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Record W2621324753 · doi:10.2514/6.2017-3927

Evaluation of Alternative Altitude Scaling Methods for Thermal Ice Protection System in NASA Icing Research Tunnel

2017· article· en· W2621324753 on OpenAlexaff
Sam Lee, Harold E. Addy, Andy P. Broeren, David Orchard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsIcingScalingReynolds numberWind tunnelAltitude (triangle)MeteorologyEnvironmental scienceAtmospheric sciencesMechanicsGeologyMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

A test was conducted at NASA Icing Research Tunnel to evaluate altitude scaling methods for thermal ice protection system. Two new scaling methods based on Weber number were compared against a method based on Reynolds number. The results generally agreed with the previous set of tests conducted in NRCC Altitude Icing Wind Tunnel where the three methods of scaling were also tested and compared along with reference (altitude) icing conditions. In those tests, the Weber number-based scaling methods yielded results much closer to those observed at the reference icing conditions than the Reynolds number-based icing conditions. The test in the NASA IRT used a much larger, asymmetric airfoil with an ice protection system that more closely resembled designs used in commercial aircraft. Following the trends observed during the AIWT tests, the Weber number based scaling methods resulted in smaller runback ice than the Reynolds number based scaling, and the ice formed farther upstream. The results show that the new Weber number based scaling methods, particularly the Weber number with water loading scaling, continue to show promise for ice protection system development and evaluation in atmospheric icing tunnels.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
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 head0.292
GPT teacher head0.485
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations4
Published2017
Admission routes1
Has abstractyes

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