3	0.42271	0.65488	0.40053	0.56755

Table 2 Model index of test scenarios

Model	Scenario	$R 2$	$F A C 2$	$F B$	$N M S E$
Model 1	1	0.8844	0.8956	0.1301	0.2607
	2	0.9441	0.9095	-0.1074	0.1069
	3	0.7638	0.8080	-0.1030	0.2810
Model 2	1	0.8914	0.8903	0.1180	0.2418
	2	0.9415	0.9111	-0.1200	0.1104
	3	0.7558	0.8042	-0.1157	0.2868
Model 3	1	0.8954	0.8389	0.0542	0.2185
	2	0.9277	0.9173	-0.1881	0.1274
	3	0.7645	0.8232	-0.1794	0.2594

Table 2 Model index of test scenarios

Model	Scenario	$R 2$	$F A C 2$	$F B$	$N M S E$
Model 1	1	0.8844	0.8956	0.1301	0.2607
	2	0.9441	0.9095	-0.1074	0.1069
	3	0.7638	0.8080	-0.1030	0.2810
Model 2	1	0.8914	0.8903	0.1180	0.2418
	2	0.9415	0.9111	-0.1200	0.1104
	3	0.7558	0.8042	-0.1157	0.2868
Model 3	1	0.8954	0.8389	0.0542	0.2185
	2	0.9277	0.9173	-0.1881	0.1274
	3	0.7645	0.8232	-0.1794	0.2594

Fig.3 Box-plot of modeling indexes of all scenarios with different model

Fig.4 Scatter plots of predicted concentrations compared to observed ones of test scenario

Fig.5 Plane concentrations of FDS simulated compared to model calculated

Fig.6 Mean TD and ARDQ of all scenarios with different models and cost functions

Table 3 Mean and standard deviation of result of source term estimation for all scenarios

模型	优化目标	x轴偏差/m		y轴偏差/m		z轴偏差/m		总距离差/m		源强相对偏差/%
模型	优化目标	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差
模型1	1	8.35	6.36	0.96	0.66	0.53	0.61	8.61	6.17	9.22	6.73
	2	5.11	4.12	0.81	0.49	0.42	0.57	5.32	4.02	8.83	6.92
	3	4.98	3.86	0.75	0.46	0.32	0.49	5.15	3.79	10.68	8.47
	4	5.45	4.65	0.81	0.49	0.35	0.54	5.63	4.58	9.61	7.37
	5	5.45	4.65	0.81	0.49	0.35	0.54	5.63	4.58	10.46	8.38
	6	5.45	4.65	0.81	0.49	0.35	0.54	5.63	4.58	10.75	8.34
	7	13.28	7.82	1.25	0.99	1.45	1.12	13.55	7.73	10.42	8.75
模型2	1	8.13	6.38	0.96	0.66	0.56	0.64	8.42	6.16	8.99	6.22
	2	5.14	4.11	0.81	0.49	0.50	0.61	5.36	4.02	9.46	7.35
	3	5.20	3.88	0.75	0.46	0.39	0.55	5.37	3.81	11.62	8.71
	4	5.54	4.57	0.81	0.49	0.40	0.58	5.74	4.48	10.06	7.74
	5	5.54	4.57	0.81	0.49	0.40	0.57	5.73	4.48	11.47	8.76
	6	5.54	4.57	0.81	0.49	0.40	0.58	5.73	4.48	11.48	8.73
	7	13.21	7.95	1.26	0.99	1.65	1.12	13.53	7.82	11.28	9.54
模型3	1	6.66	5.34	0.96	0.66	0.63	0.70	6.95	5.19	9.61	7.52
	2	7.47	4.32	0.80	0.49	0.63	0.63	7.64	4.23	14.51	8.88
	3	8.19	4.05	0.75	0.46	0.55	0.63	8.29	4.02	15.68	10.55
	4	8.39	4.29	0.81	0.48	0.55	0.62	8.51	4.23	14.05	9.13
	5	8.38	4.29	0.81	0.49	0.54	0.62	8.50	4.23	16.49	10.20
	6	8.39	4.29	0.81	0.48	0.55	0.62	8.52	4.23	15.87	10.44
	7	10.08	7.67	1.27	0.97	1.69	0.98	10.58	7.41	9.26	8.15

Fig.7 Box-plot of results of source term estimation of all scenarios

Fig.8 Distribution of real source location and estimated ones in three scenarios

Fig.9 Aggregative indicator of TD

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