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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
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