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Record W1985397922 · doi:10.1115/1.4025583

Experimental and Computational Analysis of a Multistage Axial Compressor Including Stall Prediction by Steady and Transient CFD Methods

2013· article· en· W1985397922 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Turbomachinery · 2013
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsAnsys (Canada)
FundersSiemens
KeywordsComputational fluid dynamicsStall (fluid mechanics)Gas compressorAxial compressorMechanicsTransient (computer programming)Computer scienceSimulationEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Siemens Energy has commissioned an extensive multiyear experimental and numerical (computational fluid dynamics (CFD)) project to improve its ability to design for and predict compressor stall. The experimental test rig is a half scale six stage axial compressor. The goal of this work is to provide insight into how best to predict the compressor performance map and in particular the stall point by applying state-of-the-art multiple blade row CFD simulation tools. A preliminary CFD analysis quantified numerical, model, and systematic error on the first stage of the compressor. Subsequent steady (mixing plane) and transient (time transformation) CFD simulations of the entire six stage compressor are compared to each other and to experimental data. Both the steady and transient simulations are shown to be computationally efficient and in very good agreement with the experimental data across the full performance map, up to stall inception on multiple speedlines. Physical explanations of the key flow features observed in the experiment, as well as of the differences between the predictions and experimental data, are given.

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.

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 score0.175
Threshold uncertainty score0.519

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 communication0.0000.001
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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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