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Record W2998112846 · doi:10.2514/6.2020-1799

Development of an Anti-Icing Computational Fluid Dynamics Code

2020· article· en· W2998112846 on OpenAlex
Donovan C. Maudsley, Jason Etele

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsIcingComputational fluid dynamicsAerospace engineeringMATLABAccretion (finance)Stall (fluid mechanics)Source codeIcing conditionsComputer scienceSoftwareEnvironmental scienceMechanicsMarine engineeringEngineeringMeteorologyPhysicsOperating systemAstrophysics

Abstract

fetched live from OpenAlex

Ice accretion on aircraft surfaces can negatively affect aircraft performance and stability, and result in deteriorating flight safety. Icing codes developed over the past three decades have helped reduce the risks posed by ice accretion. Recently dielectric barrier discharge (DBD) plasma systems have been proposed as a solution to both flow control in near-stall conditions and wing anti-icing. Numerically modelling ice accretion and DBD anti-icing will prove invaluable to the aircraft development process. This paper outlines the general requirements for such an ice accretion code and how these requirements will be met using the open source CFD software OpenFOAM and MATLAB.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.639

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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