We use the general framework of summation by parts operators to construct conservative, entropy-stable and well-balanced semidiscretizations of two different nonlinear systems of dispersive shallow water equations with varying bathymetry: (i) a variant of the coupled Benjamin-Bona-Mahony (BBM) equations and (ii) a recently proposed model by Svärd and Kalisch (2023) with enhanced dispersive behavior. Both models share the property of being conservative in terms of a nonlinear invariant, often interpreted as entropy function. This property is preserved exactly in our novel semidiscretizations. To obtain fully-discrete entropy-stable schemes, we employ the relaxation method. We present improved numerical properties of our schemes in some test cases.
The reproducibility repository is available on GitHub.
]]>The numerical simulation of tsunami propagation is often based on the classical shallow water equations. However, there are several regimes where the assumptions used to derive this model are not satisfied. In this case, higher-order effects need to be taken into account, leading to nonlinear dispersive wave equations. Several variants of such models exist and are used in practice. In this talk, we will review some recent developments of structure-preserving numerical methods. In particular, we will consider invariants such as the total energy and study efficient numerical methods yielding qualitative and quantitative improvements compared to standard schemes. The numerical methods will use the framework of the method of lines. Thus, we will discuss both spatial semidiscretizations and time integration methods. To develop structure-preserving schemes, we make use of the general framework of summation-by-parts (SBP) operators in space, unifying the analysis of finite difference, finite volume, finite element, discontinuous Galerkin, and spectral methods. Finally, we combine structure-preserving spatial discretizations with relaxation methods in time to obtain fully-discrete, energy-conservative schemes.
]]>Please find more information about the meetings on our website.
As a related event, we will host a talk by Valentin Churavy on December 21, as announced online.
]]>]]>Modified Patankar-Runge-Kutta (MPRK) methods are linearly implicit time integration schemes developed to preserve positivity and a linear invariant such as the total mass in chemical reactions. MPRK methods are naturally equipped with embedded schemes yielding a local error estimate similar to Runge-Kutta pairs. To design good time step size controllers using these error estimates, we propose to use Bayesian optimization. In particular, we design a novel objective function that captures important properties such as tolerance convergence and computational stability. We apply our new approach to several MPRK schemes and controllers based on digital signal processing, extending classical PI and PID controllers. We demonstrate that the optimization process yields controllers that are at least as good as the best controllers chosen from a wide range of suggestions available for classical explicit and implicit time integration methods.
Many time-dependent differential equations are equipped with invariants. Preserving such invariants under discretization can be important, e.g., to improve the qualitative and quantitative properties of numerical solutions. Recently, relaxation methods have been proposed as small modifications of standard time integration schemes guaranteeing the correct evolution of functionals of the solution. Here, we investigate how to combine these relaxation techniques with efficient step size control mechanisms based on local error estimates for explicit Runge-Kutta methods. We demonstrate our results in several numerical experiments including ordinary and partial differential equations.
The reproducibility repository is available on GitHub.
]]>High-order methods for conservation laws can be very efficient, in particular on modern hardware. However, it can be challenging to guarantee their stability and robustness, especially for under-resolved flows. A typical approach is to combine a well-working baseline scheme with additional techniques to ensure invariant domain preservation. To obtain good results without too much dissipation, it is important to develop suitable baseline methods. In this article, we study upwind summation-by-parts operators, which have been used mostly for linear problems so far. These operators come with some built-in dissipation everywhere, not only at element interfaces as typical in discontinuous Galerkin methods. At the same time, this dissipation does not introduce additional parameters. We discuss the relation of high-order upwind summation-by-parts methods to flux vector splitting schemes and investigate their local linear/energy stability. Finally, we present some numerical examples for shock-free flows of the compressible Euler equations.
The reproducibility repository is available on GitHub.
]]>We combine the recent relaxation approach with multiderivative Runge-Kutta methods to preserve conservation or dissipation of entropy functionals for ordinary and partial differential equations. Relaxation methods are minor modifications of explicit and implicit schemes, requiring only the solution of a single scalar equation per time step in addition to the baseline scheme. We demonstrate the robustness of the resulting methods for a range of test problems including the 3D compressible Euler equations. In particular, we point out improved error growth rates for certain entropy-conservative problems including nonlinear dispersive wave equations.
The reproducibility repository is available on GitHub.
]]>See http://www.simtech-summerschool.de and https://www.simtech.uni-stuttgart.de/events/simtech-summer-school/SuSch_2 for more information.
]]>Many modern discontinuous Galerkin (DG) methods for conservation laws make use of summation by parts operators and flux differencing to achieve kinetic energy preservation or entropy stability. While these techniques increase the robustness of DG methods significantly, they are also computationally more demanding than standard weak form nodal DG methods. We present several implementation techniques to improve the efficiency of flux differencing DG methods that use tensor product quadrilateral or hexahedral elements, in 2D or 3D respectively. Focus is mostly given to CPUs and DG methods for the compressible Euler equations, although these techniques are generally also useful for GPU computing and other physical systems including the compressible Navier-Stokes and magnetohydrodynamics equations. We present results using two open source codes, Trixi.jl written in Julia and FLUXO written in Fortran, to demonstrate that our proposed implementation techniques are applicable to different code bases and programming languages.
The reproducibility repository is available on GitHub.
As usual, you can find the preprint on arXiv.
]]>We present some recent developments for the numerical simulation of transport-dominated problems such as compressible fluid flows and nonlinear dispersive wave equations. Starting from entropy-stable semidiscretizations, we describe recent developments of structure-preserving time integration methods based on relaxation. We also consider adaptive discretizations and provide an outlook to some recent developments.
]]>