Pore scale definition and computation from tomography data

During recent years characterisation capabilities of porous media have been transformed by advances in computation and visualisation technologies. It is now possible to obtain detailed topological and hydrodynamic information of porous media by combining tomographic and computational fluid dynamic studies. Despite the existence of these new capabilities, the characterisation process itself is based on the same antiquated experimental macroscopic concepts. We are interested in an up-scaling process where we can keep key information for every pore size present in the media in order to feed multi-scale transport models. Hydrometallurgical, environmental, food, pharmaceutical and chemical engineering are industries with process outcomes based on homogeneous and heterogeneous reactions and therefore sensitive to the reaction and transport processes happening at different pore scales. The present work addresses a key step in the information up-scaling process, i.e. a pore identification algorithm similar to alternating sequential filters. In a preliminary study, topological and hydrodynamic variables are correlated with the pore size. Micrometre and millimetre resolution tomographies are used to characterise the pore size distribution of a packed column and different rocks. Finally, we compute the inter-pore-scale redistribution function which is a measure of the heterogeneity of the media and magnitude needed in multi-scale modelling. Program title: Poresizedist Catalogue identifier: AEJJ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJJ_v1_0.html Program obtainable from: CPC Program Library, Queenʼs University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 312 No. of bytes in distributed program, including test data, etc.: 6534 Distribution format: tar.gz Programming language: MatLab Computer: Desktop or Laptop Operating system: Runs under MatLab (tested in Linux and Windows) RAM: Tested for problems up to 1010 bytes Classification: 7.9, 14 External routines: MatLab Image Toolbox Nature of problem: Identify individual pores from a foreground image representing void space. Solution method: Algorithm based on successive erosions with a shrinking erosion disk diameter. Restrictions: The tomographic data must fit in the available computer memory. The input tomographic data should have the open porosity space to characterise a