The invention provides a
gas concentration time sequence distribution prediction method based on multivariable data driving. Based on the theoretical relationship between the
mean square displacement and the mean scattering angle cosine of neutrons in an infinite homogeneous medium, the
mean square displacement is directly counted through Monte Carlo
simulation, the mean scattering angle cosine is reversely deduced, and a first-order scattering matrix meeting the
mean square displacement conservation is constructed accordingly. In order to be suitable for a finite geometric model, a correction factor is further introduced to correct an average scattering angle cosine obtained by a traditional method. A
verification result shows that the method remarkably reduces the calculation deviation of effective proliferation factors, improves the
neutron flux distribution precision, and is particularly suitable for high-precision multi-group calculation of high-
anisotropy fast spectrum reactor cores such as
pebble-
bed high-temperature gas cooled reactors and
sodium cooled fast reactors. According to the method, the
time sequence probability distribution prediction of the
gas concentration can be realized, the prediction uncertainty is quantified, the risk indexes such as the over-limit probability are directly output, and the scientificity and the reliability of
coal mine safety early warning are improved.