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Record W2067281702 · doi:10.1115/1.1667887

Effects of Periodic Inflow Unsteadiness on the Time-Averaged Velocity Field and Pressure Recovery of a Diffusing Bend With Strong Curvature

2004· article· en· W2067281702 on OpenAlexafffund
M. I. Yaras, P. Orsi

Bibliographic record

VenueJournal of Fluids Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInflowDiffuser (optics)ImpellerMechanicsWakeFlow (mathematics)CurvatureTurbineStatic pressurePhysicsEngineeringMechanical engineeringGeometryOpticsMathematics

Abstract

fetched live from OpenAlex

This study examines the effects of periodic inflow unsteadiness on the flow development through fishtail-shaped diffusers utilized on small gas-turbine engines. In this application, periodic unsteadiness is caused by a jet-wake type of flow discharging from each passage of the centrifugal compressor impeller. The study consists of detailed measurements in a large-scale fishtail diffuser rig with a geometry that is typical of those used in small gas-turbine engines. Measurements of the transient velocity field have been performed at five cross-sectional planes throughout the diffuser using a miniature hot-wire probe with four wires. These measurements involve frequencies of inflow unsteadiness corresponding to design as well as off-design operating conditions. Results indicate significant effects of inflow unsteadiness at the low end of the tested frequencies on the time-averaged streamwise and cross-flow velocity fields in the diffuser. This is shown to translate into a notable impact on the pressure recovery. In addition to providing insight into the physics of this flow, the experimental results presented here constitute a detailed and accurate data set that can be used to validate computational-fluid-dynamics algorithms for this type of flow.

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 score0.352
Threshold uncertainty score0.380

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 head0.002
GPT teacher head0.157
Teacher spread0.155 · 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

Citations3
Published2004
Admission routes2
Has abstractyes

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