Nonlinear wave modelling over variable water depth using extended boussinesq equations
Bibliographic record
Abstract
Numerical modeling of wave-ship interaction in shallow water over variable depth requires an accurate description of diffraction, refraction, reflection, and nonlinear wave-wave interaction. A computer program has been developed to solve time dependent Boussinesq-type hyperbolic long wave equations. The velocity at an arbitrary depth is expanded into an infinite series for the formulation of the extended Boussinesq equations. The numerical stability and dispersion characteristics are improved for increasing water depths. The partial differential equations are solved by using a fifth-order Adams-Bashforth-Moulton time marching multistep finite difference method. The results are compared with a second-order Crank Nicolson finite difference method and a Galerkin finite element method from previously published results. Results for linear and nonlinear waves are also compared with analytical and experimental data. The program will be integrated with a time-domain seakeeping program to simulate wave-ship interaction in coast al waves. The current research contributes higher order time and space discretizations, and generalizes the numerical algorithm for methods of any order given the coefficients for finite difference equations. The research allows for higher-order Boussinesq equations while minimizing the numerical error from the time and space differential approximations.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".