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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...

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Detalles Bibliográficos
Autores principales: Chen, Xing, Wang, Zhongqi, Yang, En, Li, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
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
Descripción
Sumario:[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.