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Record W2316314680 · doi:10.2514/6.2010-1128

Numerically Simulated Comparative Performance of a Scramjet and Shcramjet at Mach 11

2010· article· en· W2316314680 on OpenAlexaff
Jonathan Chan, J. P. Sislian, Derrick Alexander

Bibliographic record

Venue48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition · 2010
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsMartec (Canada)University of Toronto
Fundersnot available
KeywordsScramjetMach numberAerospace engineeringComputer scienceAeronauticsEngineeringCombustionCombustorChemistry

Abstract

fetched live from OpenAlex

The aeropropulsive performance characteristics of a scramjet and a shock-induced combustion ramjet (shcramjet) are compared at a flight Mach number of 11 and an altitude of 34.5 km. The vehicles share the same inlet type, fuel injection system, fuel/air equivalence ratio, mixing/combustor duct, methodology of nozzle design, and gridding technique. The numerical simulation of the three-dimensional vehicle flowfields from tip to tail are performed by the window allocatable resolver for propulsion code, in which the multispecies Favre-averaged Navier―Stokes equations are closed by the Wilcox k-ω turbulence model. Combustion is simulated by the H 2 -air chemical kinetics model of Jachimowski. Magnitudes of the thrust, fuel-specific impulse, pressure, and frictional forces acting on the vehicles are determined. Results show that the scramjet outperforms the shcramjet with a fuel-specific impulse of 1450 s as opposed to 1109 s developed by the shcramjet. However, the shcramjet is appreciably smaller and thus lighter than the scramjet with a combustor length which is one-fifth of the scramjet combustor length, requiring much less cooling.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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