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Record W2888491465 · doi:10.2514/1.t5428

Computational Fluid Dynamics Investigation of Transient Effects of Aircraft Ground Deicing Jets

2018· article· en· W2888491465 on OpenAlexaff
Saleh Yakhya, Sami Ernez, François Morency

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputational fluid dynamicsTransient (computer programming)MechanicsAerospace engineeringDynamics (music)Jet (fluid)Materials sciencePhysicsComputer scienceAcousticsEngineering

Abstract

fetched live from OpenAlex

The present Paper investigates the heat transfer and momentum created by a hot turbulent propylene glycol jet impinging on a horizontal plate at below the freezing-point temperature. A model for the simulation of the initial formation of liquid film at the beginning of an aircraft ground deicing process is proposed. The volume of fluid model coupled with the film formulation is employed using computational fluid dynamics to capture the interface in multiphase flow (liquid propylene glycol/liquid water/air). The three-dimensional Reynolds averaged Navier–Stokes equations are numerically solved using a finite volume discretization under unsteady conditions. The ground deicing case consists of a box and a convergent divergent nozzle installed inside the box. Approximately 512,000 rectangular cells with 0.006 m of thickness are used to refine the mesh of the liquid film on the wall. Flow conditions set are 60°C for the propylene glycol inlet temperature and 0°C for the static ambient temperature. The computed impinged surface skin Nusselt number gives the most noticeable discrepancies at the stagnation point, where the analysis results in a lower skin Nusselt number compared to the experimental results. The magnitudes of heat transfer for the ground deicing case study are found to be in good agreement with experimental hot jet data obtained from the literature.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.335

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 head0.007
GPT teacher head0.199
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 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

Citations12
Published2018
Admission routes1
Has abstractyes

Explore more

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