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Record W2034951370 · doi:10.1115/gt2009-60352

Residual Life Assessment and Life Cycle Management of Design Life Expired Discs

2009· article· en· W2034951370 on OpenAlexaff
Ashok K. Koul, Ajay Tiku, Karan Khullar, Jun Zhao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsLife Prediction Technologies (Canada)
Fundersnot available
KeywordsPrognosticsReliability (semiconductor)ResidualTurbineService lifeReliability engineeringAutomotive engineeringReplicaEngineeringFrame (networking)Sensitivity (control systems)Computer scienceMechanical engineering

Abstract

fetched live from OpenAlex

The results of a prognostics case study on GE Frame 5001P first stage turbine disc are presented in this paper. Currently used and promoted practices for metallurgical analysis such as hardness testing and replica based microstructural assessment and inspection of rotors for dimensional checks and cracks are not sufficient to ensure safety and reliability of the engine. The uncertainly of all engine variables including operational environment must be considered prior to returning the engine to service. It is required to accurately predict the temperature profile of the discs that can have serious consequences on the residual life assessment of the fracture prone rotors. The safe inspection interval (SII) determination of the design life expired engines and defining non-destructive inspection (NDI) sensitivity requirements for continued safe operation of the engine are equally important.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2009
Admission routes1
Has abstractyes

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