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Predictive Model for Concentration Distribution of Explosive Dispersal
[Image: see text] At present, concentration of explosive dispersal is very difficult and uncertain to measure. Numerical experimentation can avoid this deficiency. Data of particles during dispersal are readily available, including velocity, displacement, and mass. However, there is minimal research...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841944/ https://www.ncbi.nlm.nih.gov/pubmed/33521448 http://dx.doi.org/10.1021/acsomega.0c05128 |
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author | Chen, Xing Wang, Zhongqi Yang, En Li, Jianping |
author_facet | Chen, Xing Wang, Zhongqi Yang, En Li, Jianping |
author_sort | Chen, Xing |
collection | PubMed |
description | [Image: see text] At present, concentration of explosive dispersal is very difficult and uncertain to measure. Numerical experimentation can avoid this deficiency. Data of particles during dispersal are readily available, including velocity, displacement, and mass. However, there is minimal research on the concentration of explosive dispersal. Existing models used for the calculation of particle concentration neglect measuring the initial condition of particles and cannot, therefore, accurately describe the whole particle dispersion process. Moreover, existing concentration models do not take into account the continuous decrease in the size of particles caused by stripping and evaporation effects during flight, resulting in inaccurate descriptions of the concentration distribution. Consequently, this work derives a model to predict the concentration distribution of liquid and granular material dispersal, considering the two questions above. Concentration can be calculated based on the condensed-phase distribution and gas-phase distribution of the fuel cloud at different times by the model. This model was validated using experimental data on the mean concentration of dispersal and was well fitted. Therefore, it can be used as a tool to predict the dispersal of liquid and granular material, an explosion suppressant in coal mine accidents, and an aerosol fire extinguishant in remote forest fire extinguishers. Moreover, being able to predict the concentration of large-scale dispersal can significantly improve the accuracy and efficiency of secondary detonation. |
format | Online Article Text |
id | pubmed-7841944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-78419442021-01-29 Predictive Model for Concentration Distribution of Explosive Dispersal Chen, Xing Wang, Zhongqi Yang, En Li, Jianping ACS Omega [Image: see text] At present, concentration of explosive dispersal is very difficult and uncertain to measure. Numerical experimentation can avoid this deficiency. Data of particles during dispersal are readily available, including velocity, displacement, and mass. However, there is minimal research on the concentration of explosive dispersal. Existing models used for the calculation of particle concentration neglect measuring the initial condition of particles and cannot, therefore, accurately describe the whole particle dispersion process. Moreover, existing concentration models do not take into account the continuous decrease in the size of particles caused by stripping and evaporation effects during flight, resulting in inaccurate descriptions of the concentration distribution. Consequently, this work derives a model to predict the concentration distribution of liquid and granular material dispersal, considering the two questions above. Concentration can be calculated based on the condensed-phase distribution and gas-phase distribution of the fuel cloud at different times by the model. This model was validated using experimental data on the mean concentration of dispersal and was well fitted. Therefore, it can be used as a tool to predict the dispersal of liquid and granular material, an explosion suppressant in coal mine accidents, and an aerosol fire extinguishant in remote forest fire extinguishers. Moreover, being able to predict the concentration of large-scale dispersal can significantly improve the accuracy and efficiency of secondary detonation. American Chemical Society 2021-01-14 /pmc/articles/PMC7841944/ /pubmed/33521448 http://dx.doi.org/10.1021/acsomega.0c05128 Text en © 2021 The Authors. Published by American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. |
spellingShingle | Chen, Xing Wang, Zhongqi Yang, En Li, Jianping Predictive Model for Concentration Distribution of Explosive Dispersal |
title | Predictive Model for Concentration Distribution of
Explosive Dispersal |
title_full | Predictive Model for Concentration Distribution of
Explosive Dispersal |
title_fullStr | Predictive Model for Concentration Distribution of
Explosive Dispersal |
title_full_unstemmed | Predictive Model for Concentration Distribution of
Explosive Dispersal |
title_short | Predictive Model for Concentration Distribution of
Explosive Dispersal |
title_sort | predictive model for concentration distribution of
explosive dispersal |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841944/ https://www.ncbi.nlm.nih.gov/pubmed/33521448 http://dx.doi.org/10.1021/acsomega.0c05128 |
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