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Record W2007762164 · doi:10.1115/gt2010-22729

Simulations of Flow Ingestion and Related Structures in a Turbine Disk Cavity

2010· article· en· W2007762164 on OpenAlexafffund
Steve Julien, Julie Lefrançois, Guy Dumas, Guillaume Boutet-Blais, Simon Lapointe, Jean‐François Caron, Remo Marini

Bibliographic record

VenueVolume 4: Heat Transfer, Parts A and B · 2010
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversité Laval
FundersConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsPurgeFlow (mathematics)MechanicsMass flowTurbineExternal flowRotor (electric)Volumetric flow rateMass flow rateFlow velocityMaterials sciencePhysicsSimulationMechanical engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Preliminary results of unsteady numerical simulations of disk cavity flow in interaction with the main gaspath flow in an axial turbine are presented in this article. A large periodic sector including vanes, blades and disk cavity of approximately 74° has been used in order to allow for the formation of large scale flow structures within the cavity. Three purge flow rates have been tested, namely no purge, low purge and high purge flow rates. Energetic large scale flow structures are detected through flow visualizations for the two lowest purge flow rates. They are found to rotate at an angular velocity slightly less than the rotor speed. The presence of the large scale structures involves important pressure perturbations inside the cavity that may lead to deep mass flow ingress, whereas the unsteady vane-blade interaction seems to cause only shallow ingress. Increasing purge flow rate appears to have a stabilizing effect on the pressure fluctuations inside the cavity and to reduce the intensity of the large scale flow structures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.196
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueVolume 4: Heat Transfer, Parts A and BSame topicCombustion and flame dynamicsFrench-language works237,207