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Record W2092714723 · doi:10.2514/1.c031095

Optimization via FENSAP-ICE of Aircraft Hot-Air Anti-Icing Systems

2011· article· en· W2092714723 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Aircraft · 2011
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsBombardier (Canada)McGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIcingAerospace engineeringJet (fluid)Icing conditionsComputational fluid dynamicsComputer scienceMechanical engineeringMeteorologyEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a methodology for the optimization of hot-bleed-air anti-icing systems, known as Piccolo tubes. Such systems are widely used to anti-ice the wings of many commercial aircrafts, ranging from regional to wide-body jet aircrafts. Having identified the most critical in-flight icing conditions, as well as any anti-icing system constraints as inputs, the ideal aim is to achieve fully-evaporative conditions over the heated surfaces. To do so, an optimization method based on three-dimensional computational fluid dynamics, reduced-order models, and genetic algorithms was constructed to determine the optimal geometric configuration of the Piccolo tube (jet angles, spacing of jets, and distance from leading edge). The external and internal airflows are computed using the finite element Navier–Stokes applications package (FENSAP-ICE). The methodology leads to significantly-improved configurations for threeto five-dimensional design spaces.

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: none
Teacher disagreement score0.637
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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