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Record W2178169257 · doi:10.1115/omae2015-41894

Thermal Behavior and Growth of Submerged Ice Blocks: Experimental and Numerical Results

2015· article· en· W2178169257 on OpenAlexaff
Mehdi Ghobadi, Eleanor Bailey, Rocky Taylor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCentre For Cold Ocean Resources EngineeringMemorial University of Newfoundland
Fundersnot available
KeywordsRubbleGeologyClear iceSea ice growth processesIce wedgeSea iceIce dividePressure ridgeMaterials scienceSea ice thicknessGeotechnical engineeringArctic ice packAntarctic sea icePermafrost

Abstract

fetched live from OpenAlex

Ice rubble forms when flexural, shear or compressive forces cause broken ice to pile up at the interface between ice floes or during contact with a structure. The accumulation of rubble into linear features results in the formation of ridges, which are comprised of many individual blocks that are bonded with varying degrees of strength. Essential to the overall consolidation of a ridge is the bonding process that takes place at the interface between individual blocks. In this paper initial experimental and numerical simulations are presented that show the amount of new ice that will grow when an initially cold piece of freshwater ice is submerged in freshwater at 0° C. Understanding the thermal behavior of an ice block is important as the results can be used to understand the freeze-bonding processes that occur between two ice blocks, and further extended to understand the processes that occur between multiple ice blocks (i.e., pressure ridges and ice rubble). In the experiments presented herein, a cylindrical ice sample with an initial temperature of −20° C was submerged in a tank of water at 0° C. As the ice cylinder is initially colder than the surrounding water, heat is diffused from the water into the ice cylinder causing a new layer of ice to form around the samples. Wireless temperature sensors with onboard data loggers were placed inside the ice cylinder to measure temperature. The radius, length and weight of the sample were measured before and after the submersion to calculate the thickness of the new ice layer. COMSOL Multiphysics was employed to analyze the freezing rate and the radial temperature profile of the sample. An analytical method is also used to calculate the maximum thickness of the new ice layer formed around the sample once the temperature has equilibrated to the surrounding water temperature. Results obtained using the analytical method are then compared with experimental results. Temperature profile data collected at specified locations within the ice have also been compared with the numerical simulations. Good agreement between measured and simulated results was observed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.227
Teacher spread0.210 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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