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Record W4205609748 · doi:10.1155/2021/7891475

Modeling and Simulation of Wake Safety Interval for Paired Approach Based on CFD

2021· article· en· W4205609748 on OpenAlexvenueno aff
Xin He, Yilong Ma, Hong Yang, Yaqing Chen

Bibliographic record

VenueJournal of Advanced Transportation · 2021
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
FundersCivil Aviation Administration of China
KeywordsWakeRunwayWake turbulenceComputational fluid dynamicsSeparation (statistics)VortexInterval (graph theory)SimulationComputer scienceComputer simulationMarine engineeringAerospace engineeringEngineeringMechanicsMathematicsPhysics

Abstract

fetched live from OpenAlex

In order to relieve the stress caused by the surge of flight flow, Closely Spaced Parallel Runways (CSPRs) have been built in many hub airports, and a paired approach mode has been applied to CSPRs in some countries. This paper proposes a method for optimizing the wake separation between aircrafts which utilizes a paired approach, aiming at reducing longitudinal separation by using computational fluid dynamics technology. Firstly, the model of the wake vortex field of the paired lead aircraft is constructed. Secondly, the numerical simulation preparation for the characteristics of the wake vortex field is completed through the computational pretreatment of the model. Thirdly, a calculation model of wake safety interval based on paired approach operation is established. Finally, the proposed method shows its superiority comparing with other methods. This method realized visual analysis of wake vortex through optimization modeling based on computational fluid dynamics, contributing to increasing the capacity of the runway and improving the operation efficiency of an aerodrome.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.225
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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