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Record W2072756419 · doi:10.1016/j.crme.2012.10.012

Computing combustion noise by combining large eddy simulations with analytical models for the propagation of waves through turbine blades

2013· article· en· W2072756419 on OpenAlex
I. Durán, Matthieu Leyko, Stéphane Moreau, Franck Nicoud, Thierry Poinsot

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComptes Rendus Mécanique · 2013
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTurbineAcousticsCombustionLarge eddy simulationGas turbinesCombustorDetached eddy simulationNoise (video)PhysicsEngineeringMechanicsTurbulenceAerospace engineeringMechanical engineeringComputer scienceReynolds-averaged Navier–Stokes equations

Abstract

fetched live from OpenAlex

Two mechanisms control combustion noise generation as shown by Marble and Candel (1977) [1]: direct noise, in which acoustic waves propagate through the turbine stages and indirect noise, in which vorticity and/or entropy waves generate noise as they are convected through turbine stages. A method to calculate combustion-generated noise has been implemented in a tool called CHORUS. The method uses the large eddy simulations of the combustion chamber obtained with the unstructured solver AVBP developed at CERFACS (Schønfeld and Rudgyard, 1999 [2]) and analytical models for the propagation through turbine stages. The propagation models (Cumpsty and Marble, 1977 [3]) use the compact row hypothesis to write matching conditions between the inlet and the outlet of a turbine stage. Using numerical simulations, the validity of the analytical methods is studied and the errors made quantified.

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.

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 score0.814
Threshold uncertainty score0.553

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 head0.016
GPT teacher head0.239
Teacher spread0.223 · 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