# Lagrangian stochastic modeling of acceleration in turbulent wall-bounded flows

## TL;DR

This paper introduces a comprehensive Lagrangian stochastic model for acceleration in turbulent wall-bounded flows, capturing key features like anisotropy and intermittency, and improving boundary layer modeling.

## Contribution

It develops a novel inhomogeneous stochastic model including acceleration, generalizing existing models, and demonstrates its effectiveness in high Reynolds number channel flow simulations.

## Key findings

- Model accurately captures acceleration anisotropy
- Successfully reproduces intermittency in turbulence
- Enhances boundary layer modeling capabilities

## Abstract

The Lagrangian approach is natural to study issues of turbulent dispersion and mixing. We propose in this work a general Lagrangian stochastic model including velocity and acceleration as dynamical variables for inhomogeneous turbulent flows. The model takes the form of a diffusion process and the coefficients of the model are determined via Kolmogorov theory and the requirement of consistency with the velocity-based models. It is shown that the present model generalises both the acceleration-based models for homogeneous flows and the generalised Langevin models for the velocity. The resulting closed model is applied to a channel flow at high Reynolds number and compared to experiments as well as direct numerical simulations. A hybrid approach coupling the stochastic model with a Reynolds-Averaged-Navier-Stokes (RANS) is used to obtain a self-consistent model, as commonly used in probability density function methods. Results highlight that most of the acceleration features are well represented, notably the anisotropy and the strong intermittency. These results are valuable, since the model allows to improve the modelling of boundary layers yet remaining relatively simple. It sheds also some light on the statistical mechanisms at play in the near-wall region.