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Record W4392954725 · doi:10.2514/1.c037682

Numerical Icing Simulations of Cylindrical Geometry and Comparisons to Flight Test Results

2024· article· en· W4392954725 on OpenAlexaffabout
Zachary R. Milani, Edgar Matida, Fatemeh Razavi, Kaniz Sultana, R. Timothy Patterson, Leonid Nichman, Ali Benmeddour, Kenny Bala

Bibliographic record

VenueJournal of Aircraft · 2024
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council CanadaCarleton UniversityUniversity of Ottawa
FundersDivision of Atmospheric and Geospace Sciences
KeywordsIcingAerospace engineeringGeometryAngle of attackFlight testMechanicsAerodynamicsGeologyMathematicsPhysicsEngineeringMeteorology

Abstract

fetched live from OpenAlex

There is growing interest in government and industry to use numerical simulations for the Certification by Analysis of aircraft ice protection systems as a cheaper and more sustainable alternative to wind-tunnel and flight testing. The ice accretion on a cylindrical test article mounted under the wing of the National Research Council of Canada’s Convair-580 research aircraft during a flight test in Appendix O icing conditions was simulated using Ansys FENSAP-ICE™. A multishot simulation with input parameters averaged over the full icing period led to an increased level of liquid catch and ice accretion (by mass), and a broader ice profile when compared to a simulation with shot-averaged input parameters. An additional simulation using Ansys’ proprietary “extended icing data with vapor solution” method for calculating heat fluxes at the icing surface resulted in a broader ice profile in comparison to the classical technique, which produced a similar amount of accretion by mass. No combination of simulation settings, input parameters, and multishot methods tested in this study generated the same level of surface detail observed during flight testing, however, the amount of ice accretion, general location of ice features, and formation processes were in good agreement with the experimental results.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

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