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Record W2008336140 · doi:10.1115/gt2006-91088

Comparison of Transient Modeling Techniques for a Micro Turbine Engine

2006· article· en· W2008336140 on OpenAlexafffund
Craig R. Davison, A. M. Birk

Bibliographic record

VenueVolume 5: Marine; Microturbines and Small Turbomachinery; Oil and Gas Applications; Structures and Dynamics, Parts A and B · 2006
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCombustorTransient (computer programming)Gas compressorMechanicsTurbineTurbojetMaterials scienceMechanical engineeringEngineeringPhysicsComputer scienceCombustionChemistry

Abstract

fetched live from OpenAlex

A large number of papers have been published on transient modeling of large industrial and military gas turbines. Few, however, have examined micro turbines. The decrease in size affects the relative rates of change of shaft speed, gas dynamics and heat soak. This paper compares the modeled transient effects of a micro turbojet engine comprised of a single stage of radial compression and a single stage of axial expansion, with a diameter of 12cm. The model was validated with experimental data. Several forms of the model were produced starting with the shaft and fuel transients. Conservation of mass, and then energy, was subsequently added for the compressor, combustor and turbine, and a large inlet plenum that was part of the experimental apparatus. Heat soak to the engine body was incorporated into both the shaft and energy models. Heat soak was considered in the compressor, combustor and turbine. Since the engine diameter appears in the differential equations to different powers, the relative rates of change vary with diameter. The rate of change of shaft speed is very strongly influenced. The responses of the different transient effects are compared. The relative solution times are also discussed, since the relative size of the required time steps changes when compared to a large engine.

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 categoriesMeta-epidemiology (narrow)
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 score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.212
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.

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

Citations9
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueVolume 5: Marine; Microturbines and Small Turbomachinery; Oil and Gas Applications; Structures and Dynamics, Parts A and BSame topicTurbomachinery Performance and OptimizationFrench-language works237,207