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CFD Analysis of VX Gas Dispersion in an Indoor Environment | ||
Chemical Process Design | ||
دوره 4، شماره 1، شهریور 2025، صفحه 60-73 اصل مقاله (883.37 K) | ||
نوع مقاله: Research Article | ||
شناسه دیجیتال (DOI): 10.22111/cpd.2025.50928.1047 | ||
نویسندگان | ||
Hassan Tavakoli* ؛ Rasool Amirkhani | ||
Chemistry Group, Faculty of Basic Sciences, Imam Ali University, Iran | ||
چکیده | ||
VX, a highly persistent and lethal nerve agent, presents a severe threat in enclosed spaces during chemical attacks or accidental releases. However, its dispersion behavior within indoor environments remains poorly understood, limiting the effectiveness of mitigation and response strategies. This study introduces the first transient three-dimensional computational fluid dynamics (CFD) analysis of VX gas dispersion and removal in a confined space, defining novel safety metrics: danger time (time to reach a toxic 10 mg/m³) and safe time (time to decline below a safe 0.1 mg/m³). Employing the k-epsilon turbulence model in COMSOL Multiphysics, we investigated the effects of air inlet velocity (0.5–1.5 m/s), inflow VX concentration (100–300 mg/m³), and initial VX concentration (500–1000 mg/m³) in a validated room-scale model. Results reveal that elevating inlet velocity from 0.5 to 1.5 m/s reduces safe time by 67.1% (40.8 to 13.4 min), enhancing decontamination, while increasing inflow concentration from 100 to 300 mg/m³ accelerates danger time by 70% (0.2 to 0.06 min), amplifying risk onset. Higher initial concentrations extend safe time by 9.3%, reflecting slower dilution. VX accumulates near walls, driven by mixing effects, identifying critical hazard zones. Moreover, the results show that incorporating obstacles into the indoor environment increases both the danger and the required safety times. | ||
کلیدواژهها | ||
Nerves Gas Dispersion؛ Indoor Environment؛ Computational Fluid Dynamics | ||
مراجع | ||
[1] Mukhopadhyay, S., Schoenitz, M., Dreizin, E.L., 2021, Vapor-phase decomposition of dimethyl methylphosphonate (DMMP), a sarin surrogate, in presence of metal oxides, Defence Technology, 17, 1095-1114. https://doi.org/10.1016/j.dt.2020.08.010
[2] Lee, E.C., 2003, Clinical manifestations of sarin nerve gas exposure, Jama, 290, 659-662. https://doi.org/10.1001/jama.290.5.659
[3] Kye, Y.-S., Chung, W.-Y., Kim, Y.-J., 2012, A study on the decomposition of DFP using Cu (II)-chitosan complex, Journal of the Korea Institute of Military Science and Technology, 15, 699-704. https://doi.org/10.9766/kimst.2012.15.5.699
[4] Aldahhak, H., Powroźnik, P., Pander, P., Jakubik, W., Dias, F.B., Schmidt, W.G., Gerstmann, U., Krzywiecki, M., 2020, Toward efficient toxic-gas detectors: Exploring molecular interactions of sarin and dimethyl methylphosphonate with metal-centered phthalocyanine structures, The Journal of Physical Chemistry C, 124, 6090-6102. https://doi.org/10.1021/acs.jpcc.9b11116
[5] Ma, L., Chen, B., Qiu, S., Li, Z., Qiu, X., 2017, Agent-based modeling of emergency evacuation in a railway station square under sarin terrorist attack, International Journal of Modeling, Simulation, and Scientific Computing, 8, 1750022. https://doi.org/10.4103/atr.atr4019
[6] Davis, E.D., Gordon, W.O., Wilmsmeyer, A.R., Troya, D., Morris, J.R., 2014, Chemical warfare agent surface adsorption: hydrogen bonding of sarin and soman to amorphous silica, The Journal of Physical Chemistry Letters, 5, 1393–1399. https://doi.org/10.1021/jz500375h
[7] Balow, R.B., McEntee, M., Schweigert, I.V., Jeon, S., Peterson, G.W., Pehrsson, P., 2021, Battling chemical weapons with zirconium hydroxide nanoparticle sorbent: impact of environmental contaminants on sarin sequestration and decomposition, Langmuir, 37, 6923–6934. https://doi.org/10.1021/acs.langmuir.1c00380
[8] Singer, B.C., Hodgson, A.T., Destaillats, H., Hotchi, T., Revzan, K.L., Sextro, R.G., 2005, Indoor sorption of surrogates for sarin and related nerve agents, Environmental Science & Technology, 39, 3203–3214. https://doi.org/10.1021/es049144u
[9] Li, T., Leonard, M., Tsyshevsky, R., McEntee, M., Karwacki, C., Durke, E.M., Kuklja, M.M., Rodriguez, E.E., 2023, High reactivity of mesoporous CeO₂ to dissociate chemical warfare agent sarin, Materials Chemistry Frontiers, 7, 1855–1866. https://doi.org/10.1039/D2QM01253G
[10] Tsyshevsky, R., Head, A.R., Trotochaud, L., Bluhm, H., Kuklja, M.M., 2020, Mechanisms of degradation of toxic nerve agents: quantum-chemical insight into interactions of sarin and soman with molybdenum dioxide, Surface Science, 700, 121639. https://doi.org/10.1016/j.susc.2020.121639
