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Record W7056007011

Digital image-based computational approaches for three-dimensional microstructure characterization

2019· article· en· W7056007011 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSMARTech Repository (Georgia Institute of Technology) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsTortuositySphericityPlanarMicrostructureShearing (physics)Characterization (materials science)Node (physics)Medial axisGranularityNetwork topology
DOInot available

Abstract

fetched live from OpenAlex

Employing multiscale approaches provides an innovative solution to advancing the understanding of macro-geomechanical phenomena by capturing quantitative structure information of geomaterials at the particle-pore scale. In the last two decades, microstructural properties of Ottawa sands subjected to laboratory biaxial and triaxial compression testing have been analyzed at Georgia Tech so that their correlation with the mechanisms of strain localization could be explored. Extensive 2-D and some preliminary 3-D knowledge and insights into the inherent variation and evolving behavior induced by shearing in unconsolidated sand microstructures were learned. Aimed at enhancing and complementing these preceding studies, this research focuses on developing appropriate computational methods for 3-D microstructure characterization, with a particular focus on examining the geometry and topology of the highly intricate continuum pore space. Under a proposed skeleton-based framework, the tortuous nature of pore structure is investigated through characterizing spatial variation of geometrical tortuosity using a novel, generic computational algorithm. Based on specifying and identifying pore throats from the pore skeleton, the physically representative network architecture of pore structure is established. A local sphericity algorithm and a planar surface construction algorithm are introduced to construct pore throats and extract network statistics. The effectiveness of these pore structure analysis tools is evaluated and demonstrated on two simulated idealized packing structures. For the particle phase, image-based separation and size measurements are conducted via morphological watershed processing. The topology of the particle network is represented by the distribution of force-chain tortuosity. Design-based stereological techniques for unbiased sampling and estimation are adopted to guarantee the quantitative analyses can be performed in a scientific manner, independent of the operator. All the developed methods and tools are applied to characterize three pairs of reconstructed 3-D digital Ottawa sand microstructures, including one pair for biaxial specimens and two for triaxial specimens. Shear-induced alternations in pore structure and particle network are examined from the comparative studies between the sheared microstructure and the unsheared counterpart of each pair, as well as inside and outside the shear zone for the sheared biaxial microstructure. Variations in the inherent structures are analyzed by comparing unsheared triaxial specimens prepared with air pluviation and moist tamping methods. In the characterization of true pore morphology, the encountered geometric complications and then high computational expense highlight the difficulty and challenge of creating a unique pore network for unconsolidated porous media systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.210
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it