Additional degrees of parallelism within the Adomian decomposition method

4th International Conference on Computational Engineering (ICCE 2017), 28-29 September 2017, DarmstadtThis is the author accepted manuscript. The final version is available from Springer via the DOI in this record.The trend of future massively parallel computer architectures challenges the exploration of additional degrees of parallelism also in the time dimension when solving continuum mechanical partial differential equations. The Adomian decomposition method (ADM) is investigated to this respects in the present work. This is accomplished by comparison with the Runge-Kutta (RK) time integration and put in the context of the viscous Burgers equation. Our studies show that both methods have similar restrictions regarding their maximal time step size. Increasing the order of the schemes leads to larger errors for the ADM compared to RK. However, we also discuss a parallelization within the ADM, reducing its runtime complexity from O(n^2) to O(n). This indicates the possibility to make it a viable competitor to RK, as fewer function evaluations have to be done in serial, if a high order method is desired. Additionally, creating ADM schemes of high-order is less complex as it is with RK.The work of Andreas Schmitt is supported by the ’Excellence Initiative’ of the German Federal and State Governments and the Graduate School of Computational Engineering at Technische Universit¨at Darmstadt

Adomian decomposition method, nonlinear equations and spectral solutions of burgers equation

Tese de doutoramento. Ciências da Engenharia. 2006. Faculdade de Engenharia. Universidade do Porto, Instituto Superior Técnico. Universidade Técnica de Lisbo

On adomian based numerical schemes for euler and navier-stokes equations, and application to aeroacoustic propagation

140 p.En esta tesis se ha desarrollado un nuevo método de integración en tiempo de tipo derivadas sucesivas (multiderivative), llamado ABS y basado en el algoritmo de Adomian. Su motivación radica en la reducción del coste de simulación para problemas en aeroacústica, muy costosos por su naturaleza transitoria y requisitos de alta precisión. El método ha sido satisfactoriamente empleado en ambas partes de un sistema híbrido, donde se distinguen la parte aerodinámica y la acústica.En la parte aerodinámica las ecuaciones de Navier-Stokes incompresibles son resueltas con unmétodo de proyección clásico. Sin embargo, la fase de predicción de velocidad ha sido modificadapara incluir el método ABS en combinación con dos métodos: una discretización espacial MAC devolúmenes finitos, y también con un método de alto orden basado en ADER. El método se ha validado respecto a los problemas (en 2D) de vórtices de Taylor-Green, y el desarrollo de vórticesde Karman en un cilindro cuadrado. La parte acústica resuelve la propagación de ondas descritaspor las ecuaciones linearizadas de Euler, empleando una discretización de Galerkin discontinua. El método se ha validado respecto a la ecuación de Burgers.El método ABS es sencillo de programar con una formulación recursiva. Los resultados demuestran que su sencillez junto con sus altas capacidades de adaptación lo convierten en un método fácilmente extensible a órdenes altos, a la vez que reduce el coste comparado con otros métodos clásicos

On Adomian Based Numerical Schemes for Euler and Navier-Stokes Equations, and Application to Aeroacoustic Propagation

In this thesis, an Adomian Based Scheme (ABS) for the compressible Navier-Stokes equations is constructed, resulting in a new multiderivative type scheme not found in the context of fluid dynamics. Moreover, this scheme is developed as a means to reduce the computational cost associated with aeroacoustic simulations, which are unsteady in nature with high-order requirements for the acoustic wave propagation. We start by constructing a set of governing equations for the hybrid computational aeroacoustics method, splitting the problem into two steps: acoustic source computation and wave propagation. The first step solves the incompressible Navier-Stokes equation using Chorin's projection method, which can be understood as a prediction-correction method. First, the velocity prediction is obtained solving the viscous Burgers' equation. Then, its divergence-free correction is performed using a pressure Poisson type projection. In the velocity prediction substep, Burgers' equation is solved using two ABS variants: a MAC type implementation, and a ``modern'' ADER method. The second step in the hybrid method, related to wave propagation, is solved combining ABS with the discontinuous Galerkin high-order approach. Described solvers are validated against several test cases: vortex shedding and Taylor-Green vortex problems for the first step, and a Gaussian wave propagation in the second case. Although ABS is a multiderivative type scheme, it is easily programmed with an elegant recursive formulation, even for the general Navier-Stokes equations. Results show that its simplicity combined with excellent adaptivity capabilities allows for a successful extension to very high-order accuracy at relatively low cost, obtaining considerable time savings in all test cases considered.Predoc Gobierno Vasc

