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Record W6891662468 · doi:10.4224/40001227

Consistent ice-crushing physics at small and large scales: from ice skating to ice-induced vibration of structures

2019· other· en· W6891662468 on OpenAlexaffvenue

Bibliographic record

VenueNPARC · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsInstitut National de la Recherche ScientifiqueNational Research Council Canada
Fundersnot available
KeywordsSpallIndentationThin layerFlow (mathematics)Adiabatic processLayer (electronics)VibrationRange (aeronautics)

Abstract

fetched live from OpenAlex

Observations from in-situ video records acquired during laboratory ice-crushing experiments and medium-scale ice-indentation field tests exhibit remarkable consistency. Spalling of ice away from the contact zone produces a sawtooth pattern in the load records and the majority of the actual movement of the indentor into the ice occurs during the sharp drops in load associated with the spalls. At least half of the load is borne on relatively-intact ice (hard zones) where the interface pressure, from calculations using load data and measured hard-zone areas and from pressure sensors, is in the range 20 -70 MPa. The visual data show how spalling determines the evolution of hard-zone size and shape during the tests. A thin slurry layer of melt (~ 16%) and ice particles (~84%) produced at the hard-zone interface areas has been observed in the laboratory tests and its thickness (< 0.2 mm) determined. Similarly, significant quantities of melt/slurry produced in medium-scale indentation tests have also been documented. These observations are consistent with a process of heat generation, and consequent melt production, caused by rapid viscous flow of the thin slurry layer at the hard-zone interface. Additionally, recent analysis of data from earlier lab tests has identified a mechanism to explain how tiny ice particles from the hard-zone interface get into the slurry, to comprise the majority of its bulk. The data from in situ high-speed imaging records of ice crushing, and from records of rapid adiabatic hydraulic pressurization of ice samples in a pressure vessel, suggest that small Tyndall melt figures, produced by frequent and sharp pressure spikes during ice crushing, create a thin weakened layer at the hard-zone interface surface. The ambient flow of slurry at the interface could shear off particles from the weakened surface layer that become entrained in the slurry. The melt-production process, and the erosion/entrainment of hard-zone ice particles by the slurry layer, could account for the rapid removal of hard-zone material from the crushing interface. The integrity of the above understandings has been demonstrated in a few cases. In one instance, the understandings, particularly with respect to ice-spalling behavior, provided a comprehensive explanation of large-scale ice-induced vibration of structures, and furthermore led to a technology (known as ‘Blade-Runners’) for mitigating the phenomenon. In a second instance, remarkable aspects of ice-crushing friction have been shown to stem from essentially the same ice-crushing physics noted above, and the slurry layer has further been shown to be highly lubricating. For example, data from recent experiments of a mock ice-skating blade have shown that crushing that occurs when the blade is sliding laterally on an ice surface, as happens when a skater applies a pushing stride to accelerate or when the skater is quickly stopping, produces regular tiny spallation events at the ice/blade interface that result in a sawtooth load pattern. Additionally, the high lubricity of the slurry layer beneath the blade during lateral sliding and also when gliding forward, where crushing on asperities and crushing due to ploughing/gouging occurs, largely accounts for the low friction force that is necessary for ice skating.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.261
Teacher spread0.233 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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