[11] Chauhan, N., Chauhan, R., 2015, Active-passive measurements and CFD based modelling for indoor radon dispersion study, Journal of Environmental Radioactivity, 144, 57–61. https://doi.org/10.1016/j.jenvrad.2015.03.009
[12] Zhou, W., Iida, T., Moriizumi, J., Aoyagi, T., Takahashi, I., 2001, Simulation of the concentrations and distributions of indoor radon and thoron, Radiation Protection Dosimetry, 93, 357–367. https://doi.org/10.1093/oxfordjournals.rpd.a006448
[13] Hosseini, M., Madani, H., Shahriar, K., 2022, CFD-based modeling of sarin gas dispersion in a subway station – A hypothetical scenario, Journal of Mining and Environment, 13, 235–251. https://doi.org/10.22044/jme.2022.11604.2150
[14] Parkash, R., Chauhan, N., Chauhan, R., 2024, Application of CFD modeling for indoor radon and thoron dispersion study: A review, Journal of Environmental Radioactivity, 272, 107368. https://doi.org/10.1016/j.jenvrad.2023.107368
[15] Faugier, L., Marinus, B.G., Bosschaerts, W., Laboureur, D., Limam, K., 2023, CFD model for airflow in a subway station compared to on-site measurements: The challenges of as-built environment, Tunnelling and Underground Space Technology, 140, 105248. https://doi.org/10.1016/j.tust.2023.105248
[16] Lin, X., Fu, Y., Peng, D.Z., Liu, C.-H., Chu, M., Chen, Z., Yang, F., Tim, K., Li, C.Y., Feng, X., 2024, CFD- and BPNN-based investigation and prediction of air pollutant dispersion in urban environment, Sustainable Cities and Society, 100, 105029. https://doi.org/10.1016/j.scs.2023.105029
[17] Yang, L., Ye, M., 2014, CFD simulation research on residential indoor air quality, Science of the Total Environment, 472, 1137–1144. https://doi.org/10.1016/j.scitotenv.2013.11.118
[18] Widiatmojo, A., Sasaki, K., Widodo, N.P., Sugai, Y., Sahzabi, A.Y., Nguele, R., 2016, Predicting gas dispersion in large scale underground ventilation: A particle tracking approach, Building and Environment, 95, 171–181. https://doi.org/10.1016/j.buildenv.2015.07.025
[19] Dolezal, O., Tomaskova, H., 2020, An agent-based simulation to minimize losses during a terrorist attack, Applied Sciences, 10, 3213. https://doi.org/10.3390/app10093213
[20] Siddiqui, M., Jayanti, S., Swaminathan, T., 2012, CFD analysis of dense gas dispersion in indoor environment for risk assessment and risk mitigation, Journal of Hazardous Materials, 209, 177–185. https://doi.org/10.1016/j.jhazmat.2012.01.007
[21] Chunwen, X., Shuquan, Y., Jianfeng, T., Mengjie, S., 2024, Design and practice of indoor gas leak diffusion simulation experiment based on CFD, Experimental Technology and Management, 41, 119–126. https://doi.org/10.16791/j.cnki.sjg.2024.12.016
[22] Li, L., He, Y., Chen, W., Ji, Y., Fung, J.C., Lau, A.K., 2024, An integrated experimental and CFD analysis of ceiling-fan-integrated air conditioning system: Indoor air quality and air velocity, Building and Environment, 258, 111633. https://doi.org/10.1016/j.buildenv.2024.111633
[23] Wang, S., Zheng, X., Ng, S.T., Bao, Z., 2024, An innovative three-dimensional computational fluid dynamics-iterative ensemble Kalman filter model for the prediction of heavy gas leakage and dispersion in enclosed workplaces: Case study for hydrogen sulfide leakage, Physics of Fluids, 36. https://doi.org/10.1063/5.0216440
[24] Eckstein, A., Gilliard, J., Lee, A., Banko, A., Fobar, D., 2024, Contaminant dispersion: Modeling indoor and outdoor airflow through a ventilated building, ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, V008T010A046. https://doi.org/10.1115/imece2024-145306
[25] De Crescenzo, C., Sabbarese, S., Ciampa, R., Capece, G., Migliaccio, A., Karatza, D., Chianese, S., Musmarra, D., 2021, Simulation of methane mass transfer in a bubble column incipient turbulent regime using COMSOL Multiphysics®, Chemical Engineering Transactions, 86, 1183–1188. https://doi.org/10.1016/j.nbt.2022.04.004
[26] Ganguli, A., Kenig, E., 2011, A CFD-based approach to the interfacial mass transfer at free gas–liquid interfaces, Chemical Engineering Science, 66, 3301–3308. https://doi.org/10.1016/j.ces.2011.01.055
[27] Ma, R., Castro-Dominguez, B., Dixon, A.G., Ma, Y.H., 2018, CFD study of heat and mass transfer in ethanol steam reforming in a catalytic membrane reactor, International Journal of Hydrogen Energy, 43, 7662–7674. https://doi.org/10.1016/j.ijhydene.2017.08.173
[28] Agarwal, T.K., Sahoo, B., Joshi, M., Mishra, R., Meisenberg, O., Tschiersch, J., Sapra, B., 2019, CFD simulations to study the effect of ventilation rate on 220Rn concentration distribution in a test house, Radiation Physics and Chemistry, 162, 82–89. https://doi.org/10.1016/j.radphyschem.2019.04.018 | ||
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