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Record W2013941873 · doi:10.1115/gt2014-27097

Investigation of Efficient CFD Methods for Rotating Stall Prediction in a Centrifugal Compressor Stage

2014· article· en· W2013941873 on OpenAlexaff
Daniel DeMore, Elham Maghsoudi, Jorge E. Pacheco, James M. Sorokes, Brad Hutchinson, William M. Holmes, Bryan Lobo, Vaibhav Vashistha

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsAnsys (Canada)
Fundersnot available
KeywordsStall (fluid mechanics)Computational fluid dynamicsAxial compressorGas compressorStatorMechanicsCentrifugal compressorControl theory (sociology)ImpellerComputer scienceEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

The Time Transformation Method in ANSYS CFX is investigated as an efficient substitution to Transient Rotor Stator (TRS) analysis for rotating stall prediction in a centrifugal compressor stage. The computational study was performed by varying the number of blade sectors to determine how the circumferential extent of the computational domain affects the accuracy of the stall prediction. The results obtained using a minimum number of blades, approximately one-quarter the full blade count, and approximately one-half the full blade count were compared to both TRS and steady simulations on the same mesh to characterize the predictive capability of each approach. It is shown that both steady and unsteady methods are able to predict the formation of stall cells, but significant qualitative and quantitative differences exist in the flowfield results. The largest mass flow rate at which rotating stall was captured and the number of stall cells were in good agreement with the experimental data.

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: none
Teacher disagreement score0.307
Threshold uncertainty score0.270

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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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
Published2014
Admission routes1
Has abstractyes

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