Smoothed Particle Hydrodynamics Modelling of Bergy Bit and Offshore Structure Interactions Due to Large Waves
Bibliographic record
Abstract
This research utilised an open-sourced smoothed particle hydrodynamics (SPH) tool to model and predict the change in wave-induced forces and motions of a free-floating bergy bits approaching a fixed structure in regular waves. Simulation parameters, including particle resolution, fluid viscosity, initial wave condition and boundary treatments, are varied, and their effect on the load imparted to the bergy bit and the structure are investigated. The predicted motions are compared with previously published physical measurements for corresponding scenarios. Both predictions and measurements showed that, in regular waves, the surge motion slowed as the bergy bit approached the structure, and the heave motion increased. For wave loading on bergy bits, the agreement with the experimental data for the root mean square (RMS) force was within 2%. The pressure and velocity fields of the wave–structure–bergy bit interactions are discussed in light of the SPH predictions. This work confirms that the SPH model can accurately capture viscosity–dominated interactions, hydrodynamic damping, and eccentric impact like phenomena and predict both the impact and hydrodynamic loads due to a bergy bit drifting in waves towards a fixed offshore structure.
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 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".