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Record W2312572789 · doi:10.1063/1.4943596

Hydrodynamic instabilities of flows involving melting in under-saturated porous media

2016· article· en· W2312572789 on OpenAlexafffund
Mozhdeh Sajjadi, Jalel Azaiez

Bibliographic record

VenuePhysics of Fluids · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research Grid
KeywordsPorous mediumInstabilityThermodynamicsHeat transferMechanicsPhysicsSaturation (graph theory)Melting pointFluid dynamicsPorosityMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The process of melting in partially saturated porous media is modeled for flow displacements prone to hydrodynamic instabilities due to adverse mobility ratios. The effects of the development of instabilities on the melting process are investigated through numerical simulations as well as analytical solution to unravel the physics of the flow. The effects of melting parameters, namely, the melting potential of the fluid, the rate of heat transfer to the frozen phase, and the saturation of the frozen material along with the parameters defining the viscous forces, i.e., the thermal and solutal log mobility ratios are examined. Results are presented for different scenarios and the enhancement or attenuation of instabilities are discussed based on the dominant physical mechanisms. Beside an extensive qualitative analysis, the performance of different displacement scenarios is compared with respect to the melt production and the extent of contribution of instability to the enhancement of melting. It is shown that the hydrodynamic instabilities tend in general to enhance melting but the rate of enhancement depends on the interplay between the instabilities and melting at the thermal front. A larger melting potential and a smaller saturation of the frozen material tend to increase the contribution of instability to melting.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.530

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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 designBench or experimental
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

Citations7
Published2016
Admission routes2
Has abstractyes

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