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Record W2006713537 · doi:10.1115/gt2004-53378

Steady State and Transient Modeling of a Micro-Turbine With Comparison to Operating Engine

2004· article· en· W2006713537 on OpenAlexafffund
Craig R. Davison, A. M. Birk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOperating pointTransient (computer programming)TurbineAutomotive engineeringEngine coolant temperature sensorJet engineSteady state (chemistry)ThrustGas compressorEngineeringComputer scienceMechanical engineeringCombustion chamberCombustionElectrical engineering

Abstract

fetched live from OpenAlex

Steady state and transient computer models of a micro turbine were produced. The engine under study was a micro-jet engine that when tested at 126,000 RPM provided 95 N thrust. The aero-thermal model uses generic performance maps for the compressor and turbine which were modified, based on operating data, to represent the components in the engine under study. The model also includes the inlet ducting connected to the engine. It simulates engine operation from idle to full power over the expected operating range of ambient temperature, pressure and humidity. A comparison of steady state model results to actual engine operating data is presented over the full range of speeds. The effect of ambient humidity on the engine operating point is examined for a micro-engine, in particular at temperatures above 30° Celsius. The techniques for introducing component faults are given and their effect on the engine operation is presented. The degraded components are the turbine and inlet flow passages. The methods for modeling the transient behavior of the engine are also presented. Results are presented for both acceleration and deceleration of the engine between steady state operating point. These results are also compared to the operating 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 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 score0.304
Threshold uncertainty score0.289

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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

Citations10
Published2004
Admission routes2
Has abstractyes

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