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Record W4391301411 · doi:10.2514/6.2024-1427

Numerical Analysis of an Ejector under Pulsating Inflow Characteristic of RDC Exhaust Conditions

2024· article· en· W4391301411 on OpenAlexaff
Gregory S. Uhl, Saïd Taileb, Nicolas Odier, Stéphan Zurbach, Thierry Poinsot, Marc Bellenoue

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsInjectorInflowMechanicsJet (fluid)Materials scienceNuclear engineeringEnvironmental sciencePhysicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

To generate favorable flow characteristics for turbine integration at the outlet of a Rotating Detonation Combustor (RDC), installing an ejector or bypass flow device between both components is a viable option. As a first step, an existing ejector is investigated numerically using a Large-Eddy Simulation (LES). Initially, a stationary operating point is investigated and results are compared to available experimental data, where good agreement could be obtained. Subsequently, an axially pulsated flow representative of RDC exhaust velocities is generated at the primary nozzle exit. The amplitude of pressure fluctuations is reduced by about 70% throughout the ejector, whereas the pulsation frequency is retained at the ejector outlet. For the presented operating point, a secondary normal shock appears periodically at the end of the constant-area mixing chamber and retracts upstream as a pressure wave. This flowfield characteristic might not be preferable for a dedicated RDC - ejector configuration due to the introduced net total pressure loss. The shock formation is likely due to the constant-area mixing chamber acting as an aerodynamic convergent under boundary layer presence. This result indicates that an unsteady approach is required to characterise RDC - ejector interaction.

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 categoriesInsufficient payload (model declined to judge)
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 score0.510
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.269
Teacher spread0.256 · 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.

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
Published2024
Admission routes1
Has abstractyes

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