Defiltering turbulent flow fields for Lagrangian particle tracking using machine learning techniques

TL;DR

This paper introduces a machine learning-based defiltering technique to reconstruct fluid velocities in coarse-grid turbulent flow fields, significantly improving Lagrangian particle tracking accuracy.

Contribution

The study presents a novel machine learning approach for defiltering turbulent flow fields, enhancing particle motion simulation in low-resolution data.

Findings

Perfect velocity reconstruction at filter size 4

Substantial improvement at filter sizes 8 and 16

Accurate particle trajectory and velocity fluctuation recovery

Abstract

We propose a defiltering method of turbulent flow fields for Lagrangian particle tracking using machine learning techniques. Numerical simulation of Lagrangian particle tracking is commonly used in various fields. In general, practical applications require an affordable grid size due to the limitation of computational resources; for instance, a large-eddy simulation reduces the number of grid points with a filtering operator. However, low resolution flow fields usually underestimate the fluctuations of particle velocity. We thus present a novel approach to defilter the fluid velocity to improve the particle motion in coarse-grid (i.e., filtered) fields. The proposed method, which is based on the machine learning techniques, intends to reconstruct the fluid velocity at a particle location. We assess this method in a priori manner using a turbulent channel flow at the friction Reynolds number ${\rm Re}_\tau=$ $180$. The investigation is conducted for the filter size, $n_{\rm filter}$, of $4$, $8$, and $16$. In the case of $n_{\rm filter} = 4$, the proposed method can perfectly reconstruct the fluid velocity fluctuations. The results of $n_{\rm filter} = 8$ and $16$ also exhibit substantial improvements in the fluctuation statistics although with some underestimations. Subsequently, the particle motion computed using the present method is analyzed. The trajectories, the velocity fluctuations, and the deposition velocity of particles are reconstructed accurately. Moreover, the generalizability of the present method is also demonstrated using the fields whose computational domain is larger than that used for the training. The present findings suggest that machine learning-based velocity reconstruction will enable us precise particle tracking in coarse-grid flow fields.