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Record W2782593635 · doi:10.1139/cjce-2017-0515

Tailrace surge shaft optimization procedure for minimum downsurge

2018· article· en· W2782593635 on OpenAlexvenueno aff
Satyajeet Sinha

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSurgeSurge tankTailwaterHydropowerMarine engineeringTurbineTransient (computer programming)EngineeringWater hammerStorm surgeEnvironmental scienceGeotechnical engineeringGeologyElectrical engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Tailrace surge shafts are required in hydropower projects where the spent water is conveyed through a long tailrace tunnel under pressure to the recipient. Startup and shutdown of the turbine can cause sudden changes in water velocity and can develop dangerously high and low pressures. Surge shafts are provided in water conductor systems to significantly reduce these pressure surges. Based on numerous transient analyses carried out, it was observed that the cross-sectional area of the tailrace surge shaft can be optimized based on the relationship between the differences in the tailwater level and the minimum downsurge level at the tailrace surge shaft obtained with respect to the different lengths of the tailrace tunnel and different cross-sectional areas of the tailrace surge shaft. In this study, a procedure was proposed by which the tailrace surge shafts can be optimized and, hence, the cost of the hydropower projects with tailrace surge shafts can be minimized.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.174
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2018
Admission routes1
Has abstractyes

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