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Record W1979076034 · doi:10.2514/6.2015-2025

Numerical Investigation of Wall Mounting Effects in Semi-Span Wind-Tunnel Tests

2015· article· en· W1979076034 on OpenAlexafffund
Mohamed Bouriga, François Morency, Julien Weiss

Bibliographic record

Venue53rd AIAA Aerospace Sciences Meeting · 2015
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsÉcole de Technologie Supérieure
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCompute CanadaBombardier
KeywordsSpan (engineering)Wind tunnelStructural engineeringEngineeringMaterials scienceAerospace engineering

Abstract

fetched live from OpenAlex

In this paper, the effects of installing an aircraft half-model on a sidewall of a wind tunnel are investigated. Three-dimensional RANS computations are performed on a geometry representative of an actual modern widebody airliner. The influence of a non-metric spacer, introduced between the half-model and the sidewall, is studied. The study focuses on the effects of the spacer presence on the lift coefficient and the pressure distribution around the model. Computational results show a significant change of the flow field in the case of the half-span installation compared to the full-span case. Unlike several previous investigations showing that the lift usually increases with increasing spacer height, the present study shows that the influence of the spacer is strongly dependent on the Mach number and the angle of attack. It is also found that changing the spacer height may have positive impact on the correlation between semi-span and full-span results for the lift coefficient at specific Mach numbers and angles of attack, while also negatively affecting the correlations of the same coefficient for other flow conditions.

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)0.001
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 score0.079
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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