%0 Journal Article 
%A Romera Navia, Alberto
%T Estimating Underresolution Effects in Turbulence Modeling via a Data-Driven Convolutional Neural Network

%D 2025
%U http://hdl.handle.net/10498/38994
%X The objective of this research is to estimate and predict the effects on a computational fluid flow that gets its grid resolution downsampled. For that, direct numerical simulation (DNS) data is generated through an in-house solver for the study case of a lid-driven cavity flow. From this data in two different resolutions— one regular resolution and one downsampled by a factor of two—, a comparison is drawn in terms of how good it adapts to the downsampled DNS results between large-eddy simulation (LES) filters and a convolutional neural network (CNN) trained on this data. The results of the study give some insights about the potential use of neural networks to act as filters, where the runtime compared to DNS solver data is three orders of magnitude faster; whereas it only outperforms LES filters in 21.36% of the cases.

%K Convolutional neural network
%K grid resolution
%K computational fluid dynamics
%K Red neuronal convolucional
%K dinámica computacional de fluidos
%K resolución de malla
%~ Universidad de Cádiz