Data for: Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD)

Name: Data for: Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD)
Creator: Botros Hanna
Published: 2019-09-17T15:40:37.248Z
Keywords: Fluid Dynamics, Computational Fluid Dynamics, Computational Methods in Fluid Dynamics

Hanna, Botros

doi:10.17632/9y86jkf9dz.1

Data for: Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD)

Published: 17 September 2019| Version 1 | DOI: 10.17632/9y86jkf9dz.1

Contributor:

Botros Hanna

Description

Lid-driven cavity flow variables distribution in a cubic domain. Reynolds numbers range from 6000 :12000. Various grids and aspect ratios. These data are the inputs and the outputs for a regression model that predicts the local grid-induced error given the coarse-grid local fluid flow features.

Data for: Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD)

Description

Files

Categories

Licence