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Record W2314168980 · doi:10.2514/6.2011-7059

Computational Investigation of Two-Dimensional Ejector Performance

2011· article· en· W2314168980 on OpenAlexaff
R. J. Margason, Paul M. Bevilaqua

Bibliographic record

Venue11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsInjectorComputer scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Computational Fluid Dynamics has been used to develop performance maps of thrust augmenting ejectors for use in the conceptual design of STOVL aircraft. The effect of changes in the principle ejector design parameters, including the inlet area, diffuser area, and length of the ejector duct, as well as the type and configuration of the primary nozzles, were computed. It is concluded that the optimum inlet area occurs at a relatively narrow peak whose value depends on the length of the ejector duct. It has also been shown that a workstation can be used to compute accurate solutions of the Reynolds averaged Navier-Stokes equations, in a computational domain large enough to include the elliptic effect of the conditions at the ejector exit on the flow into the ejector inlet.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.207
Teacher spread0.192 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

Explore more

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