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Record W3009316896

Numerically simulated comparative performance of a scramjet and shcramjet at Mach 11

2010· article· en· W3009316896 on OpenAlexfundno aff
Jonathan Chan

Bibliographic record

VenueTSpace (University of Toronto) · 2010
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsScramjetMach numberAerospace engineeringMechanicsPhysicsEngineeringCombustorCombustionChemistry
DOInot available

Abstract

fetched live from OpenAlex

This study investigates the design and aeropropulsive performance of a complete, hydrogen powered, shock-induced combustion ramjet (shcramjet) at a flight Mach number of 11 and altitude of 34.5 km. The design includes a Prandtl-Meyer compression inlet, cantilevered ramp fuel injectors, a shock-inducing wedge and a divergent nozzle. Numerical studies are undertaken using the WARP code that solves the three-dimensional Favre-averaged Navier-Stokes equations closed by the Wilcox k-ω turbulence model and the Jachimowski H2/air chemical kinetics model. Studies of fuel injection properties, mixing duct length, combustor wedge and nozzle geometry are completed to maximize the overall performance of the vehicle. The final shcramjet configuration generates a specific impulse of 1110 s. A comparison is undertaken with a scramjet vehicle at identical flight conditions and using many of the same components. The comparable scramjet generates a higher specific impulse of 1450 s although it is significantly larger and therefore heavier.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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
Published2010
Admission routes1
Has abstractyes

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