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Record W2468132608 · doi:10.1139/cjce-2015-0382

Characterization of surge superposition following 2-stage load rejection in hydroelectric power plant

2016· article· en· W2468132608 on OpenAlexvenueno aff
Sheng Chen, Jian Zhang, Xichen Wang

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsSurgeSuperposition principleHydroelectricitySurge tankStage (stratigraphy)Load rejectionEnvironmental scienceControl theory (sociology)MathematicsEngineeringComputer scienceGeologyElectrical engineeringMechanical engineeringMathematical analysis

Abstract

fetched live from OpenAlex

When calculating the maximum upsurge in surge tank due to load rejection in a hydroelectric power plant, it has been natural and customary to believe that the maximum surge amplitude occurs in simultaneous load rejection of all units at 100% load. As 2-stage load rejection (2-stage LR), involving a step-wise reduction in load, is not considered since it is assumed to produce less severe surge conditions. This study formulates the surge superposition associated with 2-stage LR and shows, surprisingly but significantly, that such 2-stage LR sometimes produces more severe surge conditions than simultaneous and complete load rejection (SCLR). The results indicate that this unexpected phenomenon is ascribable to the resistant effect of throttled surge tank, whose increase will lead to a greater difference in the maximum upsurges between 2-stage LR and SCLR conditions. Different time intervals during 2-stage LR correspond to different maximum upsurges. The analytical formula predicting the worst interval time is derived exactly and verified with two numerical cases.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.004
GPT teacher head0.145
Teacher spread0.142 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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