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| Hauptseite > Publikationsdatenbank > Solving nonlinear parabolic PDEs in several dimensions: Parallelized ESERK codes > print |
| Hauptseite > Publikationsdatenbank > Solving nonlinear parabolic PDEs in several dimensions: Parallelized ESERK codes > print |
| 001 | 878664 | ||
| 005 | 20210130005718.0 | ||
| 024 | 7 | _ | |a 10.1016/j.jcp.2020.109771 |2 doi |
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| 082 | _ | _ | |a 000 |
| 100 | 1 | _ | |a Martín-Vaquero, J. |0 0000-0001-5118-5576 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Solving nonlinear parabolic PDEs in several dimensions: Parallelized ESERK codes |
| 260 | _ | _ | |a Amsterdam |c 2020 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a There is a very large number of very important situations which can be modeled with nonlinear parabolic partial differential equations (PDEs) in several dimensions. In general, these PDEs can be solved by discretizing in the spatial variables and transforming them into huge systems of ordinary differential equations (ODEs), which are very stiff. Therefore, standard explicit methods require a large number of iterations to solve stiff problems. But implicit schemes are computationally very expensive when solving huge systems of nonlinear ODEs. Several families of Extrapolated Stabilized Explicit Runge-Kutta schemes (ESERK) with different order of accuracy (3 to 6) are derived and analyzed in this work. They are explicit methods, with stability regions extended, along the negative real semi-axis, quadratically with respect to the number of stages s, hence they can be considered to solve stiff problems much faster than traditional explicit schemes. Additionally, they allow the adaptation of the step length easily with a very small cost.Two new families of ESERK schemes (ESERK3 and ESERK6) are derived, and analyzed, in this work. Each family has more than 50 new schemes, with up to 84.000 stages in the case of ESERK6. For the first time, we also parallelized all these new variable step length and variable number of stages algorithms (ESERK3, ESERK4, ESERK5, and ESERK6). These parallelized strategies allow to decrease times significantly, as it is discussed and also shown numerically in two problems. Thus, the new codes provide very good results compared to other well-known ODE solvers. Finally, a new strategy is proposed to increase the efficiency of these schemes, and it is discussed the idea of combining ESERK families in one code, because typically, stiff problems have different zones and according to them and the requested tolerance the optimum order of convergence is different. |
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| 773 | _ | _ | |a 10.1016/j.jcp.2020.109771 |g p. 109771 - |0 PERI:(DE-600)1469164-4 |p 109771 |t Journal of computational physics |v 423 |y 2020 |x 0021-9991 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/878664/files/ESERK36_Rev.pdf |
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