Literature DB >> 23669789

Experimental and numerical study of the dispersion of carbon dioxide plume.

Ji Xing1, Zhenyi Liu, Ping Huang, Changgen Feng, Yi Zhou, Deping Zhang, Feng Wang.   

Abstract

Carbon Capture and Storage (CCS) and Enhanced Oil Recovery (EOR) technologies have been widely applied in the environmental protection and petroleum production fields. However, accidental release of carbon dioxide may cause damage and losses during oil and gas production. This paper presents a reduced-scale field experiment designed to imitate a CO2 blowout for the purpose of acquiring concentration the distribution in the flow field. Additionally, computational fluid dynamics (CFD) code was used to perform numerical simulations of the field experiment using the k-ε, RNG k-ε and SST k-ω models. The results of these models were compared with the experimental data for validation, and statistical performance indicators were introduced to verify the simulated values. According to experimental and numerical results, the interior flow structure of a CO2 plume was analyzed together with consideration of negative buoyancy effects. The concentration as a function of time was studied by comparing the observed values and simulation results. We conclude that the CFD simulation results from the k-ε and SST k-ω models are in acceptable agreement with the experimental data according to the Chang's criteria, and predicted values from the RNG k-ε model are unsatisfactory. Therefore, the CFD techniques can be satisfactorily applied in industrial risk analysis procedures with acceptable accuracy according to the Chang's criteria.
Copyright © 2013 Elsevier B.V. All rights reserved.

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Year:  2013        PMID: 23669789     DOI: 10.1016/j.jhazmat.2013.03.066

Source DB:  PubMed          Journal:  J Hazard Mater        ISSN: 0304-3894            Impact factor:   10.588


  2 in total

1.  Optimisation of dispersion parameters of Gaussian plume model for CO₂ dispersion.

Authors:  Xiong Liu; Ajit Godbole; Cheng Lu; Guillaume Michal; Philip Venton
Journal:  Environ Sci Pollut Res Int       Date:  2015-09-15       Impact factor: 4.223

2.  Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases.

Authors:  Rongxiao Wang; Bin Chen; Sihang Qiu; Zhengqiu Zhu; Yiduo Wang; Yiping Wang; Xiaogang Qiu
Journal:  Int J Environ Res Public Health       Date:  2018-07-10       Impact factor: 3.390

  2 in total

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