Towards rational use of baffle arrays on sloped and horizontal terrain for filtering boulders
Bibliographic record
Abstract
Baffle arrays are used to filter boulders from granular flows, such that the impact load exerted on barriers is reduced. However, current guidelines provide limited recommendations on baffle design. In this study, a calibrated discrete element method model was used to model boulders entrained in a bulk granular assembly interacting with baffles and a terminal rigid barrier. Different baffle spacings relative to the boulder diameter (1 < s/δ < 4) were considered. A ratio of s/δ = 1 is recommended for reducing the impact load by up to 80%, whilst s/δ = 4 renders an array of baffles inadequate for filtration. The optimum configuration is a staggered array with three rows of baffles on a horizontal plane in front of a barrier. This layout reduces the peak discharge by up to four times more than a similar array on sloping terrain, compared to channels without baffles. Furthermore, the transition from sloping terrain to a horizontal plane works together with the array of baffles to dissipate flow kinetic energy. On the horizontal plane, baffles attenuate the flow velocity more as the Froude number Fr increases, implying that baffles should be used if high Fr values are anticipated. Finally, guidance is provided on estimating load attenuation from boulder filtration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".