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Record W2321275924 · doi:10.1115/gt2009-60164

Assessment of the Flow Quality of a Transonic Turbine Cascade

2009· article· en· W2321275924 on OpenAlexaff
R. Woodason, Asad Asghar, W. Allan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTransonicFlow visualizationCascadeFlow (mathematics)MechanicsSchlierenFlow separationMaterials scienceTurbineTurbulenceAerospace engineeringPhysicsAerodynamicsEngineering

Abstract

fetched live from OpenAlex

The assessment of flow quality through a newly constructed transonic turbine cascade is presented. Although the main objective of this research was to investigate the effect of the modification of a vane profile due to repair on pressure loss, only the results for checking the flow periodicity, two-dimensionality of the flow and transonic exit flow condition are described in this paper. The cascade blades were constructed using the profiles of nozzle guide vanes of a low pressure turbine of an in-service turboshaft engine. The assessment of the flow quality in the cascade was carried out using three methods: wall static pressure measurements at the inlet and exit of each flow passage of the cascade to check the flow periodicity, surface flow visualization using blackened paraffin oil to check the two dimensionality of the flow and thirdly, Schlieren flow visualization to verify the periodicity and transonic flow conditions at the exit of the cascade. The cascade inlet and exit wall pressure showed that the flow was nominally periodic in the cascade. The surface flow visualization of the suction surface showed that the flow was two-dimensional on approximately 70% of the central span and also indicated flow separations on the suction surface. The Schlieren flow visualization confirmed the flow periodicity and revealed the existence of shock waves on the suction surface and near the trailing edge of the blades.

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 score0.416
Threshold uncertainty score0.126

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 head0.010
GPT teacher head0.265
Teacher spread0.255 · 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
Published2009
Admission routes1
Has abstractyes

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