2012
DOI: 10.1016/j.ocemod.2012.05.003
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Fourier pseudospectral methods for 2D Boussinesq-type equations

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Cited by 9 publications
(7 citation statements)
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“…In the present study the BR flux was avoided given its sub-optimal convergence rates (Duran and Marche, 2015). Among the other options, which can deliver optimal convergence rates (Kirby and Karniadakis, 2005;Steinmoeller et al, 2012Steinmoeller et al, , 2016, the LDG flux is chosen in this work and can be obtained by setting and (Cockburn and Shu, 1998).…”
Section: Dispersive Terms Computationmentioning
confidence: 99%
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“…In the present study the BR flux was avoided given its sub-optimal convergence rates (Duran and Marche, 2015). Among the other options, which can deliver optimal convergence rates (Kirby and Karniadakis, 2005;Steinmoeller et al, 2012Steinmoeller et al, , 2016, the LDG flux is chosen in this work and can be obtained by setting and (Cockburn and Shu, 1998).…”
Section: Dispersive Terms Computationmentioning
confidence: 99%
“…Following the work in Steinmoeller et al (2012), this quantity is normalized by its initial value and then recorded over time for two of the meshes (i.e. with 80 and 160 cells)…”
Section: Oscillatory Flow In a Parabolic Bowlmentioning
confidence: 99%
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“…Most of the BT equations impose a smallness amplitude assumption as = O( 2 ), which is too restrictive for many applications in nearshore areas. 7,[11][12][13][14][15][16][17][18][19] However, the runtime cost of DG methods is high, given their demands for storage and evolution of local degrees of freedom within each computational cell and their restrictive CFL condition when applied with explicit Runge-Kutta (RK) time stepping. [3][4][5] The GN equations share the same characteristics of other BT models.…”
Section: Introductionmentioning
confidence: 99%
“…9-11 DG methods are becoming increasingly popular in solving BT equations. 7,[11][12][13][14][15][16][17][18][19] However, the runtime cost of DG methods is high, given their demands for storage and evolution of local degrees of freedom within each computational cell and their restrictive CFL condition when applied with explicit Runge-Kutta (RK) time stepping. These costs would even be higher when modeling wave propagation and transformation in coastal areas, where the multitude of spatial and temporal scales further increase the wave feature and complexity.Classical adaptive mesh refinement (AMR) techniques were initially used in an attempt to reduce fine resolution costs by adapting the mesh resolution.…”
mentioning
confidence: 99%