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Durability Assessment of Francis Turbine Spiral Casings

2025· article· W7108070211 on OpenAlexaff

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2025
Typearticle
Language
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsHydro-QuébecUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsDurabilityService lifeCasingFrancis turbineTransient (computer programming)Spiral (railway)Hydroelectricity

Abstract

fetched live from OpenAlex

Abstract The hydraulic transients associated with flexible operation network balancing tend to induce cyclic overpressure loads on the hydraulic components of the passages. Stresses generated by these have a direct impact on the asset durability and can even lead to catastrophic structural failures. The objective of this study is to analyze the cyclic loads for different hydroelectric generator operating scenarios and to assess their impact on the service life of the spiral casing. First, we look at three different operating sequences: (1) load rejection; (2) start/stop sequence; (3) regulation in transient zones. Experimental data were used to define the parameters for each type of cyclic load. The analytical relationship between cyclic stresses and the durability of a Francis spiral casing was studied using the ASME S-N methodology to assess the impact of cyclic loading on service life reduction. Second, for three representative operating modes: (1) normal operation; (2) intensive operation; (3) peaking, the overall impact of the generated loads on the durability of the spiral casing was established and a comparative analysis was performed. We observe that the most damaging loads are induced by regulation in transient zones, followed by start/stop sequences. Regarding the operating modes, our results reveal that the peaking has the greatest impact on service lifetime, followed by intensive operation. Given these, we propose solutions to improve operating policies and maintenance strategies, based on the initial unit design and the operating mode considered.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
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 studies0.0000.002
Scholarly communication0.0000.001
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 head0.204
Teacher spread0.196 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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