An adaptive-in-temperature method for on-the-fly sampling of thermal neutron scattering data in continuous-energy Monte Carlo codes

Abstract

While on-the-fly methods have recently been developed for higher energy regimes, the double differential scattering of thermal neutrons has not been examined in detail until now. In this dissertation, an on-the-fly sampling method is developed by investigating the temperature dependence of the thermal double differential scattering distributions. The temperature dependence is analyzed with a linear least squares regression test to develop fit coefficients that are used to sample thermal scattering data at any temperature. The amount of pre-stored thermal scattering data has been drastically reduced from around 25 megabytes per temperature per nuclide to only a few megabytes per nuclide by eliminating the need to compute data at discrete temperatures. The fits are capable of accurately reproducing the thermal scattering probabilities and accounting for thermal binding effects.