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  4. A methodology to calibrate and to validate effective solid potentials of heterogeneous porous media from computed tomography scans and laboratory-measured nanoindentation data
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A methodology to calibrate and to validate effective solid potentials of heterogeneous porous media from computed tomography scans and laboratory-measured nanoindentation data

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A Methodology to Calibrate and to Validate Effective Solid.pdf

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sword-2019-09-17T12:32:01.original.xml (130 B)
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Author(s)
Monfared, Siavash
•
Laubie, Hadrien
•
Radjai, Farhang
•
Hubler, Mija
•
Pellenq, Roland
•
Ulm, Franz-Josef
Date Issued
2018
Journal
Acta Geotechnica
Publisher
Springer Science and Business Media LLC
Version
Author's final manuscript
Abstract
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Built on the framework of effective interaction potentials using lattice element method, a methodology to calibrate and to validate the elasticity of solid constituents in heterogeneous porous media from experimentally measured nanoindentation moduli and imported scans from advanced imaging techniques is presented. Applied to computed tomography (CT) scans of two organic-rich shales, spatial variations of effective interaction potentials prove instrumental in capturing the effective elastic behavior of highly heterogeneous materials via the first two cumulants of experimentally measured distributions of nanoindentation moduli. After calibration and validation steps while implicitly accounting for mesoscale texture effects via CT scans, Biot poroelastic coefficients are simulated. Analysis of stress percolation suggests contrasting pathways for load transmission, a reflection of microtextural differences in the studied cases. This methodology to calibrate elastic energy content of real materials from advanced imaging techniques and experimental measurements paves the way to study other phenomena such as wave propagation and fracture while providing a platform to fine-tune effective behavior of materials given advancements in additive manufacturing and machine learning algorithms.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
http://creativecommons.org/licenses/by-nc-sa/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/136044
DOI of Published Version
10.1007/S11440-018-0687-9
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