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Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media
Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414857/ https://www.ncbi.nlm.nih.gov/pubmed/32770012 http://dx.doi.org/10.1038/s41598-020-69661-0 |
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author | Lubbers, Nicholas Agarwal, Animesh Chen, Yu Son, Soyoun Mehana, Mohamed Kang, Qinjun Karra, Satish Junghans, Christoph Germann, Timothy C. Viswanathan, Hari S. |
author_facet | Lubbers, Nicholas Agarwal, Animesh Chen, Yu Son, Soyoun Mehana, Mohamed Kang, Qinjun Karra, Satish Junghans, Christoph Germann, Timothy C. Viswanathan, Hari S. |
author_sort | Lubbers, Nicholas |
collection | PubMed |
description | Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications. |
format | Online Article Text |
id | pubmed-7414857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74148572020-08-11 Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media Lubbers, Nicholas Agarwal, Animesh Chen, Yu Son, Soyoun Mehana, Mohamed Kang, Qinjun Karra, Satish Junghans, Christoph Germann, Timothy C. Viswanathan, Hari S. Sci Rep Article Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications. Nature Publishing Group UK 2020-08-07 /pmc/articles/PMC7414857/ /pubmed/32770012 http://dx.doi.org/10.1038/s41598-020-69661-0 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lubbers, Nicholas Agarwal, Animesh Chen, Yu Son, Soyoun Mehana, Mohamed Kang, Qinjun Karra, Satish Junghans, Christoph Germann, Timothy C. Viswanathan, Hari S. Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media |
title | Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media |
title_full | Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media |
title_fullStr | Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media |
title_full_unstemmed | Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media |
title_short | Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media |
title_sort | modeling and scale-bridging using machine learning: nanoconfinement effects in porous media |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414857/ https://www.ncbi.nlm.nih.gov/pubmed/32770012 http://dx.doi.org/10.1038/s41598-020-69661-0 |
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