MétaCan
Menu
Back to cohort
Record W2046995566 · doi:10.1115/power2014-32053

Numerical Simulation of Stall Development Into Surge and Stall Control Using Air Injection in Centrifugal Compressors

2014· article· en· W2046995566 on OpenAlexaff
Taher Halawa, Mohamed Alqaradawi, Osama Badr, Mohamed S. Gadala

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSurgeStall (fluid mechanics)ImpellerShroudGas compressorCentrifugal compressorMechanicsMass flow rateAxial compressorInletMaterials scienceEnvironmental sciencePhysicsMeteorologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This study presents a numerical simulation of the formation of rotating stall and the initiation of surge in order to study the connection between stall and surge in centrifugal compressors. Also, the current paper introduces an optimization of the air injection method as a way to increase the surge margin. Results showed that during stall, the compressor is exposed to velocity and pressure fluctuations varying with time, and these fluctuations are increased suddenly and causing surge initiation. The major part which is responsible for the sudden increase in fluctuations is the vaneless region because it was found that the problem starts at the impeller exit near the shroud surface and then transfers to the impeller inlet. Results also showed that during surge, forces on the impeller blades increase to nearly double of its initial value and then decrease again. By using air injection at the vaneless region with different injection angles, it was found that injection with angle of 30° has a good effect on preventing surge and minimizing the pressure fluctuations comparing to other injection angles results. Results showed finally that the surge margin can be increased by using the injection with angle of 30° and with injection mass flow rate of 1% of the design inlet mass flow rate and this causes the surge limit to shift from 4 kg/s to 3.9 kg/s.

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 score0.336
Threshold uncertainty score0.316

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 head0.006
GPT teacher head0.208
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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicTurbomachinery Performance and OptimizationFrench-language works237,207