火灾环境下液化石油气球罐的失效时间预测模型

doi:10.16085/j.issn.1000-6613.2024-2039

摘要/Abstract

摘要：

罐区多米诺事故危害性极大，火灾热辐射是引发多米诺事故的主要因素之一，而储罐受火后的失效现象是一种十分依赖时间的过程，因此准确预测储罐的失效时间对火灾多米诺效应的定量分析具有重要意义。鉴于目前对球形储罐的研究存在一定局限性，本文采用Ansys Workbench建立热流固耦合模型，对火灾环境下液化石油气（LPG）球罐的热响应行为进行数值模拟，建立热辐射强度与充装率两种因素影响下的失效时间预测模型。结果表明：球罐受火后的失效行为是内部压力急剧增大和高温情况下材料强度性能退化两方面共同作用的结果；火灾多米诺事故场景下球罐的支柱最先倒塌失效，球壳次之，且球壳应力最大点位于与支柱连接处；充装率一定时，球罐接收的热辐射强度越大，失效时间越短；热辐射强度一定时，充装率越大，失效时间越短。

关键词: 多米诺效应, 失效时间, 热响应, 球形储罐, 热流固耦合

Abstract:

Tank farm Domino accidents pose significant hazards, with fire-induced thermal radiation being one of the primary triggers. The failure phenomenon of storage tanks after fire is a highly time-dependent process, so accurately predicting the failure time of storage tanks is of great significance for the quantitative analysis of fire Domino effects. Given the current limitations in research on spherical storage tanks, this study employed Ansys Workbench to establish a thermal-fluid-solid coupling model for simulating the thermal response behavior of liquefied petroleum gas (LPG) spherical tanks under fire conditions. A failure time prediction model was developed, considering the effects of heat flux and filling degree. The results indicated that the failure behavior of spherical tanks under fire was the result of the combined effects of a rapid increase in internal pressure and the degradation of material strength under high temperatures. In Domino accident scenarios involving fire, the pillars failed first, followed by the tank shell, with the maximum stress point located at the connection between the shell and the pillars. At a constant filling degree, higher heat flux led to shorter failure times, while at a constant heat flux, higher filling degree also resulted in shorter failure times.

Key words: Domino effect, failure time, thermal response, spherical tank, thermal-fluid-solid coupling

中图分类号:

X937

王曦林, 毕明树, 任婧杰. 火灾环境下液化石油气球罐的失效时间预测模型[J]. 化工进展, 2025, 44(12): 7338-7348.

WANG Xilin, BI Mingshu, REN Jingjie. Failure time prediction model of LPG spherical tank under fire environment[J]. Chemical Industry and Engineering Progress, 2025, 44(12): 7338-7348.

