Numerical simulations for thermal radiation and personnel risk associated with large-scale oil pool fires under unsteady environmental wind conditions

Yichen ZHANG, Hanchao MA, Zhenqi HU, Hong HUANG, Jinlong ZHAO

Journal of Tsinghua University(Science and Technology) ›› 2026, Vol. 66 ›› Issue (9) : 1829-1843.

PDF(4506 KB)
PDF(4506 KB)
Journal of Tsinghua University(Science and Technology) ›› 2026, Vol. 66 ›› Issue (9) : 1829-1843. DOI: 10.16511/j.cnki.qhdxxb.2026.27.045
Public Safety

Numerical simulations for thermal radiation and personnel risk associated with large-scale oil pool fires under unsteady environmental wind conditions

Author information +
History +

Abstract

Objective: Large-scale oil pool fires release intense thermal radiation, posing significant threats to personnel, equipment, and adjacent facilities. In actual accidents, such fires are often influenced by unsteady environmental wind conditions rather than ideal calm or steady wind. Wind fluctuations alter flame inclination, smoke diffusion, air entrainment, and the spatial distribution of thermal radiation intensity, thereby increasing uncertainty in personnel risk assessment. Existing research has primarily focused on no-wind or steady-wind conditions, leaving the coupled effects of wind speed and turbulence intensity insufficiently understood. This study investigates the thermal radiation and personnel risk associated with large-scale oil pool fires under unsteady environmental wind conditions based on numerical simulations. Methods: A square-shaped diesel oil pool fire model of 50 m2 was developed using Fire Dynamics Simulator. Unsteady environmental wind fields were generated by combining the Kaimal spectrum function with the harmonic superposition method, and the resulting wind velocity time histories were incorporated into the numerical simulations. Three wind speeds (5 m/s, 10 m/s, and 15 m/s) and five turbulence intensities (0%, 5%, 10%, 20%, and 30%) were utilized to generate 15 distinct cases. The 0% turbulence intensity case served as the steady-wind reference. Heat flux gauges were positioned at z=1.5 m and on the vertical section to capture the spatial distribution and temporal variation of thermal radiation intensity. The model was validated against full-scale controlled wind tunnel oil pool fire data for wind speeds of 5 m/s and 10 m/s. Representative gauge values at similar distances from the oil pool center were compared with experimental measurements to assess the reliability of the numerical simulations. Following validation, the effects of wind speed, turbulence intensity, and measuring distance on thermal radiation intensity were analyzed. Cumulative probability was introduced to quantify the stochastic fluctuations of thermal radiation intensity, and a logistic model was used to fit the relationship between thermal radiation intensity and cumulative probability. Finally, the thermal radiation intensity threshold model was combined with the simulated distribution to evaluate personnel safety distances under various wind conditions. Results: The numerical simulations yielded the following results: 1) Compared with full-scale controlled wind tunnel experimental data, the relative errors in the far-field region, critical for personnel risk assessment, ranged from 0.43% to 32.49%, with an average of 16.43%. Notably, 16 of 20 cases had errors within 25%. 2) The validated model showed that unsteady environmental wind increased the complexity of thermal radiation intensity variation. At low turbulence intensity, fluctuations were primarily controlled by flame entrainment and flame turbulence, whereas at high turbulence intensity, ambient wind turbulence was the dominant factor. 3) The relationship between thermal radiation intensity and cumulative probability followed a logistic model. For a given thermal radiation intensity, the cumulative probability decreased with increasing turbulence intensity. 4) Personnel safety distance was jointly influenced by wind speed and turbulence intensity. The downstream safety distance increased with wind speed and decreased with turbulence intensity, whereas the lateral safety distance exhibited the opposite trend. 5) The peak thermal radiation intensity generally occurred within 30 s after ignition, indicating that the early stages of fire development represent the critical period for personnel exposure. Conclusions: By incorporating unsteady environmental wind into the numerical simulation of large-scale oil pool fires, this study clarified the coupled effects of wind speed and turbulence intensity on thermal radiation intensity, cumulative probability, and personnel safety distance. The logistic model proved effective in characterizing the probabilistic distribution of thermal radiation intensity. The results show that assessments based solely on steady-wind assumptions may underestimate lateral and upwind risks, thus providing a more realistic foundation for hazard-zone determination and emergency evacuation planning.

