Rolling table centrifuge modelling of partially saturated granular material to inform on instability during solid bulk cargo transport
Bibliographic record
Abstract
Abstract The response of partially saturated granular cargoes during maritime transportation has resulted in the capsize and sinking of 27 bulk carriers at sea and the loss over 90 seafarers’ lives in the last decade. The partially saturated granular material response to energy imparted during cargo loading, ship engine vibrations and vessel rolling motions from sea states causes a change in state of the granular cargo, which can lead to vessel instability and ultimately capsize. However, the mechanisms driving the response of partially saturated granular cargos within a bulk carrier hold are not well understood. This paper presents results from an experimental study of rolling table centrifuge model tests on a partially saturated silica sand to contribute to improved understanding of the response of granular cargoes during maritime transport. Observed settlement, pore pressure, moisture content and density changes during and/or following a sequence of large amplitude rolling motions are presented. The results indicate that for the conditions considered, a progressive upwards migration of pore water during rolling led to creation of a free surface of water above the granular sample that was left in a denser, lower moisture content sample compared to its initial state. Sloshing of free water on top of even a competent cargo during rolling motions of the ship can contribute to loss of ship stability and ultimately capsize.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".