Investigating boom performance: an experimental study of load dynamics and freeboard influence in highly energetic flows under ice-free conditions
Bibliographic record
Abstract
AbstractFloating retention structures, also known as booms, are crucial protective infrastructure in rivers and narrow water bodies. They serve various purposes, including controlling the movement of ice and debris, reducing the risk of ice jams, expediting the formation of a stable ice cover, and acting as safety barriers. This research was motivated by the challenge of employing booms in highly energetic flows, a relatively unexplored area in the existing body of research. To address this gap, small-scale experiments were conducted to assess the behavior of booms in dynamic conditions. In these experiments, booms were represented by single cylinders constructedfrom Polyvinyl Chloride (PVC) pipes. A load cell was employed to record the dynamic load exerted on the boom over time. This study emphasizes the load across a wide range of Froude numbers, spanning from low Froude numbers to higher values, ultimately reaching total submergence of the boom. These distinct Froude numbers were generated by adjusting the inflow discharge in the experimental flume and adjusting the downstream tailwater control. Additionally, the study examines four booms with varying levels of freeboard to assess the impact of freeboard height, in addition to the Froude number, on the load experienced by the boom. The results of these experiments reveal that the load and its fluctuations increase with rising Froude numbers until the point of total submergence, after which the load decreases slightly. Furthermore, booms with higher freeboards exhibit greater tolerance for higher load magnitudes. Given the increasing frequency of unexpected water body conditions resulting from climate change, the findings of this experimental investigation offer valuable insights for engineers and organizations. This understanding enables them to better comprehend how booms respond under critical hydrodynamic conditions, helping them prepare for the associated risks and hazards in various water flows.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".