NUMERICAL MODELING OF COASTAL DIKE OVERTOPPING USING SPH AND NON-HYDROSTATIC NLSW EQUATIONS
Bibliographic record
Abstract
This paper evaluates the results of two fundamentally different numerical models: DualSPHysics and SWASH, which can be used to assess the ability of coastal defense structures to offset or mitigate the water overtopping and subsequent implications for expected future sea level rise. The models are open source implementations of the smoothed particle hydrodynamics (SPH) method and of a non-hydrostatic adaptation of the non-linear shallow water (NLSW) equations, respectively. The small-scale physical experiment of Stansby and Feng (2004) is used to validate and asses the performance of the two numerical models for the case of breaking monochromatic waves overtopping a coastal dike. Numerical and experimental time-histories of water surface elevation are quantitatively compared and numerical velocity fields during the processes of wave breaking and overtopping are analysed in detail. In addition, to further validate the DualSPHysics model, numerical experiments are performed considering the more realistic case of irregular waves using the SWASH model as benchmark. Overall, results provided by both numerical models are generally comparable, although some strengths and shortcomings of each are highlighted. These results can provide guidance in selecting the most appropriate model for a particular situation given specific accuracy requirements and availability of resources.
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 | 0.001 |
| 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".