A collision algorithm for anisotropic disperse flows based on ellipsoidal parcel representations

In this study, a new approach to the prediction of collision probabilities is proposed, which is specifically derived for Lagrangian Monte Carlo simulations of highly anisotropic disperse flows. The collision probability formulation eliminates the influence of the collision mesh by introduction of a parcel diameter. Two non-parametric parcel diameter estimators are given, an isotropic parcel diameter estimator for homogeneous droplet clouds, and an anisotropic parcel diameter estimator for inhomogeneous droplet clouds of reduced dimensionality, which obtains an ellipsoidal parcel representation from a weighted principal component analysis of the parcel population. The new formulation effectively reduces the parameters for spatial resolution from two (mesh size, number of parcels) to one (number of parcels only). For validation, simple synthetic test cases are run that allow a comparison of the collision probability formulation to purely stochastic and purely deterministic schemes. Dependence on numerical parameters is tested exemplary with a highly anisotropic hollow-cone spray for gasoline direct injection.

► A new collision algorithm is derived able to adapt to highly anisotropic dispersions.
► During derivation, analytical correctness is preserved; model fitting is not required.
► The algorithm is validated by synthetic test cases.
► Convergence is demonstrated with an engine spray simulation.
► In anisotropic dispersions, the algorithm significantly reduces numerical dependencies.