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Record W2319185036 · doi:10.2514/6.2010-7529

Ice Crystal Accretion Test Rig Development for a Compressor Transition Duct

2010· article· en· W2319185036 on OpenAlexaffabout
James MacLeod, Dan Fuleki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
FundersNational Aeronautics and Space Administration
KeywordsAccretion (finance)Gas compressorGeologyAerospace engineeringPhysicsEngineeringAstrophysics

Abstract

fetched live from OpenAlex

Ingestion of ice crystals into aircraft gas turbine engines have been shown to trigger partial or complete power loss. Although the ice crystal phenomenon has been recognized since the early 1950's, it was not until the mid-1990's that significant attention had been given to it with a key event being a flight campaign conducted with a small commuter aircraft which demonstrated ice crystals to be responsible for engine power loss. Although flight and engine testing have revealed ice crystals to be detrimental to gas turbine engine operation, these are not ideal test platforms to observe the ice crystal phenomenon due to limited access for instrumentation and visual observations. This paper discusses the development of an ice crystal test system used to simulate the ice crystal environment seen in a gas turbine compressor while maintaining visual accessibility and ease of instrumentation. This test system consists of a method to produce a range of ice crystal environments, the ability to vary airflow conditions in the rig and a static test section which simulates a gas turbine compressor transition duct. This system has been successful in producing a wide range of ice crystal test conditions and has shown significant ice accretion can occur on surfaces above 0°C while allowing for visual observations and recording of temperature data during the accretion phenomenon. © 2010 by Her Majesty the Queen in Right of Canada.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.331

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 head0.011
GPT teacher head0.212
Teacher spread0.202 · 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

Citations21
Published2010
Admission routes2
Has abstractyes

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