Modification of method of sampling radiation source particle in spherical geometry

Sampling of radiation source particles is important for obtaining a correct result in the thermal radiative transfer simulation with implicit Monte Carlo. When conducting the implicit Monte Carlo simulation of spherical geometry, temperature in a cell (a spherical shell) is generally treated as a spatially independent value. That means that the particles of radiative source are uniformly distributed in a spherical shell. In some cases where the gradient of temperature inside a cell is relatively small, the treatment does not cause too many errors. However, when the opacity of material becomes large enough or the spherical shell becomes thick enough, the temperature of thermal wave head will change sharply and there will be a great temperature gradient even in a single spherical shell. The treatment will make the thermal radiation propagate much faster than the practical one, which is unacceptable in physics. We investigate the physical and numerical reasons for this violation, finding that the simulation results strongly rely on the separation of cell and that the thermal wave propagates faster with the cell number decreasing. In order to yield an accurate result, the cell number has to increase up to a large enough value. Unfortunately, more cells need more particles to reduce the numerical variance, and more particles will cost more computation time and thus causing the simulation efficiency to lower. In our work, temperature is not treated as a constant in space any more. Instead, it is treated as a linear function in a cell. Based on a new temperature function and radiative energy density distribution, a probability density distribution function of emitting position of radiation source particle in spherical geometry is obtained. Then two new spatial sampling methods are proposed and the sampling procedures of radiation source particle are designed. To verify our new sampling methods, we test several typical thermal radiative problems and compare the result with a reference solution. Numerical experiments show that both two new sampling methods can correct the errors of thermal radiative propagation speed and overcome the difficulty that simulation result is strongly dependent on cell number. In addition, both new sampling methods can obtain an accurate result even with less cells and less particles, which can saves plenty of computation time and improves the simulation efficiency.