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On the Comparison of Hydroelectric Runner Fatigue Failure Risk Based on Site Measurements

2021· article· en· W3171699106 on OpenAlexaffabout
Olivier Morin, Denis Thibault, Martin Gagnon

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsReliability (semiconductor)TurbineCrackingFatigue testingHydroelectricityStrain gaugeFatigue crackingEnvironmental scienceStructural engineeringMarine engineeringReliability engineeringEngineeringMechanical engineeringMaterials sciencePower (physics)

Abstract

fetched live from OpenAlex

Abstract The fatigue reliability of a turbine runner is closely related to its dynamic behavior. Over the past few years, Hydro-Québec has performed measurement campaigns on many of its turbine runners. These measurements led to the evaluation of the fatigue risk level of each runner in various operating conditions, which allows operating conditions with a higher risk of crack propagation to be avoided. This paper presents the results for turbine dynamic behavior assessed in steady-state conditions. Stress levels at strain gauge locations are used to evaluate the risk of fatigue cracking based on the Kitagawa-Takahashi diagram. Results show a good correlation between the calculated risk of cracking and real cases of cracks found in runners. Furthermore, the results comparison highlights a apparent tendency for recent designs to be more prone to cracking at speed-no-load operating condition than older designs. The paper gives an overview of the methodology used and discusses the conclusions derived from the sample of turbine runners available for this study.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.419

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 head0.022
GPT teacher head0.206
Teacher spread0.184 · 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 designBench or experimental
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

Citations3
Published2021
Admission routes2
Has abstractyes

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