# Optimal Closures in a Simple Model for Turbulent Flows

## TL;DR

This paper develops a computational framework to optimize eddy-viscosity closures in LES models, reducing divergence from DNS and providing insights into model limitations.

## Contribution

It introduces a PDE-constrained optimization approach for deriving optimal eddy-viscosity closures in LES, applicable to various PDE models.

## Key findings

- Optimal eddy viscosities reduce divergence rate between LES and DNS.
- Framework generalizes standard Smagorinsky model.
- Enhanced understanding of closure model performance limits.

## Abstract

In this work we introduce a computational framework for determining optimal closures of the eddy-viscosity type for Large-Eddy Simulations (LES) of a broad class of PDE models, such as the Navier-Stokes equation. This problem is cast in terms of PDE-constrained optimization where an error functional representing the misfit between the target and predicted observations is minimized with respect to the functional form of the eddy viscosity in the closure relation. Since this leads to a PDE optimization problem with a nonstandard structure, the solution is obtained computationally with a flexible and efficient gradient approach relying on a combination of modified adjoint-based analysis and Sobolev gradients. By formulating this problem in the continuous setting we are able to determine the optimal closure relations in a very general form subject only to some minimal assumptions. The proposed framework is thoroughly tested on a model problem involving the LES of the 1D Kuramoto-Sivashinsky equation, where optimal forms of the eddy viscosity are obtained as generalizations of the standard Smagorinsky model. It is demonstrated that while the solution trajectories corresponding to the DNS and LES still diverge exponentially, with such optimal eddy viscosities the rate of divergence is significantly reduced as compared to the Smagorinsky model. By systematically finding {optimal forms of the eddy viscosity within a certain general class of closure} models, thisframework can thus provide insights about the fundamental performance limitations of these models.