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Record W3121360950 · doi:10.22215/etd/2016-11262

Numerical Investigation of a Novel RBCC Ejector Configuration Compared to a Traditional Circular Ejector

2016· dissertation· en· W3121360950 on OpenAlexaff
Adrian Gerber

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsCarleton University
FundersJapan Aerospace Exploration AgencyNational Aeronautics and Space Administration
KeywordsInjectorNozzleEntrainment (biomusicology)Conical surfaceInletMechanicsMixing (physics)EngineeringMaterials scienceMechanical engineeringEnvironmental sciencePhysicsAcoustics

Abstract

fetched live from OpenAlex

The rocket based combined cycle can be an alternative engine to power a vehicle into space.The engine has 4 stages and one of the more challenging stages to increase performance is the first stage, the ejector stage.One proposed method to increase performance is by achieving higher entrainment performance and mixing at low free-stream velocities.The Exchange Inlet is used as an alternative nozzle to achieve this performance.The Exchange Inlet is compared to a conical nozzle within a RBCC engine with computational fluid dynamics at various pressures.The mixing and entrainment properties are compared between the two engine configurations and additional cases are investigated with shorter mixing sections to further investigate these properties.The Exchange Inlet is found to have better entrainment than the circular nozzle in all cases.' fluctuations from Reynolds Averaging " Fluctations from Favre Averaging xvi

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)0.001
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.241
Teacher spread0.207 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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