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Record W2162553271 · doi:10.1115/1.4000191

On the Design of an Aero-Engine Nose Cone Anti-Icing System Using a Rotating Heat Pipe

2009· article· en· W2162553271 on OpenAlexaff
S. Gilchrist, D. Ewing, C.Y. Ching

Bibliographic record

VenueJournal of Thermal Science and Engineering Applications · 2009
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHeat transferHeat pipeMaterials scienceEvaporatorMicro-loop heat pipeCondenser (optics)MechanicsHeat transfer enhancementThermal conductionMechanical engineeringHeat exchangerHeat transfer coefficientComposite materialEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

The feasibility of using a rotating heat pipe to anti-ice the nose cones of small turbofan aero-engines is investigated. A stationary jacket evaporator design was used to transport heat into the rotating heat pipe located along the central fan shaft of the engine. The rotating heat pipe condenser was made an integral part of the nose cone using a high conductivity, lightweight material and the tip of the nose cone. The use of heating channels along the nose cone and passive heat transfer enhancement in the evaporator were also investigated. The computational model used to predict the heat transfer performance is outlined. The overall heat transfer to the nose cone was 0.8–1.2 kW using water in the heat pipe and 0.4–0.75kW using ethanol. The heating channels were not effective due to the small contact area with the nose cone. The heat transfer enhancement in the evaporator increased the total heat transfer modestly and the temperature of the nose cone increased over the contact area made with the high conductivity material. The results show that rotating heat pipes are a feasible nose cone anti-icing technology.

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 score0.002
Threshold uncertainty score0.005

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 science0.0010.000
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 head0.017
GPT teacher head0.229
Teacher spread0.213 · 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

Citations17
Published2009
Admission routes1
Has abstractyes

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