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Record W2045950278 · doi:10.1115/2000-gt-0623

Steady State Performance Simulation of Auxiliary Power Unit With Faults for Component Diagnosis

2000· article· en· W2045950278 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVolume 1: Aircraft Engine; Marine; Turbomachinery; Microturbines and Small Turbomachinery · 2000
Typearticle
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsQueen's University
Fundersnot available
KeywordsFault (geology)Steady state (chemistry)Power (physics)Component (thermodynamics)TurbineComputer scienceBaseline (sea)Automotive engineeringControl theory (sociology)SimulationEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

A computer model of an auxiliary power unit has been developed. The model determines the steady state operating condition of the engine under user specified ambient conditions and load. Once the healthy baseline condition is determined faults, such as turbine degradation or flow passage blockage, were introduced and the effect on the engine’s operating parameters noted. Comparing the results to healthy baseline data generates fault signatures and fault maps, that can be used in engine diagnosis. By observing the changes in parameters that can be economically monitored the viability of this simple diagnosis method can be determined.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.189
Teacher spread0.183 · 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