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Record W1604432265 · doi:10.4271/2005-01-3375

Mechanisms for Downstream Ice Growth

2005· article· en· W1604432265 on OpenAlexaff
Egemen Ogretim, Wade Huebsch, J.C. Narramore, Bob Mullins

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsDownstream (manufacturing)Environmental scienceComputer scienceEngineeringOperations management

Abstract

fetched live from OpenAlex

Even though aircraft icing has been an active area of research for many years due to its public safety ramifications, there are still gaps in the knowledge base, such as in the ice accretion process. Ice prediction codes have been developed and generally can capture the gross features of the ice shape for many areas of parameter space. However, there are still features of the ice shapes that are not captured and not well understood. For example, current icing codes have difficulty in predicting the ice or frost that develops beyond the impingement limits from a standard trajectory analysis. This is a strong indicator that there are other physical mechanisms that lead to ice growth in these areas, which require further investigation. The present study focuses on the effects of relative humidity and shed vortices from the ice surface on the downstream ice growth. Relative humidity was found to be a secondary effect in the direct impingement regions due to the time scales involved. However, exposure to supersaturated air for long periods can lead to localized ice/frost growth aft of the primary ice shape. It was also found that the vortices shed from ice surface or vortices entrapped within the ice roughness can alter the trajectory paths of the droplets and potentially change the ice growth process. The altered paths result in impingement on areas that are beyond the direct impingement region.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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.

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
Published2005
Admission routes1
Has abstractyes

Explore more

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