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Record W2907550665 · doi:10.2514/1.t5479

Effects of Lip Thickness on the Flowfield Structures of Supersonic Film Cooling

2018· article· en· W2907550665 on OpenAlexaboutno aff
Changqing Song, Chibing Shen

Bibliographic record

VenueJournal of Thermophysics and Heat Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersNational University of Defense TechnologyNational Natural Science Foundation of China
KeywordsSupersonic speedShock waveMach numberMaterials scienceSupersonic wind tunnelMechanicsShock (circulatory)SchlierenCoolantMach waveOpticsPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Schlieren visualizations of flowfield structures of supersonic film cooling in a backward-facing step were conducted in a Mach 2.95 suction wind tunnel with the film coolant tangentially ejected through half-Laval nozzles, all of which were designed in Mach 1.5 but with different lip thicknesses. The film coolants were fed with pressures matching the mainstream, and the flowfield changes of supersonic film cooling under different lip thicknesses between the slot and mainstream were clearly visualized under the conditions of the same step and slot heights. When the step height was kept the same, an increase in lip thickness decreased the angle of the shock wave emanating from the upper tip of the lip, and it decreased the strength of the reattachment shock wave when it was larger than a certain value. When the slot height was kept the same, the increase in lip thickness tended to decrease the angles of the shock waves emanating from the upper and lower tips of the lip and reattachment shock waves. This work provides an in-depth understanding of the flowfields of supersonic film cooling near the backward-facing step and may be used as a reference for the design of supersonic film 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.229

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 head0.005
GPT teacher head0.190
Teacher spread0.186 · 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 designBench or experimental
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

Citations16
Published2018
Admission routes1
Has abstractyes

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