The linear hybrid Boussinesq-SaintVenant model is well-posed and produces O(h^2/L^2) reflections - Inria - Institut national de recherche en sciences et technologies du numérique
Poster Année : 2024

The linear hybrid Boussinesq-SaintVenant model is well-posed and produces O(h^2/L^2) reflections

Résumé

BACKGROUND: Instead of a 3d model, the hybrid model represents wave shoaling, breaking and runup by coupling Boussinesq type and Saint-Venant models. It has gained wide adoption: It simply switches-off/on dispersive terms but it has three problems: - Oscillations - Mesh convergence (lack of) - Instabilities IS THE HYBRID TRULY RELIABLE? IS THE HYBRID'S PDE WORTH SOLVING? METHODS 1. Through Laplace transform, the transmission conditions implicit in the hybrid model can be identified: Dirichlet + Neumann. 2. An iterative method (Schwarz Waveform Relaxation, SWRM) is used to prove the well-posedness by decomposing the domain into two coupled models with transmission conditions and iterating on the traces of transmission conditions. 3. If the initial condition is supported in one side of the interface, then the coupling error can be described as the difference between the hybrid and the one-way coupling, controlled by a “reflection coefficient” RESULTS - The SWRM converges in 3 iterations, so the problem is well-posed: YES. - The coupling error solves a Cauchy problem with a filtered-reflected initial condition - The SV to B case is always worse due to the larger reflection coefficient and the greater phase-speed
Fichier principal
Vignette du fichier
Galaz-A0-SVBTcoupling.pdf (1.51 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04612106 , version 1 (14-06-2024)

Licence

Identifiants

  • HAL Id : hal-04612106 , version 1

Citer

José Galaz, Maria Kazolea, Antoine Rousseau. The linear hybrid Boussinesq-SaintVenant model is well-posed and produces O(h^2/L^2) reflections. PhD day 2024, Mar 2024, Montpellier, France. ⟨hal-04612106⟩
40 Consultations
20 Téléchargements

Partager

More