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

Published: 17 Sep 2019 | Version 1 | DOI: 10.17632/9y86jkf9dz.1
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Description of this data

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.

Experiment data files

This data is associated with the following publication:

Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD)

Published in: Progress in Nuclear Energy

Latest version

  • Version 1

    2019-09-17

    Published: 2019-09-17

    DOI: 10.17632/9y86jkf9dz.1

    Cite this dataset

    Hanna, Botros (2019), “Data for: Machine-Learning Based Error Prediction Approach for Coarse-Grid Computational Fluid Dynamics (CG-CFD) ”, Mendeley Data, v1 http://dx.doi.org/10.17632/9y86jkf9dz.1

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Categories

Fluid Dynamics, Computational Fluid Dynamics, Computational Methods in Fluid Dynamics

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

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