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Record W2779793642 · doi:10.1177/1687814017748014

Numerical simulation of supersonic film cooling in a rearward-facing slot

2017· article· en· W2779793642 on OpenAlexaboutno aff
Changqing Song, Chibing Shen

Bibliographic record

VenueAdvances in Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
FundersNational University of Defense TechnologyNational Natural Science Foundation of China
KeywordsSupersonic speedCoolantMechanicsExtrapolationNozzleMaterials scienceJet (fluid)Transition pointInjectorHeat transferThermodynamicsPhysicsMathematics

Abstract

fetched live from OpenAlex

Supersonic film cooling tangentially ejected through a half Laval nozzle set in a backward-facing slot was numerically simulated to investigate the structures of flowfield and the mechanisms of heat transfer after the slot. In particular, changes in flowfield near the turning point in the cooling effectiveness curve were studied. The turning point is defined by extrapolation of the cooling effectiveness curve using a power-law relationship. The point corresponds to the streamwise position where the hot mainstream is reaching the wall protected by the film coolant and to the disappearance of the unsmooth transition points in streamwise velocity profiles, where the growth rate of velocity in wall normal direction changes gradually from one state to another. The transition of velocity becomes smooth downstream of the turning point. The unsmooth transition point also exists in other profiles of flow parameters, such as the mole fraction of film gas and the total temperature of the fluid, which are indicators of the mixing extent between the mainstream and film coolants. Moreover, the unsmooth transition point is more evident in the film coolant of nitrogen than that of helium due to the slower drop rate of effectiveness in the current configuration of the 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.507
Threshold uncertainty score0.622

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 communication0.0000.001
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 head0.246
Teacher spread0.239 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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