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Record W4205178087 · doi:10.2514/6.2022-1410

RANS Analysis of Merging Supersonic Streamwise Vortices for Enhanced Mixing in Scramjet Combustors

2022· article· en· W4205178087 on OpenAlexfundno aff
Dónal McCaughey, Marco Geron, Rob Watson

Bibliographic record

VenueAIAA SCITECH 2022 Forum · 2022
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsReynolds-averaged Navier–Stokes equationsMach numberSupersonic speedScramjetVortexVorticityMechanicsPhysicsTurbulenceMixing (physics)CompressibilityClassical mechanicsCombustorChemistryCombustion

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-1410.vid Streamwise vortices are studied as a mixing enhancer for scramjet combustors due to their insensitivity to compressibility relative to spanwise structures. A specific merging interaction between supersonic corotating vortices has been shown in experiments to sustain turbulence production against decay. In this paper this interaction is investigated numerically to determine how well it can be replicated with a RANS approach, and the effect of increasing the Mach number. The shape and rotation of the merging vortices appears similar to experimental results. The decay of vorticity follows a similar trend to experimental results, however the vorticity was higher in the RANS results. At higher Mach numbers the merging process appears to be pushed further down the domain

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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