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Record W3122619699 · doi:10.2514/1.j060077

Effect of Film Cooling on Entropy Noise Generation in a Stator Blade Row

2021· article· en· W3122619699 on OpenAlexfundno aff
Thomas Bach, Maxime Huet, Rob Watson, Stephen Spence, Yasser Mahmoudi

Bibliographic record

VenueAIAA Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilQueen's UniversityQueen's University Belfast
KeywordsMechanicsAttenuationInletReynolds numberMaterials scienceLeading edgePhysicsAcousticsTurbulenceOpticsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

In this study the effect of film cooling on entropy wave attenuation and indirect entropy noise generation was investigated in a subsonic stator blade row with a leading-edge, pressure-side, and suction-side cooling geometry. The investigation was conducted on a small blade midspan section (in a linear cascade arrangement), using a three-dimensional unsteady Reynolds-averaged Navier–Stokes simulation approach. A planar entropy wave was injected at the inlet for a range of frequencies from 200 to 1000 Hz. Additionally, the compact model by Cumpsty and Marble was extended to account for a cooling flow. Results show that for an increase in coolant mass flow rate of 3.4%, the entropy noise increases by about 12% for the transmitted pressure wave, whereas the reflected pressure wave increases by about 9%. This is attributed to the change in convective acceleration in the blade passage. The film cooling was found to have a minor effect in promoting the entropy wave attenuation. The extended compact model was capable of capturing both the entropy wave attenuation and increase in the acoustic transfer function due to 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: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.259

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 head0.215
Teacher spread0.211 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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