MétaCan
Menu
Back to cohort
Record W2175040079 · doi:10.1115/gt2005-68729

Engine Performance Improvement by Controlling the Low Pressure Compressor Working Line

2005· article· en· W2175040079 on OpenAlexaff
Dan Martis, Arthur Rowe, Marie-Laure Pinquier, Brian Price, Nicholas J. Papaconstantinou, Darryl Edwards, Vincent Perez, Samer Mhanna

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsRolls-Royce (Canada)
Fundersnot available
KeywordsGas compressorAutomotive engineeringOverall pressure ratioLine (geometry)Power (physics)Reliability (semiconductor)Controller (irrigation)Computer scienceAir compressorControl theory (sociology)EngineeringMechanical engineeringControl (management)

Abstract

fetched live from OpenAlex

The new algorithm provides closed-loop control of the LP Compressor working line in such a way as to maintain compressor stability and to provide increased power from the engine at the same firing temperature. This method is intended for the Trent 60, an aero-derivative engine designated for power generation and mechanical drive applications. The power benefit is achieved by operating at higher LPC pressure ratio and thereby increasing the inlet air flow and overall pressure ratio of the engine. Since the algorithm controls the working line, the threats to compressor stability related to the working line level are removed (including production scatter, deterioration and fouling) and the required surge margin can be safely reduced, providing a significant benefit in engine performance. The paper presents comparatively the structure of the current and new concepts, the main features of the controller and stresses the improved accuracy and reliability of new algorithms. The performance benefit is then assessed; the increase in power is about 3% at ISO, sea level conditions and varies with ambient temperature.

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 score0.314
Threshold uncertainty score0.297

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 head0.183
Teacher spread0.177 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207