No abstract

In order to invert the explosion equivalent and cloud top height of “Radiological Dispersal Device”(RDD) by cloud distribution, we firstly analyzed the temporal and spatial distribution of the cloud uplift of explosive experiments and revealed that the height of the explosion cloud increased with time according to a power function and this relationship was of certain stability. Based on the computational fluid dynamics (CFD) simulations and experimental proof, we further characterized cloud distribution of 12 TNT explosions, analyzed the diffusion patterns and parameters change of the cloud, and established a calculation model of cloud top height and equivalent of a radiological dispersal device by fitting. Finally, a calculation model for the inversion of explosion equivalent and height based on the temporal and spatial parameters of the cloud was constructed using the generalized regression neural network (GRNN) data‐driven algorithm, and verified by six different explosion tests with different equivalent. The results showed the explosion equivalent and height could be quickly inverted by combining the high dynamic information of the explosion cloud at the visual stage with the nonlinear regression mathematical method. The average error of the inverted explosion equivalent and height were 12.3 % and 7.1 % respectively compared with the experimental ones, indicating that the calculation model can be used for off‐site rapid detection, early warning and evaluation of unknown explosion source.