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Record W4402689052 · doi:10.2514/6.2024-3778

Ice Crystal Environment Modular Axial Compressor Rig: Effect of TWC and Temperature on Accretion Growth Rate Using Digital Image Processing

2024· article· en· W4402689052 on OpenAlexaff
Martin Neuteboom, Jennifer L. Chalmers, M. He, Philip Chow, Jeanne G. Mason, Christopher Dumont

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsModular designGas compressorAccretion (finance)Ice crystalsImage processingMaterials scienceComputer scienceImage (mathematics)Aerospace engineeringComputer visionEngineeringMeteorologyPhysicsOperating systemAstrophysics

Abstract

fetched live from OpenAlex

The Ice Crystal Environment-Modular Axial Compressor Rig is a purpose-built compressor rig for investigating the physics of inflight ice crystal icing of turbofan aircraft engines. Digital Image Processing (DIP) of ice accretion video in the National Research Council (NRC) Ice Crystal Environment- Modular Axial Compressor Rig (ICE-MACR) was performed on results from a Federal Aviation Administration (FAA) funded test campaign in 2023. Adaptation of the methodology previously published is described namely using a dedicated area of the video images rather than a line of pixels. This was found to provide more consistent results run to run where ice may accrete and shed in different directions from the same vane. The results are presented in graphical form from which quantitative data can be extracted from what was originally a qualitative video. Results such as build and shed cycle frequency as a function of total water content (TWC) or temperature, the rate of growth of ice accretion as function of TWC or temperature and the time to transition from build and shed to steady growth as a function of TWC and temperature can be deduced. One main conclusion is that the rate of growth of an accretion is proportional to the TWC applied provided the TWC is above a minimum threshold.

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 score0.254
Threshold uncertainty score0.547

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 head0.005
GPT teacher head0.200
Teacher spread0.196 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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