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Record W7002238849

Mitigating the Drowning Hazard Below Weirs Using Submerged Vanes

2022· dissertation· en· W7002238849 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicReligion and Sociopolitical Dynamics in Nigeria
Canadian institutionsnot available
Fundersnot available
KeywordsWeirHydraulic jumpFlumeHydraulicsFlow (mathematics)HazardInstallationJump
DOInot available

Abstract

fetched live from OpenAlex

In recent decades, public safety at weirs has become more and more of a concern for the owners and authorities responsible for such structures. The hazard at weirs is due to the type of flow called a submerged hydraulic jump, which has been the cause of numerous drownings, thus leading to the nickname “drowning machine” for such structures. A submerged hydraulic jump consists of a large horizontal vortex, or roller, that creates a strong backward current that can pull people and objects back towards the weir and keep them entrapped there indefinitely. Engineers have designed a number of different structural modifications to weirs in order to mitigate the submerged hydraulic jump. In 2019, laboratory flume studies at the University of Saskatchewan showed that by installing an array of angled flat rectangular plates, or submerged vanes, downstream of a weir, it was possible to eliminate the submerged hydraulic jump and create a new flow pattern that consistently carried floating bodies to one wall of the flume. Such a result was promising, since in theory, it meant that someone trapped below a weir could be safely brought to the bank by the flow. However, the range of conditions for which the vanes would be effective was not known. The goal of this research was broadly to investigate the use of submerged vanes below a weir to mitigate the submerged hydraulic jump and therefore the drowning hazard. This was accomplished through the use of a weir and vanes set within a rectangular flume, and the vanes’ effectiveness in conveying an object to the bank was tested by inserting objects into the flow: a spherical ball, and a scaled-down model of a human body, called a test-body. Several parameters were varied throughout different tests: the flow rate, the tailwater depth, the angle at which the vanes were set, the lateral spacing between the vanes, and the distance of the vanes from the weir face. The vanes did bring the test-body toward the channel wall under most conditions. The vanes were effective at tailwater depths just deeper than the vanes up to approximately the height of the weir, but somewhat less consistently so at the highest end of the tailwater range. The higher the vane angle, the more consistent conveyance toward the wall, but this also led to a visibly rougher water surface and more splashing against the channel wall. Vane iii angles, relative to the mean flow direction, of 10° or greater were usable; a vane angle of 30° was thought best due to the consistency in conveyance toward the wall, but with less splashing than higher angles. An array of three vanes, with spacing equal to the vane length, was preferable over two or four vanes, which respectively resulted in less consistent conveyance toward the channel wall, or greater likelihood for the test-body to hit the vanes, without any consistent improvement in conveyance. At a flow rate in the flume of 25 L/s, the test-body quite often hit or became caught on one or more of the vanes, but rarely at 75 L/s. It was believed that the difference in position of the weir’s overflowing nappe in relation to the vanes was a major factor in why the test-body more often hit the vanes at 25 L/s compared to 75 L/s. The first tested position of the vanes was the most effective, where at 75 L/s the nappe intersected somewhere through the middle of the vanes.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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