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Record W4380589950 · doi:10.4271/2023-01-1418

Comparison of Freeze-Out versus Grind-Out Ice Crystals for Generating Ice Accretion Using the ICE-MACR

2023· article· en· W4380589950 on OpenAlexaffabout
Martin Neuteboom, Eric Fleurent-Wilson, Jennifer L. Chalmers

Bibliographic record

VenueSAE International Journal of Advances and Current Practices in Mobility · 2023
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsTransport CanadaNational Research Council Canada
Fundersnot available
KeywordsIcingIce crystalsSpan (engineering)MeteorologyGrindMaterials scienceAltitude (triangle)Accretion (finance)Atmospheric sciencesGeologyEngineeringPhysicsGeometryComposite materialMathematicsAstrophysicsGrindingStructural engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Since the introduction of ice crystal icing certification requirements [<span class="xref">1</span>], icing facilities have played an important role in demonstrating compliance of aircraft air data probes, engine probes, and increasingly, of turbine engines. Most sea level engine icing facilities use the freezing-out of a water spray to simulate ice crystal icing conditions encountered at altitude by an aircraft in flight. However, there are notable differences in the ice particles created by freeze-out versus those observed at altitude [<span class="xref">2</span>, <span class="xref">3</span>, <span class="xref">4</span>]. Freeze-out crystals are generally spherical as compared to altitude crystals which have variable crystalline shapes. Additionally, freeze-out particles may not completely freeze in their centres, creating a combination of super-cooled liquid and ice impacting engine hardware. An alternative method for generating ice crystals in a test facility is the grinding of ice blocks or cubes to create irregular shaped crystals. These grind-out particles have a different morphology to atmospheric crystals. but are fully glaciated and their irregular shapes may better approximate the fracture dynamics of atmospheric crystals when impacting engine hardware. The National Research Council (NRC), in collaboration with Transport Canada Civil Aviation (TCCA), have studied the differences between using freeze-out generated ice crystals and grind-out ice crystals to generate ice accretion in a compressor rig: the ice-crystal environment-modular axial compressor rig (ICE-MACR) in the NRC’s altitude icing wind tunnel (AIWT). Comparison of the freestream ice crystal morphologies is presented as well as the fractured particle characteristics downstream of a two-stage compressor within the compressor annulus. Qualitative and quantitative comparisons are made of the accretion behaviour resulting from the two ice-crystal generating methods. It was found that while particle morphology differs considerably between freeze-out and grind-out before rotor impact, fractured particle size and accretion within the rig was similar for both methods for the limited range of overlapping conditions that could be produced in the test facility.</div></div>

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.122
GPT teacher head0.451
Teacher spread0.329 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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