RT bachelor thesis
T1 Estimating Underresolution Effects in Turbulence Modeling via a Data-Driven Convolutional Neural Network
T2 Estimación de los Efectos de Subresolución en Modelado de Turbulencia mediante una Red Neuronal Convolucional basada en Datos
A1 Romera Navia, Alberto
A2 Ingeniería Mecánica y Diseño Industrial
K1 Convolutional neural network
K1 grid resolution
K1 computational fluid dynamics
K1 Red neuronal convolucional
K1 dinámica computacional de fluidos
K1 resolución de malla
AB 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.
YR 2025
FD 2025-09-15
LK http://hdl.handle.net/10498/38994
UL http://hdl.handle.net/10498/38994
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026