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Record W4233659017 · doi:10.1115/gt2010-22759

Measurements of Endwall Flows in Transonic Linear Turbine Cascades: Part I—Low Flow Turning

2010· article· en· W4233659017 on OpenAlexafffund
Farzad Taremi, S. A. Sjolander, T. J. Praisner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsCarleton University
FundersGovernment of Ontario
KeywordsTransonicSecondary flowMach numberMechanicsAerodynamicsBoundary layerTurbineVortexTotal pressureFlow (mathematics)Flow separationPhysicsMaterials scienceTurbulenceAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

The current two part paper presents the results of an experimental investigation of the endwall flows in four transonic linear turbine cascades with two levels of flow turning: 90° and 112° of total flow turning, respectively. For each case, two levels of aerodynamic loading were examined. Part I of the paper examines the low-turning case. A seven-hole pressure probe was used to document the flow fields downstream of the cascades. The experimental results include blade surface pressure distributions, total pressure losses, secondary kinetic energy and streamwise vorticity distributions. The turbine cascades considered in Part I are referred to as SL3F and SL4F (exit Mach number ≈ 0.8). The airfoils have the same inlet and outlet design flow angles, but different aerodynamic loading levels: SL4F has a Zweifel coefficient that is 30% higher than that for SL3F. The midspan flow measurements indicate that SL4F produces higher profile losses than SL3F. SL4F also exhibits stronger secondary flow with larger exit flow-angle variations. Consequently, SL4F produces higher secondary losses. Growth of secondary losses has been documented by collecting additional measurements downstream of the SL3F cascade. Vortex dissipation and endwall boundary layer growth result in additional secondary losses. The loss coefficients and the secondary flow parameters are integrated over the entire measurement plane to present their individual contributions to total entropy generation. In this context, the profile and secondary loss results from two different loss-breakdown schemes are presented and compared. The treatment of near-endwall losses in the absence of detailed pressure probe results is also discussed here.

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 score0.012
Threshold uncertainty score0.507

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations7
Published2010
Admission routes2
Has abstractyes

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