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Record W4392960115 · doi:10.1017/jfm.2024.150

Pore-scale mushy layer modelling

2024· article· en· W4392960115 on OpenAlexafffund
F. Amiri, S. L. Butler

Bibliographic record

VenueJournal of Fluid Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsScale (ratio)Materials scienceLayer (electronics)MechanicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Equations describing mushy systems, in which solid and liquid are described as a single continuum, have been extensively studied. Most studies of mushy layers have assumed them to be ‘ideal’, such that the liquid and solid were in perfect thermodynamic equilibrium. It has become possible to simulate flows of passive porous media at the pore scale, where liquid and solid are treated as separate continua. In this contribution, we study the simplest possible mushy layers at the pore scale, modelling a single straight cylindrical pore surrounded by a cylindrical annulus representing the solid matrix. Heat and solute can be exchanged at the solid–liquid boundary. We consider harmonic temperature and concentration perturbations and examine their transport rates due to advection and diffusion and the melting and solidification driven by this transport. We compare the results of this numerical model with those of a one-dimensional ideal mushy layer and with analytical solutions valid for ideal mushy layers for small temperature variations. We demonstrate that for small values of an appropriately defined Péclet number, the results of the pore-scale model agree well with ideal mushy layer theory for both transport rates and melting. As this Péclet number increases, the temperature and concentration profiles with radius within the pore differ significantly from constant, and the behaviour of the pore-scale model differs significantly from that of an ideal mushy layer. Some effects of mechanical dispersion arise naturally in our pore-scale model and are shown to be important at high Péclet number.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.250
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes2
Has abstractyes

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