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Record W4407397355 · doi:10.2514/6.2025-0754

Experimental Validation of a 1D Control Volume Solver at Multiple Ejector Primary Jet Mass Flow Rates and High Entrainment Conditions

2025· article· en· W4407397355 on OpenAlexaff
Derek Lastiwka, Jeff Edwards, Artem Korobenko, Craig T. Johansen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInjectorEntrainment (biomusicology)Jet (fluid)MechanicsControl volumeSolverMass flow rateFlow (mathematics)Environmental scienceVolume of fluid methodVolume (thermodynamics)Computer scienceAerospace engineeringPhysicsMaterials scienceThermodynamicsEngineeringAcoustics

Abstract

fetched live from OpenAlex

Experimental validation of a quasi 1D ejector-ramjet (ERAM) solver was performed at various subsonic flow inlet conditions corresponding to high entrainment ratios. An ejector apparatus designed to replicate the intake of an ejector-ramjet engine under development, and was connected to a vacuum chamber to achieve the desired inlet conditions. Two groups of experiments were performed, one where the ejector apparatus’ primary jet was enabled and three different primary jet mass flow rates were tested, and the other group where the primary jet was disabled. In both groups of experiments various-sized orifice plates were used to restrict the flow entering the evacuated vacuum chamber to achieve the desired high entrainment ratios. Two groups of simulations were performed with the 1D-ERAM, one where the friction factor calculations used assumed fully developed turbulent flow, and one where the friction factor calculations accounted for flow development in the mixing tube. From the experimental data, ejector performance parameters of entrainment and compression ratios were calculated, and were then compared against the predictions made by the 1D-ERAM solver.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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