The non-standard finite difference scheme for linear fractional PDEs in fluid mechanics

AbstractA non-standard finite difference scheme is developed to solve the linear partial differential equations with time- and space-fractional derivatives. The Grunwald–Letnikov method is used to approximate the fractional derivatives. Numerical illustrations that include the linear inhomogeneous time-fractional equation, linear space-fractional telegraph equation, linear inhomogeneous fractional Burgers equation and the fractional wave equation are investigated to show the pertinent features of the technique. Numerical results are presented graphically and reveal that the non-standard finite difference scheme is very effective and convenient for solving linear partial differential equations of fractional order

Numerical Investigation of Parallel-in-Time Methods for Dominantly Hyperbolic Equations

Simulations aid in many scientific and industrial applications. A general ambition for these simulations is to keep the time-to-solution as small as possible while maintaining a desired accuracy. Besides with high computational power, this can be achieved by employing multiple processing units with parallelization. Today’s state of the art is the spatial parallelization which provides a very good parallel efficiency. However, such a parallelization introduces communication and synchronization overheads leading to a maximal number of processing units which can be used efficiently. Applying a parallelization in time on top of the parallelization in space makes using more processing units possible. An issue of the parallel-in-time methods is their problem dependent efficiency. It tends to be generally bad for dominantly hyperbolic problems. The viscous Burgers equation, which for small viscosities falls into that category, is used to investigate two methods of parallelization in time. First, a look is taken at the Adomian decomposition method (ADM) and possibilities of exploiting additional degrees of parallelism within this method. Its viability is questioned by comparing its discrete version (DADM) to the explicit Runge-Kutta method (ERK). The comparison shows similar restric- tions regarding their maximal time step size for both methods. Furthermore, the DADM leads to larger errors with increasing order of accuracy compared to the ERK. However, discussing the parallelization within the DADM shows a reduction of the runtime complexity from quadratic to linear is possible. With this reduction in the runtime DADM seems to be a viable competitor to the ERK. This is especially true for high-order schemes, as fewer function evaluations have to be run serial. Increasing the order of accuracy is also embarrassingly easy with the DADM compared to the ERK. The second method investigated in this thesis is the Parareal algorithm. Here, the focus lies on the potential of the implicit Runge-Kutta method with semi-Lagrangian advection (SLIRK) as the coarse solver for the Parareal algorithm. Its potential compared to using the explicit Runge-Kutta method (ERK) and the implicit-explicit Runge-Kutta method (IMEX) is tested with three different benchmarks. The comparison shows the ERK is in contrast to the other two methods not able to provide speedup potential with the chosen benchmarks. For advection dominated problems SLIRK performs better than IMEX due to its stability. The stability of SLIRK leads to speedup potential for a larger range of viscosities with the Parareal algorithm. Still, the instability of Parareal itself causes a decreasing potential with a decreasing viscosity. With an inviscid case the number of iterations to convergence for Parareal is too large to yield a reasonable speedup. An additional result worth mentioning is it was possible to show the importance of predicting the phase of the solution correctly for the convergence of Parareal

Finite difference method for numerical solution of a generalized burgers-huxley equation

There are many applications of the generalized Burgers-Huxley equation which is a form of nonlinear Partial Differential Equation such as in the work of physicist which can effectively models the interaction between reaction mechanisms, convection effects and diffusion transports. This study investigates on the implementation of numerical method for solving the generalized Burgers-Huxley equation. The method is known as the Finite Difference Method which can be employed using several approaches and this work focuses on the Explicit Method, the Modified Local Crank-Nicolson (MLCN) Method and Nonstandard Finite Difference Schemes (NFDS). In order to use the NFDS, due to a lack of boundary condition provided in the problem, this research used the Forward Time Central Space (FTCS) Method to approximate the first step in time. Thomas Algorithm was applied for the methods that lead to a system of linear equation. Computer codes are provided for these methods using the MATLAB software. The results obtained are compared among the three methods with the exact solution for determining their accuracy. Results shows that NFDS has the lowest relative error and one of the best way among these three methods in order to solve the generalized Burgers-Huxley equations