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Record W2771486732 · doi:10.2514/1.t5322

Effects of Feeding Pressures on the Flowfield Structures of Supersonic Film Cooling

2017· article· en· W2771486732 on OpenAlexaboutno aff
Changqing Song, Chibing Shen

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersNational University of Defense TechnologyNational Natural Science Foundation of China
KeywordsShadowgraphSupersonic speedMechanicsMach numberShock waveMaterials scienceCoolantSupersonic wind tunnelSchlierenPressure-sensitive paintAerodynamicsShock (circulatory)NozzleWind tunnelPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Schlieren and shadowgraph visualizations of flowfields in a typical configuration of supersonic film cooling in a backward-facing slot were conducted in a Mach 2.95 continuous-suction wind tunnel, with the film gas tangentially ejected through a half-Laval nozzle of Mach 1.5. The flowfields after the step without and with the injection of film coolant were measured in the paper, including the coolant delivery pressures in the matched and unmatched conditions with the mainstream, respectively. The effects of the different injecting pressures of film coolant on the flowfields in these conditions were also analyzed. A clear visual image of the flowfields of supersonic film cooling in a backward-facing step was obtained, and the characteristics of the flowfields near the step were described. The results confirmed that the feeding pressure of film gas affected the flowfields after the step, especially the shock wave emanating from the upper tip of the lip and the shock wave and expansion fan from the lower tip of the lip. This work aided the verification of numerical simulation results and the in-depth understanding of the flowfields of supersonic film cooling near the backward-facing step.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.223

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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