图/表 17

参考文献 28

[1]	KHAN Faisal I, ABBASI S A. Models for Domino effect analysis in chemical process industries[J]. Process Safety Progress, 1998, 17(2): 107-123.
[2]	COZZANI Valerio, SALZANO Ernesto. The quantitative assessment of Domino effects caused by overpressure Part Ⅰ. Probit models[J]. Journal of Hazardous Materials, 2004, 107(3): 67-80.
[3]	COZZANI Valerio, SALZANO Ernesto. The quantitative assessment of Domino effect caused by overpressure. Part Ⅱ. Case studies[J]. Journal of Hazardous Materials, 2004, 107(3): 81-94.
[4]	COZZANI Valerio, GUBINELLI Gianfilippo, ANTONIONI Giacomo, et al. The assessment of risk caused by Domino effect in quantitative area risk analysis[J]. Journal of Hazardous Materials, 2005, 127(1/2/3): 14-30.
[5]	ZHANG Mingguang, JIANG Juncheng. An improved probit method for assessment of Domino effect to chemical process equipment caused by overpressure[J]. Journal of Hazardous Materials, 2008, 158(2/3): 280-286.
[6]	SUN Dongliang, HUANG Guangtuan, JIANG Juncheng, et al. Study on the rationality and validity of probit models of Domino effect to chemical process equipment caused by overpressure[J]. Journal of Physics: Conference Series, 2013, 423: 012002.
[7]	MUKHIM Euginia Diana, ABBASI Tasneem, TAUSEEF S M, et al. Domino effect in chemical process industries triggered by overpressure — Formulation of equipment-specific probits[J]. Process Safety and Environmental Protection, 2017, 106: 263-273.
[8]	ABDOLHAMIDZADEH Bahman, ABBASI Tasneem, RASHTCHIAN D, et al. A new method for assessing Domino effect in chemical process industry[J]. Journal of Hazardous Materials, 2010, 182(1/2/3): 416-426.
[9]	KHAKZAD Nima, KHAN Faisal, AMYOTTE Paul, et al. Domino effect analysis using Bayesian networks[J]. Risk Analysis, 2013, 33(2): 292-306.
[10]	KHAKZAD Nima. Application of dynamic Bayesian network to risk analysis of Domino effects in chemical infrastructures[J]. Reliability Engineering & System Safety, 2015, 138: 263-272.
[11]	KHAKZAD Nima, LANDUCCI Gabriele, COZZANI Valerio, et al. Cost-effective fire protection of chemical plants against Domino effects[J]. Reliability Engineering & System Safety, 2018, 169: 412-421.
[12]	ZHOU Jianfeng, RENIERS Genserik. Dynamic analysis of fire induced Domino effects to optimize emergency response policies in the chemical and process industry[J]. Journal of Loss Prevention in the Process Industries, 2022, 79: 104835.
[13]	LANDUCCI Gabriele, GUBINELLI Gianfilippo, ANTONIONI Giacomo, et al. The assessment of the damage probability of storage tanks in Domino events triggered by fire[J]. Accident Analysis & Prevention, 2009, 41(6): 1206-1215.
[14]	WU Zhuang, HOU Lei, WU Shouzhi, et al. The time-to-failure assessment of large crude oil storage tank exposed to pool fire[J]. Fire Safety Journal, 2020, 117: 103192.
[15]	YANG Jianfeng, ZHANG Bo, CHEN Liangchao, et al. Improved solid radiation model for thermal response in large crude oil tanks[J]. Energy, 2023, 284: 128572.
[16]	YANG Jiahao, ZHANG Mingguang, ZUO Yawen, et al. Improved models of failure time for atmospheric tanks under the coupling effect of multiple pool fires[J]. Journal of Loss Prevention in the Process Industries, 2023, 81: 104957.
[17]	AMIN Md Tanjin, SCARPONI Giordano Emrys, COZZANI Valerio, et al. Dynamic Domino effect assessment (D2EA) in tank farms using a machine learning-based approach[J]. Computers & Chemical Engineering, 2024, 181: 108556.
[18]	LI Qinggong, SONG Wenhua, ZHANG Miao, et al. Numerical simulation of liquefied propane gas storage tanks full-size pool fire based on FDS[J]. Applied Mechanics and Materials, 2013, 353/354/355/356: 2419-2423.
[19]	COZZANI Valerio, GUBINELLI Gianfilippo, SALZANO Ernesto. Escalation thresholds in the assessment of Domino accidental events[J]. Journal of Hazardous Materials, 2006, 129(1/2/3): 1-21.
[20]	BERBEROVIC E. Investigation of free-surface flow associated with drop impact: Numerical simulations and theoretical modeling[D]. Darmstadt: Technical University of Darmstadt, 2010.
[21]	FAVERO J L, SECCHI A R, CARDOZO N S M, et al. Viscoelastic fluid analysis in internal and in free surface flows using the software OpenFOAM[J]. Computers & Chemical Engineering, 2010, 34(12): 1984-1993.
[22]	MEHDIZADEH A, SHERIF S A, LEAR W E. Numerical simulation of thermofluid characteristics of two-phase slug flow in microchannels[J]. International Journal of Heat and Mass Transfer, 2011, 54(15/16): 3457-3465.
[23]	ZADEH Shobeir Aliasghar, RADESPIEL Rolf. Numerical study on bubble formation in a microchannel flow-focusing device using the VOF method[C]// ASME 2011 9th International Conference on Nanochannels, Microchannels, and Minichannels, 2011.
[24]	RAEINI Ali Q, BLUNT Martin J, BIJELJIC Branko. Modelling two-phase flow in porous media at the pore scale using the volume-of-fluid method[J]. Journal of Computational Physics, 2012, 231(17): 5653-5668.
[25]	ROOHI Ehsan, ZAHIRI Amir Pouyan, Mahmood PASSANDIDEH-FARD. Numerical simulation of cavitation around a two-dimensional hydrofoil using VOF method and LES turbulence model[J]. Applied Mathematical Modelling, 2013, 37(9): 6469-6488.
[26]	KERPICCI Husnu, YAGCI Alper, ONBASIOGLU Seyhan U. Investigation of oil flow in a hermetic reciprocating compressor[J]. International Journal of Refrigeration, 2013, 36(1): 215-221.
[27]	KLOSTERMANN J, SCHAAKE K, SCHWARZE R. Numerical simulation of a single rising bubble by VOF with surface compression[J]. International Journal for Numerical Methods in Fluids, 2013, 71(8): 960-982.
[28]	TSUI Yeng-Yung, LIN Shiwen. A VOF-based conservative interpolation scheme for interface tracking (CISIT) of two-fluid flows[J]. Numerical Heat Transfer B: Fundamentals, 2013, 63(4): 263-283.

