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Record W2008910076 · doi:10.2514/6.2005-658

Spongy Icing Revisited: Measurements of Ice Accretion Liquid Fraction in Two Icing Tunnels

2005· article· en· W2008910076 on OpenAlexafffundabout
Edward P. Lozowski, Myron Oleskiw, Ryan Blackmore, Anatolij R. Karev, László E. Kollar, M. Farzaneh

Bibliographic record

Venue43rd AIAA Aerospace Sciences Meeting and Exhibit · 2005
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversité du Québec à ChicoutimiThe King's UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsIcingFraction (chemistry)Accretion (finance)Materials scienceIce formationGeologyEnvironmental scienceAtmospheric sciencesMeteorologyPhysicsChemistryAstrophysicsChromatography

Abstract

fetched live from OpenAlex

AIAA 2005-658: Fifty years ago, the phenomenon of spongy icing was well-known to the in-flight icing community. Subsequently, it has been forgotten by aerodynamicists, although atmospheric scientists and marine scientists have continued to investigate it. In this paper, its occurrence is documented in two icing wind tunnels: the Altitude Icing Wind Tunnel located at the National Research Council Canada, and the CIGELE Atmospheric Icing Research Wind Tunnel located at the Université du Québec à Chicoutimi. Several dozen trials were undertaken in each icing wind tunnel using a NACA 0012 airfoil. The liquid fraction of the resulting ice accretions was measured calorimetrically, using the same calorimeter at both locations. The results show that ice accretions formed under certain in-flight conditions can contain unfrozen liquid water in amounts up to 10% or more. The liquid fraction increases with increasing LWC and rising static temperature, while it decreases with increasing airspeed. There remains some uncertainty as to the effect of MVD.

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)0.002
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 score0.446
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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
Published2005
Admission routes3
Has abstractyes

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