Decomposition Methods for Differential Equations: Theory and by Juergen Geiser

By Juergen Geiser

Decomposition equipment for Differential Equations: thought and purposes describes the research of numerical equipment for evolution equations in keeping with temporal and spatial decomposition equipment. It covers real-life difficulties, the underlying decomposition and discretization, the steadiness and consistency research of the decomposition tools, and numerical results.

The booklet makes a speciality of the modeling of chosen multi-physics difficulties, prior to introducing decomposition research. It offers time and house discretization, temporal decomposition, and the combo of time and spatial decomposition equipment for parabolic and hyperbolic equations. the writer then applies those easy methods to numerical difficulties, together with try examples and real-world difficulties in actual and engineering functions. For the computational effects, he makes use of a variety of software program instruments, reminiscent of MATLAB®, R3T, WIAS-HiTNIHS, and OPERA-SPLITT.

Exploring iterative operator-splitting equipment, this ebook exhibits find out how to use higher-order discretization tips on how to resolve differential equations. It discusses decomposition equipment and their effectiveness, mixture danger with discretization equipment, multi-scaling probabilities, and balance to preliminary and boundary values problems.

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Extra resources for Decomposition Methods for Differential Equations: Theory and Applications

Example text

Due to this restriction, the influence of the physical parameters is important for the stability criteria. One such condition is the Courant-Friedrichs-Lewy [CFL] condition, see [51], in which no explicit, unconditionally stable, consistent finite difference schemes for hyperbolic initial value problems exist. So the CFL condition gives the relation between the spatial and the time discretization with respect to the physical parameters, for which we have unconditionally stable results. This condition can be used for decoupling the equations into different operators, when they present different timescales.

21) where i is the spatial discretization index of the grid nodes. 2D We have the following scales: for the diffusion operator, we have Δx 2 , and for the reaction operator we have λ. We decouple both operators, if the conditions 2D 2D Δx2 << λ or Δx2 >> λ are met. If the scales are each approximate, we neglect the decoupling. 5 The scales of the operators can be changed, if we assume a higher-order discretization in space or finer spatial grids. Therefore, the balance between the order of the time and space discretization is important and should be made efficient.

In our contributions, we deal with the physical decomposition, which is more applied. In the next section we introduce the numerical analysis for the decomposition methods. 4 Numerical Analysis of the Decomposition Methods The underlying model equations for the multi-physics problems are evolution systems of partial differential equations. To solve such equations, numerical methods as discretization and solver methods have to be studied. We concentrate on the coupling of the equation systems and study decoupled equations with respect to large time-steps.

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