Gaussian particle selection pairing for the generalised binomial Langevin multiple mapping conditioning model

Gaussian particle selection pairing for the generalised binomial Langevin multiple mapping conditioning model

Stochastic implementations of mixing models typically use particles where mixing is modelled by ultimately calculating weighted averages of particle pairs. The method for pairing particles is the principal difference between most of the mixing models. Pairs of particles are sometimes only permitted to mix within a given distance of another particle but there is rarely any bias to which a particle within an acceptable distance is selected, with the closest particle the most common method if any bias is applied. An approach used by the authors is uniform particle selection, where particles within a certain distance are equally likely to be paired. A comparison between uniform and Gaussian particle pairing selection was investigated for the binomial-Langevin Multiple Mapping Conditioning model using homogeneous nonreacting DNS as a test case. No significant differences between the two selection pairing methods were found for the range of parameters tested, but the effect of the method in a reactive case is not yet investigated.

Files associated with this item cannot be displayed due to copyright restrictions, but may be accessed online. Please see the link in the URL field.