场景编号	火灾区域			火源中心与目标储罐球心的间距/m	目标储罐接收的最大热辐射强度/kW·m^-2
场景编号	X	Y	Z	火源中心与目标储罐球心的间距/m	目标储罐接收的最大热辐射强度/kW·m^-2
1	(-19.7, 19.7)	(12, 34)	(13, -11.8)	11	40
2	(-19.7, 19.7)	(12, 32)	(13, -11.8)	10	51
3	(-19.7, 19.7)	(12, 28)	(13, -11.8)	8	58
4	(-19.7, 19.7)	(12, 26.5)	(13, -11.8)	7.25	65
5	(-19.7, 19.7)	(12, 25)	(13, -11.8)	6.5	71
6	(-19.7, 19.7)	(12, 24)	(13, -11.8)	6	74
7	(-19.7, 19.7)	(12, 23)	(13, -11.8)	5.5	80
8	(-19.7, 19.7)	(12, 22)	(13, -11.8)	5	85
9	(-19.7, 19.7)	(12, 20)	(13, -11.8)	4	91
10	(-19.7, 19.7)	(12, 19)	(13, -11.8)	3.5	99

场景编号	火灾区域			火源中心与目标储罐球心的间距/m	目标储罐接收的最大热辐射强度/kW·m^-2
场景编号	X	Y	Z	火源中心与目标储罐球心的间距/m	目标储罐接收的最大热辐射强度/kW·m^-2
1	(-19.7, 19.7)	(12, 34)	(13, -11.8)	11	40
2	(-19.7, 19.7)	(12, 32)	(13, -11.8)	10	51
3	(-19.7, 19.7)	(12, 28)	(13, -11.8)	8	58
4	(-19.7, 19.7)	(12, 26.5)	(13, -11.8)	7.25	65
5	(-19.7, 19.7)	(12, 25)	(13, -11.8)	6.5	71
6	(-19.7, 19.7)	(12, 24)	(13, -11.8)	6	74
7	(-19.7, 19.7)	(12, 23)	(13, -11.8)	5.5	80
8	(-19.7, 19.7)	(12, 22)	(13, -11.8)	5	85
9	(-19.7, 19.7)	(12, 20)	(13, -11.8)	4	91
10	(-19.7, 19.7)	(12, 19)	(13, -11.8)	3.5	99

设计参数	数值
结构型式	五带混合式
球壳与支柱的连接	赤道正切式，加U形托板结构型式
直径/m	19.7
球壳厚度/mm	46
支柱数量及规格	14个ϕ600mm×16mm
支柱底板地面至球壳赤道平面的距离/mm	11800
托板直径/mm	400
托板厚度/mm	15
底板直径/mm	1000
底板厚度/mm	50

设计参数	数值
结构型式	五带混合式
球壳与支柱的连接	赤道正切式，加U形托板结构型式
直径/m	19.7
球壳厚度/mm	46
支柱数量及规格	14个ϕ600mm×16mm
支柱底板地面至球壳赤道平面的距离/mm	11800
托板直径/mm	400
托板厚度/mm	15
底板直径/mm	1000
底板厚度/mm	50

工况编号	接收最大热辐射强度/kW·m^-2	充装率/%	球壳失效时间/s	支柱失效时间/s
1	40	20	512	291
2	40	40	510	290
3	40	60	509	289
4	40	85	501	287
5	51	20	430	283
6	51	40	422	282
7	51	60	421	281
8	51	85	413	280
9	58	20	302	198
10	58	40	301	196
11	58	60	300	194
12	58	85	299	191
13	65	20	268	179
14	65	40	260	178
15	65	60	259	177
16	65	85	258	174
17	71	20	252	172
18	71	40	250	171
19	71	60	249	170
20	71	85	248	169
21	74	20	249	170
22	74	40	248	168
23	74	60	240	167
24	74	85	239	166
25	80	20	248	163
26	80	40	240	162
27	80	60	238	161
28	80	85	237	160
29	85	20	234	161
30	85	40	233	160
31	85	60	232	159
32	85	85	231	158
33	91	20	213	151
34	91	40	212	150
35	91	60	209	149
36	91	85	208	148
37	99	20	209	147
38	99	40	205	146
39	99	60	201	145
40	99	85	200	144