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Record W4402688470 · doi:10.2514/1.t7335

Ice Crystal Environment – Modular Axial Compressor Rig: Heat Flux Observations Under Icing

2024· article· en· W4402688470 on OpenAlexaff
Martin Neuteboom, Jeanne G. Mason, Philip Chow, Christopher Dumont

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2024
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
FundersFederal Aviation AdministrationNational Research Council
KeywordsIcingGas compressorModular designHeat fluxFlux (metallurgy)Aerospace engineeringMaterials scienceNuclear engineeringEnvironmental scienceAtmospheric sciencesMechanical engineeringMeteorologyMechanicsHeat transferPhysicsEngineeringComputer scienceMetallurgy

Abstract

fetched live from OpenAlex

Ice crystal icing of turbofan engines occurs when an aircraft encounters atmospheric ice crystals in flight. The crystals permeate the compressor core, creating runback, which eventually refreezes, blocking airflow and leading to rollback, shedding, or flameout. The Ice crystal environment modular axial compressor rig (ICE-MACR) is a purpose-built compressor rig for investigating the physics of in-flight ice crystal icing of turbofan aircraft engines. Observations of heat flux and temperature transients of the ICE-MACR surfaces under ice crystal ingestion are presented and discussed. It was found that, when accretion occurs, heat flux transients can be modeled as [Formula: see text], where [Formula: see text] was found to be primarily a function of temperature and [Formula: see text] a function of both total water content and temperature. In cases where accretion did not occur, heat flux transients were found not to fit any simple mathematical model. Analysis of both heat flux and metal temperature at the point of transition to steady accretion growth indicates that transition typically occurs when heat flux from the metal casing to the impinging mixed-phase drops below a threshold value.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.509

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 head0.020
GPT teacher head0.207
Teacher spread0.187 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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