Algorithm for the treatment of boundary conditions in NMM-SPH coupling models: Interface element-wise boundary particle scheme
Bibliographic record
Abstract
Large deformation and complex fluid flow interactions, known as the fluid–structure interaction (FSI), are common in geotechnical and coastal projects. To address FSI problems involving crack development, block contact and large movement, a numerical model that utilises smoothed particle hydrodynamics for fluid field spatial discretisation and numerical manifold method for solid block domain solution is proposed in the present study. Specifically, to restore the particle inconsistency near the interface and the overflow issue around the sharp corner in traditional coupled methods, a recommended boundary particle approach is designed to provide full support for particles near the interface, regardless of the interface’s geometry. Moreover, this approach enables the choice of different spatial resolutions tailored to requirements of solid and fluid domains. Six numerical examples were conducted to verify the reliability, accuracy and robustness of the new method. Results showed that relative errors around the sharp corner are lower than those in previous studies. Furthermore, the new model performs well in dealing with the motion of blocks with arbitrary shapes, including large movements, block contacts, crack propagations , and large deformations. Additionally, various inner-variable field evolutions between fluids and solids were visualised, which is beneficial for the prediction of multi-hazard scenario outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".