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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Blast Quantification Using Hopkinson Pressure Bars
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Predictive Model for Concentration Distribution of Explosive Dispersal.

Xing Chen1,2, Zhongqi Wang1, En Yang1

  • 1State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Haidian District, Beijing 100081, China.

ACS Omega
|February 1, 2021
PubMed
Summary

Predicting explosive dispersal concentration is challenging. This study introduces a new model that accurately calculates concentration by considering initial particle conditions and size changes during flight, improving safety applications.

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Area of Science:

  • Chemical Engineering
  • Physics
  • Safety Science

Background:

  • Measuring explosive dispersal concentration is difficult and existing models lack accuracy.
  • Current models fail to account for initial particle conditions and in-flight size reduction (stripping, evaporation).

Purpose of the Study:

  • To develop a novel model for predicting the concentration distribution of liquid and granular explosive dispersal.
  • To address limitations in existing models by incorporating initial particle states and dynamic size changes.

Main Methods:

  • Derivation of a new mathematical model for concentration prediction.
  • Inclusion of condensed-phase and gas-phase fuel cloud distributions over time.
  • Validation against experimental data for mean dispersal concentration.

Main Results:

  • The developed model accurately predicts concentration distribution.
  • Model validation showed good agreement with experimental data.
  • The model accounts for particle size reduction during dispersal.

Conclusions:

  • The new model provides a reliable tool for predicting liquid and granular material dispersal concentration.
  • Applications include explosion suppression in mines and aerosol fire extinction.
  • Accurate prediction of large-scale dispersal enhances secondary detonation safety.