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Record W4416400833 · doi:10.2514/1.c038627

Case Studies in Modeling Cruise-Generated Trailing Vortices

2025· article· en· W4416400833 on OpenAlexafffundabout
Anthony Brown, Frank Holzäpfel

Bibliographic record

VenueJournal of Aircraft · 2025
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaFederal Aviation AdministrationDeutsches Zentrum für Luft- und Raumfahrt
KeywordsVortexWake turbulenceWakeTurbulenceCruiseDescent (aeronautics)Probabilistic logicJet (fluid)

Abstract

fetched live from OpenAlex

Trailing vortices from jet transport aircraft in high-altitude cruising flight have been measured in high spatiotemporal detail by a research jet operated by the National Research Council Canada. The vortex locations and circulations; descent rate; and vortex core thermodynamic, dynamic, transport, and spatial scale characteristics were derived. Also, from these measurements, the background atmospheric state properties of pressure, wind structure, temperature, thermal stratification, and turbulence have been established. Selected cases from the measured data have been used to evaluate two versions of the Probabilistic 2-Phase (P2P) wake vortex prediction model. While the default P2P version uses ground-based measurement data to calibrate its probabilistic envelopes, the runtime-optimized airborne version (P2P a ) uses uncertainties of all relevant impact parameters to construct the probabilistic envelopes. Flight and model data have been compared, and the suitability of the P2P model versions for onboard wake vortex prediction and warning during cruise have been discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.272

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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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