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Record W2965775619

Physical Modelling of an Hydropower Generation Station and Simulating Turbine Energy Losses

2018· article· en· W2965775619 on OpenAlexaboutno aff
Li, Fok

Bibliographic record

VenueWDSA / CCWI Joint Conference Proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
Fundersnot available
KeywordsHydropowerInflowTurbineInletMarine engineeringSpillwayDraft tubeRenewable energyElectricity generationEnvironmental scienceFrancis turbineEngineeringFlow (mathematics)MeteorologyPower (physics)Geotechnical engineeringMechanical engineeringMechanics
DOInot available

Abstract

fetched live from OpenAlex

potential power generation via the turbine is proportional to the head and flow of water. This paper presents a physical model study of a hydropower expansion project including the construction of the inlet, a multi-orifice plate and multi-tubes to simulate the energy losses via the turbines, and the downstream tailrace. A 90-year old run-of-the-river hydropower generation station of 90 m3/s is currently undergoing expansion to a full maximum of 172 m3/s by adding an extra turbine of 80 m3/s, a submerged bypass spillway tunnel and an expanded intake. The expanded intake will require rock excavation to divert inflow to the new powerhouse, resulting in change of flow direction twice (more than 180°). With the complex inflow conditions, there are serious concern over damaging vortices and reduction of turbine efficiency. This project is part of the Ontario Power Generation (OPG) renewable energy initiatives. A 1:25 scale model (14 m by 4 m by 1.5 m) was built at Queen’s University, Ontario with an objective to investigate the complex hydraulic conditions at the hydropower station intake and provide data for calibration of the hydrodynamic design model. The physical model results suggest that extra precaution be taken to design the inlet to reduce the flow vortices.

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 score0.735
Threshold uncertainty score0.775

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 communication0.0000.001
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 head0.039
GPT teacher head0.243
Teacher spread0.205 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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