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Record W2324482630 · doi:10.1115/gt2007-28100

Enhancements to the Load Acceptance and Rejection Capability of a High Pressure Aeroderivative Engine

2007· article· en· W2324482630 on OpenAlexaff
Brian Price, Louis Demers, Jean-Francois Lebel, Sylvain Bonneville

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsRolls-Royce (Canada)
Fundersnot available
KeywordsInertiaRange (aeronautics)TurbineAutomotive engineeringSoftwareComputer sciencePower (physics)Gas turbinesEngineeringControl engineeringSimulationMechanical engineering

Abstract

fetched live from OpenAlex

This paper describes improvements to the control of a high pressure, aeroderivative industrial gas turbine in order to better accommodate rapid load changes. In such circumstances it is important to maintain the speed of the driven equipment within an acceptable range. This can require the gas turbine to quickly adjust to the new load, to minimize the power imbalance, which is the cause of the speed variation. The paper describes the theory behind control schedules required to achieve this, and how they relate to avoiding surge, flameout or instability, while minimizing speed variations of the driven equipment. A whole engine thermodynamic model coupled to the control software was used to simulate the engine response during these rapid transients. The features of this model are described. The model allowed optimization of the control software in advance of the engine test. Results of whole engine tests are presented and compared to the model; and the types of load steps that remain most challenging are highlighted. The resulting capability remains partly determined by the specifics of the application, for example the inertia of the driven equipment, the nominal speed of operation, and the allowable speed variations. The effects of these can be predicted using the model and are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.200

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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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