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    Techniques for improved statistical convergence in quantification of eddy diffusivity moments

    Dana L. O.-L. Lavacot1,*, Jessie Liu1,*, Brandon E. Morgan2, and Ali Mani1

    • *These authors contributed equally to this work.

    Phys. Rev. Fluids 10, 114904 – Published 17 November, 2025

    DOI: https://doi.org/10.1103/yrnt-y4mp

    Abstract

    While recent approaches, such as the macroscopic forcing method (MFM) or Green's function-based approaches, can be used to compute Reynolds-averaged Navier-Stokes closure operators using forced direct numerical simulations, MFM can also be used to directly compute moments of the effective nonlocal and anisotropic eddy diffusivities. The low-order spatial and temporal moments contain limited information about the eddy diffusivity but are often sufficient for quantification and modeling of nonlocal and anisotropic effects. However, when using MFM to compute eddy diffusivity moments, the statistical convergence can be slow for higher-order moments. In this work, we demonstrate that using the same direct numerical simulation (DNS) for all forced MFM simulations improves statistical convergence of the eddy diffusivity moments. We present its implementation in conjunction with a decomposition method that handles the MFM forcing semianalytically and allows for consistent boundary condition treatment, which we develop for both scalar and momentum transport. We demonstrate that for a two-dimensional Rayleigh-Taylor instability case study, using the same DNS for all forced MFM simulations results in convergence with O(100) simulations rather than O(1000) simulations. We then demonstrate the impacts of improved convergence on the quantification of the eddy diffusivity.

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