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Record W1965022853 · doi:10.1115/ijpgc2002-26101

Power Plant Life/Risk Assessment Techniques

2002· article· en· W1965022853 on OpenAlexaffabout
M. Clark, R. J. Browne, M. T. Flaman, E. M. Lehockey, Ian Thompson

Bibliographic record

Venue2002 International Joint Power Generation Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsPipingCreepMetallographyReliability engineeringCharacterization (materials science)Computer scienceForensic engineeringEnvironmental scienceMechanical engineeringMaterials scienceEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Many fossil power plants in N. America are now required to operate in excess of 20 years beyond their original design life. To ensure safe reliable operation, life assessment of key high-risk components is required. Life assessment of older power plant requires the application of many diverse techniques and the integration of several engineering disciplines with plant operations experience. Traditional approaches to life assessment of high temperature components will be described and illustrated with examples taken from experience on large coal fired plants from the old Ontario Hydro system. These results will include a number of high energy piping welds with approximately 160,000 hours of service that contain intercritical region heat affected zone (HAZ) creep damage (Type IV). These welds have been monitored through in-situ metallography techniques for over 20,000 hours since damage was first detected. New techniques are also being developed or employed: miniature sample testing for evaluating creep and fracture properties along with miniature EDM sample removal techniques. The use of innovative mechanical scratch gauges for the purpose on-line strain monitoring of mechanical or civil structures. The use of orientation imaging microscopy (OIM) for simultaneous comprehensive characterization of the interaction between residual plastic strains, texture, grains size distribution and other crystallographic features as applied to root cause failure analysis. Finally risk based asset management software RBMS for the purpose of detailed component assessments. Examples/results of these techniques will also be presented in the paper.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0090.000

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 head0.041
GPT teacher head0.255
Teacher spread0.214 · 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

Citations2
Published2002
Admission routes2
Has abstractyes

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