ITPM

Image-based throat/tube Permeability Model
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Actualizado 10 Oct 2022

Image-based Throat Permeability Model Image-based tube/throat permeability model is a mean to find the absolute permeability of tube with arbitrary cross-section this function can use 4 mthods for estimating the absolute permeability: 1) Latice Boltzmann simulation, 2) An artificial neural network with 1 input paramter , 3) Another artificial neural network with 7 input paramter and , 4) an empirical correlation which uses the average distance values of the transformed input images

Inputs: A: is a binary image in which void space is 0 and solid space is 1, this image shows the cross-section of the throat/tube Res: is the spatial resolution and it is expressed as micron/pixel Method: asks that what method you wanted to use for permeability calculation the values could be : LBM, EMP, ANN1P, and ANN7P. Plot: when put as 1 it will shows the LBM convergence charts and if set to zero it wont

Output: Absolute Permeability of throat/tube in Darcy

The LBM section is adopted from this source: Haslam, I. W., Crouch, R. S., & Seaïd, M. (2008). Coupled finite element–lattice Boltzmann analysis. Computer Methods in Applied Mechanics and Engineering, 197(51-52), 4505-4511.

If you are using ITPM in your research, please cite this article:

Hybrid Pore network and Lattice Boltzmann Permeability modeling accelerated by machine learning, Arash Rabbani, Masoud Babaei, Journal of Advances in Water Resources, 2019

Note: In order to run this code on MATLAB, you need to have Image Processing and Neural Fitting Toolboxes

Check out my tutorial videos on porous material modeling via Matlab on youtube:
https://www.youtube.com/playlist?list=PLaYes2m4FtR3DBM7TIb6oOZYI-tG4fHLd

Also, more description is in the GitHub address:
https://github.com/ArashRabbani/PaperCodes/tree/master/001-Image-based%20Throat%20Permeability%20Model

Citar como

Hybrid Pore network and Lattice Boltzmann Permeability modeling accelerated by machine learning, Arash Rabbani, Masoud Babaei, Journal of Advances in Water Resources, 2019

Compatibilidad con la versión de MATLAB
Se creó con R2018b
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux
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Más información sobre Statistics and Machine Learning Toolbox en Help Center y MATLAB Answers.

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1.0.2

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