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Record W4307372021 · doi:10.3389/frwa.2022.974145

Simulation of diffusive solute transport in heterogeneous porous media with dipping anisotropy

2022· article· en· W4307372021 on OpenAlexafffund
Danyang Su, Mingliang Xie, K. Ulrich Mayer, Kerry T. B. MacQuarrie

Bibliographic record

VenueFrontiers in Water · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of New BrunswickUniversity of British Columbia
FundersCompute CanadaWestern Canada Research GridNuclear Waste Management Organization
KeywordsAnisotropyPorous mediumDiffusionCoordinate systemCartesian coordinate systemAnisotropic diffusionTortuosityGridGeometryMechanicsMaterials scienceStatistical physicsComputer scienceGeologyPhysicsPorosityThermodynamicsMathematicsGeotechnical engineeringOptics

Abstract

fetched live from OpenAlex

Dipping anisotropy is a common feature in heterogeneous porous media that can substantially affect solute transport. For problems with complex geometry the influence of dipping anisotropy must be analyzed using numerical models, since suitable analytical solutions are not available. The most straightforward approach is to use a Cartesian coordinate system aligned with the material coordinate system. However, this approach is usually not practical, especially in 3D simulation domains with dipping layers and heterogeneous material properties. Furthermore, in the case of diffusion-dominated transport, the effect of anisotropy is often neglected. In this research, a general-purpose, fully 3-D unstructured grid code was developed to simulate diffusion-dominated solute transport in systems with dipping anisotropy, while accounting for complex geometry. The code has been verified against both 2-D and 3-D analytical solutions and has then been applied to two anisotropic diffusion problems, including an in-situ diffusion experiment and a hypothetical deep geologic repository, respectively. The simulation results indicate that consideration of anisotropy is required if the solute distribution in the rock matrix is of importance, in particular for assessing long-term evolution in layered systems. The formulation presented provides a versatile method for assessing diffusion-dominated solute transport in systems with dipping anisotropy subject to complex geometry.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.282

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)0.0000.000

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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes2
Has abstractyes

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