Key words

large-scale oil pool fire / unsteady environmental wind / thermal radiation intensity / Logistic model / personnel safety distance

Cite this article

Download Citations
Yichen ZHANG , Hanchao MA , Zhenqi HU , et al . Numerical simulations for thermal radiation and personnel risk associated with large-scale oil pool fires under unsteady environmental wind conditions[J]. Journal of Tsinghua University(Science and Technology). 2026, 66(9): 1829-1843 https://doi.org/10.16511/j.cnki.qhdxxb.2026.27.045

References

1
WACHTMEISTER H, HENKE P, HÖÖK M. Oil projections in retrospect: Revisions, accuracy and current uncertainty[J]. Applied Energy, 2018, 220, 138- 153.
2
邓文扬. 环境风作用下大尺度油池火燃烧特性研究[D]. 合肥: 中国科学技术大学, 2022.
DENG W Y. Research on the combustion characteristics of large-scale pool fires under cross wind[D]. Hefei: University of Science and Technology of China, 2022. (in Chinese)
3
HU L H. A review of physics and correlations of pool fire behaviour in wind and future challenges[J]. Fire Safety Journal, 2017, 91, 41- 55.
4
LAM C S, WECKMAN E J. Wind-blown pool fire, Part Ⅰ: Experimental characterization of the thermal field[J]. Fire Safety Journal, 2015, 75, 1- 13.
5
DOMINO S P. On the subject of large-scale pool fires and turbulent boundary layer interactions[J]. Physics of Fluids, 2024, 36(2): 025163.
6
赵金龙, 唐卿, 黄弘, 等. 基于数值模拟的大型外浮顶储罐区定量风险评估[J]. 清华大学学报(自然科学版), 2015, 55(10): 1143- 1149.
ZHAO J L, TANG Q, HUANG H, et al. Quantitative risk assessment of external floating roof tank areas based on the numerical simulations[J]. Journal of Tsinghua University (Science and Technology), 2015, 55(10): 1143- 1149.
7
天津市应急管理局. 滨海新区中塘镇中外运久凌储运仓库"10·28"重大火灾事故调查报告[EB/OL]. (2019-05-22)[2025-09-28]. https://yjgl.tj.gov.cn/ZWGK6939/SGDCBG354/202007/t20200729_3184829.html.
Bureau of Emergency Management of Tianjin. Investigation report on the "10·28" major fire accident at the Sinotrans Jiuling storage and handling warehouse in Zhongtang Town, Binhai New Area[EB/OL]. (2019-05-22)[2025-09-28]. https://yjgl.tj.gov.cn/ZWGK6939/SGDCBG354/202007/t20200729_3184829.html. (in Chinese)
8
LIU C X, YIN Z Y, JANGI M, et al. Experimental study on radiative heat flux from annular pool fires under the cross airflow[J]. Applied Thermal Engineering, 2025, 260, 124947.
9
SHARMA A, MISHRA K B. Thermal hazard and fire risk evaluation of transformer oil-heat transfer dynamics and safety implications[J]. Thermal Science and Engineering Progress, 2025, 65, 103814.
10
苗磊. 大风环境中航空煤油池火数值模拟[D]. 哈尔滨: 哈尔滨工程大学, 2015.
MIAO L. Numerical simulation of aviation fuel pool fire under high speed wind[D]. Harbin: Harbin Engineering University, 2015. (in Chinese)
11
张伟鹏. 基于全表面火灾的大型成品油储罐安全间距研究[D]. 北京: 中国石油大学, 2023.
ZHANG W P. Research on safe spacing of large oil product storage tanks based on full surface fire[D]. Beijing: China University of Petroleum, 2023. (in Chinese)
12
胡亮. 基于谱表示法的结构随机风速风压场模拟[D]. 上海: 同济大学, 2010.
HU L. Simulation of stochastic wind velocity and wind pressure fields on structures based on spectral representation method[D]. Shanghai: TongJi University, 2010. (in Chinese)
13
RONCALLO L, SOLARI G. An evolutionary power spectral density model of thunderstorm outflows consistent with real-scale time-history records[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 203, 104204.
14
郜志腾. 中性风场中风速脉动对水平轴风力机气动特性的影响[D]. 兰州: 兰州理工大学, 2017.
GAO Z T. Effects of wind velocity fluctuation in neutral atmospheric wind farm on the aerodynamic characteristics of the horizontal axis wind turbine[D]. Lanzhou: Lanzhou University of Technology, 2017. (in Chinese)
15
SIMIU E, SCANLAN R H. Wind effects on structures: fundamentals and applications to design[M]. New York: John Wiley and Sons, 1996.
16
DAVENPORT A G. The dependence of wind load upon meteorological parameters[C]//International Research Seminar on Wind Effects on Buildings and Structures. Ottawa, Canada: University of Toronto Press, 1968: 19–30.
17
SCHAUMANN P, WILKE F. Current developments of support structures for wind turbines in offshore environment[C]//Fourth International Conference on Advances in Steel Structures. Shanghai, China: Elsevier Science Ltd, 2005: 1107–1114.
18
