OpenPNM: A Pore Network Modeling Package
By Jeff T. Gostick, Mahmoudreza Aghighi, James Hinebaugh, T. Tranter, Michael A. Hoeh, Harold Day, Brennan Spellacy, Mostafa H. Sharqawy, Aimy Bazylak, A. Burns and 2 more. Computing in science & engineering (Print), 2016
382 citations recorded by the source corpus.
Pore network modeling is a widely used technique for simulating multiphase transport in porous materials, but there are very few software options available. This work outlines the OpenPNM package that was jointly developed by several porous media research groups to help address this gap. OpenPNM is written in Python using NumPy and SciPy for most mathematical operations, thus combining Python's ease of use with the performance necessary to perform large simulations. The package assists the user with managing and interacting with all the topological, geometrical, and thermophysical data. It also includes a suite of commonly used algorithms for simulating percolation and performing transport calculations on pore networks. Most importantly, it was designed to be highly flexible to suit any application and be easily customized to include user-specified pore-scale physics models. The framework is fast, powerful, and concise. An illustrative example is included that determines the effective diffusivity through a partially water-saturated porous material with just 29 lines of code.
Subfields: Environmental Engineering, Mechanics of Materials, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/498e94df079e6562138565af0acb1dde06e478a4
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Citations in the corpus
- PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images (2019, background): “…such as watershed partitioning to create networks (Khan, Tranter, Agnaou, Elkamel, & Gostick, 2019, J Gostick (2017)) for pore-network modeling (J. Gostick et al., 2016), and direct numerical simulations (García-Salaberri et al., 2019) which can often supplant experiments (García-Salaberri et…”
- Versatile and efficient pore network extraction method using marker-based watershed segmentation. (2017, methodology): “The maximal ball values were 023307-9 obtained by importing the data files provided by the Blunt group into OPENPNM, which has the ability to import the “statoil” format.”