Investigation of Numerical Modelling Techniques for Predicting Highly Nonlinear Extreme Waves in Shallow and Deep Water
Bibliographic record
Abstract
This paper aims to investigate the accuracy and computational efficiency of three CFD-based numerical codes to accurately simulate extremely large regular and irregular waves of different steepness in deep and shallow water conditions. The work assesses the performance of three numerical techniques with different formulations of the fluid dynamic equations. Firstly, an open-sourced smoothed particle hydrodynamics (SPH) code; secondly, a finite difference method (FDM) based 3D numerical model with the assumption of inviscid and incompressible fluid flow; and thirdly, a commercial CFD code that uses a finite volume method (FVM) to solve the Reynolds-averaged Navier-Stokes (RANS) equations. A suite of metrics and methodologies, considering three key performance parameters: accuracy, computational requirements and available features for providing a consistent framework for the quantitative assessment of different techniques, has been presented. Numerically simulated free surface elevations, wave periods, and spectrum (for irregular waves only) are compared with experimental data previously acquired at an Offshore Engineering Basin (OEB) facility. Extensive convergence studies were carried out for each numerical tool for a selected large wave before predictions were model for all waves. All three models reproduced waves with an accuracy comparable to physical wave makers in the wave basin experiments for the deep-water regular and irregular waves; however, the SPH model performed better than the other two models for the shallow water waves. The challenge remains for wave basins to reduce unwanted basin effects and numerical facilities to accurately model waves with proper account for boundary effects and numerical diffusions. In addition, only flat-bottom domains were considered in the investigation, leaving the wave modelling for uneven bottom for future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".