刘锡良, 周颖. 风荷载的几种模拟方法[J]. 工业建筑, 2005, 35(5): 81- 84.
LIU X L, ZHOU Y. Numerical simulation methods of wind load[J]. Industrial Construction, 2005, 35(5): 81- 84.
19
陈小波, 陈健云, 李静. 海上风力发电塔脉动风速时程数值模拟[J]. 中国电机工程学报, 2008, 28(32): 111- 116.
CHEN X B, CHEN J Y, LI J. Numerical simulation of fluctuating wind velocity time series of offshore wind turbine[J]. Proceedings of the CSEE, 2008, 28(32): 111- 116.
20
ATALLAH S, ALLAN D S. Safe separation distances from liquid fuel fires[J]. Fire Technology, 1971, 7(1): 47- 56.
21
张鑫宇, 陈敏, 范水勇. 基于莫宁-奥布霍夫相似理论的地面站点风速预报偏差订正[J]. 气象, 2023, 49(5): 624- 632.
ZHANG X Y, CHEN M, FAN S Y. Correction of wind speed forecast deviations at ground stations based on Monin-Obukhov similarity theory[J]. Meteorological Monthly, 2023, 49(5): 624- 632.
22
SHINOZUKA M. Simulation of multivariate and multidimensional random processes[J]. The Journal of the Acoustical Society of America, 1971, 49(1B): 357- 368.
23
LOU M W, JIA H Y, LIN Z, et al. Study on fire extinguishing performance of different foam extinguishing agents in diesel pool fire[J]. Results in Engineering, 2023, 17, 100874.
24
HEIDARINEJAD G, EFTEKHARI M, SAFARZADEH M, et al. Investigating radiation, toxic and hot gases fire hazards in large-scale storage tanks for oil derivatives with and without wind conditions[J]. International Journal of Thermal Sciences, 2025, 208, 109504.
25
陈国华, 张心语, 周志航, 等. 两池火耦合作用下柴油拱顶罐热响应的数值模拟[J]. 化工进展, 2020, 39(11): 4342- 4350.
CHEN G H, ZHANG X Y, ZHOU Z H, et al. Numerical simulation on thermal response of diesel dome tank under the impact of double-pool fire[J]. Chemical Industry and Engineering Progress, 2020, 39(11): 4342- 4350.
26
冯娇娇, 王静虹, 李佳, 等. 火灾事故下人群承受热辐射阈值差异分析[J]. 中国安全科学学报, 2020, 30(10): 134- 140.
FENG J J, WANG J H, LI J, et al. Difference in thermal radiation threshold of people under fire accidents[J]. China Safety Science Journal, 2020, 30(10): 134- 140.
27
LI X F, CHEN G H, HUANG K X, et al. Consequence modeling and domino effects analysis of synergistic effect for pool fires based on computational fluid dynamic[J]. Process Safety and Environmental Protection, 2021, 156, 340- 360.
28
李诗彤, 王亮, 钟珂. 基于FDS的2种纵向通风设置对公路隧道火灾疏散环境模拟结果的影响研究[J]. 中国安全生产科学技术, 2026, 22(2): 146- 153.
LI S T, WANG L, ZHONG K. Study on the impact of two longitudinal ventilation settings on fire evacuation simulations in highway tunnels based on FDS[J]. Journal of Safety Science and Technology, 2026, 22(2): 146- 153.
29
HOU S Y, LUAN X Y, WANG Z, et al. A quantitative risk assessment framework for domino accidents caused by double pool fires[J]. Journal of Loss Prevention in the Process Industries, 2022, 79, 104843.
30
ZHAO J L, ZHANG X, SONG G H, et al. Experiments and modeling of the temperature profile of turbulent diffusion flames with large ullage heights[J]. Fuel, 2023, 331, 125876.
31
ZHANG S H, ZHAO J L, HU Z Q, et al. Study on the evolution of flame morphology and burning rate of pool fires under the combined influence of crosswind and ullage height[J]. Energy, 2025, 322, 135722.
32
DENG L, SHI C L, XU J X, et al. Forecasting the flame geometry and ground received radiative heat fluxes of two parallel adjacent fires of hydrocarbon fuel with crosswinds[J]. International Journal of Thermal Sciences, 2025, 207, 109376.
33
CRAMER J S. The early origins of the logit model[J]. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 2004, 35(4): 613- 626.
34
CHEN J, WANG Z H, ZHANG Y N, et al. New insights into the ignition characteristics of liquid fuels on hot surfaces based on TG-FTIR[J]. Applied Energy, 2024, 360, 122827.
35
MENG M, NIU D X. Modeling CO2 emissions from fossil fuel combustion using the logistic equation[J]. Energy, 2011, 36(5): 3355- 3359.
36
RAJ P K. A review of the criteria for people exposure to radiant heat flux from fires[J]. Journal of Hazardous Materials, 2008, 159(1): 61- 71.
37
National Fire Protection Association (NFPA). Standard for the Production, Storage, and Handling of Liquefied Natural Gas (LNG): NFPA 59A-2023[S]. Quincy, MA: NFPA, 2022.

RIGHTS & PERMISSIONS

All rights reserved. Unauthorized reproduction is prohibited.
PDF(4506 KB)

Accesses

Citation

Detail

Sections
Recommended

/