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Record W1974062949 · doi:10.2514/1.9559

Hypersonic Mixing Enhancement by Compression at a High Convective Mach Number

2004· article· en· W1974062949 on OpenAlexafffund
Bernard Parent, J. P. Sislian

Bibliographic record

VenueAIAA Journal · 2004
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMach numberMechanicsShock (circulatory)PhysicsOblique shockTurbulenceSupersonic speedMaterials science

Abstract

fetched live from OpenAlex

The effect of an oblique shock and of a Prandtl‐Meyer compression fan on the characteristics of the turbulent mixing of a square-cross-section hydrogen jet in hypervelocity air is presented. The air properties before the compression process are set to those found after the first shock of a two-shock external compression shcramjet inlet at a flight Mach number of 11 and an altitude of 34.5 km. The hydrogen properties are such that the convective Mach number is 1.2, the global equivalence ratio is 0.68, and the pressure of the hydrogen matches the pressure of the air at injection. Also presented is an algebraic expression approximating increase in mixing efficiency growth through compression. The algebraic expression is based on available empirical correlations for the turbulent mixing layer and is simplified for the special case of a high-convective-Mach-number mixing layer in which the Mach numbers of both streams are high. The numerical results are obtained by using the WARP code to solve the Favre-averaged Navier‐Stokes equations closed by the Wilcox kω turbulence model and the Wilcox dilatational dissipation correction, discretized by the Yee‐Roe flux-limited scheme. Results obtained indicate increase in the mixing efficiency growth by 5.7 and 6.3 times through the oblique shock and the compression fan, respectively. Despite generating weaker axial vortices, the compression fan results into a greater increase mixing efficiency growth because of a higher density increase.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.494

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)0.0000.000

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 head0.003
GPT teacher head0.196
Teacher spread0.193 · 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 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

Citations19
Published2004
Admission routes2
Has abstractyes

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