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Generation of Supersaturated Total Dissolved Gases from Low-Level Outlets at Hydropower Facilities

2022· article· en· W4298012246 on OpenAlexaffabout
Pengcheng Li, David Z. Zhu, James A. Crossman, Alf Leake, Ran Li

Bibliographic record

VenueJournal of Hydraulic Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsBC Hydro (Canada)University of Alberta
Fundersnot available
KeywordsEntrainment (biomusicology)SupersaturationEnvironmental scienceAir entrainmentTurbulenceBubbleHydropowerMass transferEnvironmental engineeringHydrology (agriculture)MeteorologyGeologyMechanicsGeotechnical engineeringChemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

Supersaturated total dissolved gases (TDGs) generated by dams can cause gas bubble trauma and mortality in fish in downstream waters. In this study, we evaluated air entrainment and generation of supersaturated TDG at two submerged low-level outlets (LLOs) at Hugh L. Keenleyside Dam, British Columbia, Canada. Specifically, we determined how air entrainment (less than 1% volume fraction) through the gate well, discharge level through the outlets, and geometry of the stilling basins at the south and north ends of the dam influenced supersaturated TDG generation. A mathematical formulation was developed, incorporating physical processes including air entrainment, bubble breakup, and gas transfer. Numerical modeling was also adopted to validate turbulence and flowfield downstream of the submerged low-level outlets. Despite being 8-m shallower, significantly higher TDG levels were measured in the stilling basin of the south LLO (about 120%) compared to that of the north LLO (≤110%). Results show that turbulence in the stilling basin can produce smaller bubbles and increase the mass transfer coefficient across bubbles, which will substantially enhance gas transfer and TDG generation. Higher TDG generated in the shallower LLO was therefore attributed to the water depth generating stronger turbulence flow, with more efficient gas transfer. This study improves TDG prediction and helps inform the development of operational alternatives during periods of high TDG generation to mitigate impacts on the aquatic environment.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.737

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 head0.018
GPT teacher head0.195
Teacher spread0.177 · 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 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

Citations6
Published2022
Admission routes2
Has abstractyes

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