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Record W3216099166 · doi:10.1088/1367-2630/ac3cf4

Experiments on the dynamic wetting of growing icicles

2021· article· en· W3216099166 on OpenAlexafffund
John Ladan, Stephen W. Morris

Bibliographic record

VenueNew Journal of Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRipplePhysicsImpurityMechanicsInstabilityWettingThermodynamics

Abstract

fetched live from OpenAlex

Abstract The distinctive shape of an icicle is the outcome of a highly non-equilibrium process involving heat and mass transport in the presence of fluid flowing over an evolving topography. It has previously been shown that the ripple patterns with a near universal wavelength that are observed on many icicles are correlated with small levels of impurities in the feed water. Models of icicle shape evolution, and of the origin of the ripple pattern, require a detailed understanding of how liquid water flows over a growing icicle. The impurity effect is not accounted for by any existing model of ripple formation. Here, we explore this flow dynamics using laboratory-grown icicles with a fluorescent dye as an impurity. Contrary to previous models, we find that the ice is incompletely wetted by the liquid phase, and that the whole process is much more stochastic than has been previously assumed. In addition, the presence of impurities modifies the wetting properties of the ice surface, while the emerging topography interacts with the liquid distribution. There is evidence for mixed-phase ice. These observations must inform any successful model of an impurity-driven rippling instability. Our results have general implications for the morphological evolution of many natural, gravity-driven, wet ice growth processes.

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 score0.382
Threshold uncertainty score0.198

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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