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Record W4366214995 · doi:10.1137/22m1476642

Effects of Anisotropy in Tridimensional Diffusion: Flow Patterns and Transport Efficiency

2023· article· en· W4366214995 on OpenAlexafffund
Piyush Awasthi, M. Suneel Kumar, Yana Nec

Bibliographic record

VenueSIAM Journal on Applied Mathematics · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsThompson Rivers UniversityUniversity of British Columbia
FundersMitacs
KeywordsAnisotropyIsotropyStatistical physicsNonlinear systemPhysicsFlow (mathematics)Saddle pointSymmetry (geometry)Classical mechanicsAnisotropic diffusionGeometryMechanicsMathematics

Abstract

fetched live from OpenAlex

Modeling diffusive processes via a constant effective diffusivity value taken to represent realistic uncertainty or heterogeneity is entrenched in scientific and engineering applications. This brings forth the question, to what extent does the flow pattern changes when symmetry is broken by anisotropy. This study supplies the answer by deriving a class of tridimensional solutions to the steady nonlinear diffusion equation in a spherical domain divided into an arbitrary number of meridian sectors with distinct diffusivities and generation rates. The new family of solutions permits flexible modeling, where traditionally only isotropic radial transport was considered. The flow patterns support an extensive variety of topological terrain via tesseral and sectoral harmonics. The anisotropy gives rise to an unconventional type of a fixed point combining both node and saddle attributes. The contours are nonsmooth on the contiguity planes between sectors and might or might not be localized in the polar angle and/or azimuthal angle , implying a particle might remain confined to a relatively small neighborhood or meander over the sphere. The impact on motion trajectories and thus transport efficiency implies that the energy required to sustain a steady flow is starkly underestimated when symmetry is assumed for simplicity despite the presence of anisotropy.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.498

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 head0.005
GPT teacher head0.189
Teacher spread0.185 · 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 designSimulation or modeling
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

Citations2
Published2023
Admission routes2
Has abstractyes

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