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Record W2075407668 · doi:10.1115/gt2008-51530

Performance Prediction of Centrifugal Impellers Using a Two-Zone Model

2008· article· en· W2075407668 on OpenAlexaff
Ian Britton, Jérôme Gauthier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsImpellerCentrifugal compressorRange (aeronautics)Performance predictionCentrifugal pumpFlow (mathematics)Gas compressorComputer scienceMechanicsMechanical engineeringRotational speedSimulationEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper outlines the methodology and theory required for the development of a computer code for the performance prediction of centrifugal impellers. The theory is blended from two main sources on two-zone model development. The equations provided show a simplified version of previous two-zone models published while maintaining a similar level of accuracy in the prediction of pressure ratio and isentropic efficiency. The idea behind the development of the algorithm is the production of an impeller map which can be used in the preliminary design phase with a minimum amount of input information, namely basic geometry which could be determined from simple design point calculations. Validation of the model was performed against compressor maps and geometries available in the literature. It was subsequently shown that with the blending of inlet, diffusion ratio and two-zone models, a centrifugal impeller model was constructed which agreed well over a wide range of rotational speeds and mass flow rates.

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)0.001
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.193
Teacher spread0.175 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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