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Record W2749660835 · doi:10.2514/1.b36574

Starting Characteristics of Prandtl–Meyer Scramjet Intakes with Overboard Spillage

2017· article· en· W2749660835 on OpenAlexaff
Niloofar Moradian, Edward N. Timofeev

Bibliographic record

VenueJournal of Propulsion and Power · 2017
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrandtl numberScramjetSpillageMagnetic Prandtl numberMechanicsLimitingTrailing edgeSupersonic speedTurbulent Prandtl numberPhysicsMathematicsEngineeringHeat transferTurbulenceChemistryCombustorMechanical engineering

Abstract

fetched live from OpenAlex

The method for obtaining the limiting contraction for supersonic intake-starting via overboard spillage demonstrated earlier by Veillard et al. (“Limiting Contractions for Starting Simple Ramp-Type Scramjet Intakes with Overboard Spillage,” Journal of Propulsion and Power, Vol. 24, No. 5, 2008, pp. 1042–1049) is applied in the present paper to Prandtl–Meyer scramjet intakes. Starting characteristics for Prandtl–Meyer intakes of various particular designs are also obtained. It is shown that the strong shock design principle proposed by Veillard et al. for simple ramp-type intakes holds for Prandtl–Meyer intakes as well, that is, the intake design based on the assumption of a strong shock terminating at the trailing edge of the intake’s ramp would lead to the Kantrowitz (self-starting) line, which is very close to the theoretically established limiting values for this intake family. The theoretical findings on startability of Prandtl–Meyer intakes are confirmed by the numerical intake-starting experiments based on the same flow model as the one used in the theory (inviscid non-heat-conducting ideal gas with constant specific heats).

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.209
Teacher spread0.205 · 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

Citations17
Published2017
Admission routes1
Has abstractyes

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