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Record W1173437740 · doi:10.1115/gt2015-42748

Post-Surge Load Prediction for Multi-Stage Compressors via CFD Simulations

2015· article· en· W1173437740 on OpenAlexafffund
Martial Dumas, Huu Duc Vo, Yu Hong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsPolytechnique Montréal
FundersUniversité de MontréalCompute CanadaPratt and Whitney Canada
KeywordsGas compressorSurgeComputational fluid dynamicsReynolds-averaged Navier–Stokes equationsAxial compressorCentrifugal compressorTurbineAerodynamicsPlenum spacePerformance predictionEngineeringMechanical engineeringMarine engineeringSimulationAerospace engineering

Abstract

fetched live from OpenAlex

A methodology is proposed and developed for the simulation of post-surge condition in a multi-stage compressor that is part of a gas generator system that also includes the combustor and turbine and ducts. Given the essentially one-dimensional nature of surge, the approach basically consists of coupling single blade passage multi-stage RANS CFD simulations of the compressor for with 1D equations modelling the behaviour of the other components applied as dynamic boundary conditions. This method allows for the simulation of the flow behaviour inside a multi-stage compressor during surge and, by extension, for the prediction at the design phase of the time variation of aerodynamic forces on the blades and of the pressure and temperature at bleed locations inside the compressor used for turbine cooling. The main advantages of this method over existing methods are its relatively modest computational time and resource requirements and the fact that it does not require any empirical data input beyond what is used in standard CFD simulations. The method is implemented in a commercial CFD code (ANSYS CFX) and applied to three compressor geometries with distinct features. Simulations on a low-speed (incompressible) three stage axial compressor allows for a validation with experimental data, which shows that the proposed methodology captures the surge behaviour of the system very well both qualitatively and quantitatively. This comparison also highlights the strong dependence of the surge cycle frequency on the volume of the downstream plenum (combustion chamber). Subsequently, the addition of a low-speed centrifugal compressor to the previous compressor is used to demonstrate the adaptability of the approach to a multi-stage axial-centrifugal configuration, yielding qualitatively realistic surge results. Finally, application of the method to an industrial transonic compressor geometry demonstrates the tool on a mixed flow-centrifugal compressor configuration operating in a highly compressible flow regime. A comparison of predicted versus measured shaft loading amplitude during surge is highly promising.

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: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.278
Teacher spread0.228 · 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

Citations19
Published2015
Admission routes2
Has abstractyes

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