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Investigation of Water Hammer Overpressure in the Hydraulic Passages of Hydropower Plants Equipped with Francis Turbines

2022· article· en· W4306175592 on OpenAlexaffabout
S-I Lupa, M Gagnon, Sebastian Muntean, Georges Abdul-Nour

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2022
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversité du Québec à Trois-RivièresHydro-Québec
Fundersnot available
KeywordsOverpressureWater hammerHydropowerHammerExperimental dataMarine engineeringGeotechnical engineeringTransient (computer programming)Environmental scienceComputer scienceEngineeringStructural engineeringMechanical engineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract Water hammer shortens hydraulic passage lifespan and may cause sudden failure. The primary goal is to use a hierarchical approach to assess the main parameters associated with water hammer. This will help investigate their influence and assist in decision making. Analytical calculation results and a numerical model are compared against experimental data. Our investigations examine water hammer overpressure loading induced by transient regimes. We used data from experimental campaigns carried out within the Hydro-Québec fleet that cover different types of hydraulic turbines and hydraulic passage configurations as experimental dataset. Guide vane closing rate was the main parameter controlled during the overpressure experiments and a general trend was identified for overpressure. This empirical trend is compared to model estimates in order to validate the hypothesis taken into account for calculations. An in-depth understanding of the water hammer phenomenon helps to select the appropriate theoretical model and recommend the optimal operating parameters to extend lifetime and to avoid catastrophic failures. Our study case suggests that available experimental data can be used along with gradually increasing analysis complexity to identify the optimal methodology for a given configuration.

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 score0.162
Threshold uncertainty score0.181

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 head0.008
GPT teacher head0.162
Teacher spread0.154 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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