# Predicting the impact of particle-particle collisions on turbophoresis with a reduced number of computational particles

## TL;DR

This paper presents a novel method to accurately simulate particle-particle collisions in turbulent flows with fewer computational particles by artificially enlarging collision radii, preserving collision dynamics across different regimes.

## Contribution

It introduces a collision treatment that maintains accurate collision statistics with reduced particles by scaling collision radii, enabling efficient simulations of turbophoresis effects.

## Key findings

- The method reproduces full simulation results with fewer particles.
- It is effective across low and high Stokes number regimes.
- The approach preserves deterministic collision modeling.

## Abstract

A common feature of wall-bounded turbulent particle-laden flows is enhanced particle concentrations in a thin layer near the wall due to a phenomenon known as turbophoresis. Even at relatively low bulk volume fractions, particle-particle collisions regulate turbophoresis in a critical way, making simulations sensitive to collisional effects. Lagrangian tracking of every particle in the flow can become computationally expensive when the physical number of particles in the system is large. Artificially reducing the number of particles in the simulation can mitigate the computational cost. When particle-particle collisions are an important aspect determining the simulation outcome, as in the case when turbophoresis plays an active role, simply reducing the number of particles in the simulation significantly alters the computed particle statistics. This paper introduces a computational particle treatment for particle-particle collisions which reproduces the results of a full simulation with a reduced number of particles. This is accomplished by artificially enhancing the particle collision radius based on scaling laws for the collision rates. The proposed method retains the use of deterministic collision models and is applicable for both low and high Stokes number regimes.