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Erschienen in: Computing and Visualization in Science 3-4/2018

27.06.2018 | Original Article

Influence of the phase accuracy of the coarse solver calculation on the convergence of the parareal method iteration for hyperbolic PDEs

verfasst von: Mikio Iizuka, Kenji Ono

Erschienen in: Computing and Visualization in Science | Ausgabe 3-4/2018

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Abstract

Gander and Petcu (ESAIM Proc 25:114–129, 2008) reported that, theoretically, the convergence of the parareal method iteration for hyperbolic PDEs is strongly influenced by the phase (frequency) accuracy of the coarse solver calculation. However, no numerical study has clearly shown this. Therefore, through numerical tests, we investigate the influence of the phase accuracy of the coarse solver calculation on the convergence of the parareal method iteration for hyperbolic PDEs. First, we consider a simple harmonic motion and a multi-DOF mass-spring system (MDMSS) as examples of hyperbolic PDEs using the modified Newmark-\(\beta \) method (Mizuta et al. in J JSCE 268:15–21, 1977), which can provide the exact phase of the time integration of a simple harmonic motion. Based on the results of the numerical tests, we show that the convergence of the parareal method iteration for hyperbolic PDEs is approximately independent of the parameters of parallel-in-time integration (PinT) and instead is dependent primarily on the phase accuracy of the coarse solver calculation. In addition, we show that reducing the number of bases in the reduced basis method (RBM) (Chen et al., in: Rozza (ed) Reduced order methods for modeling and computational reduction, MS and a modeling, simulation and applications, vol 9, Springer, Berlin, pp 187–214, 2014) causes the saturation of a decrease in an error during the parareal iteration for the MDMSS using the mode analysis method. The RBM is expected to make available accurate phase calculation in the coarse solver by maintaining the time step width as same as that of the fine solver. Second, we investigate whether the same saturation appears for the linear advection–diffusion equation when we use the RBM. We use the time evolution basis method in the RBM for the linear advection–diffusion equation. As a result, we show that reducing the number of bases causes the saturation of the decrease in the error in the linear advection–diffusion equation. Based on the results of the present study, an increase in the phase accuracy of the coarse solver calculation is strongly required for better convergence of the parareal method iteration for hyperbolic PDEs. Moreover, the saturation of the decrease in the error during the parareal method iteration should be overcome when using the RBM.

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Literatur
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Zurück zum Zitat Gander, M., Petcu, M.: Analysis of a Krylov subspace enhanced parareal algorithm for linear problems. ESAIM Proc. 25, 114–129 (2008)MathSciNetCrossRefMATH Gander, M., Petcu, M.: Analysis of a Krylov subspace enhanced parareal algorithm for linear problems. ESAIM Proc. 25, 114–129 (2008)MathSciNetCrossRefMATH
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Zurück zum Zitat Mizuta, Y., Nishiyama, K., Hirai, I.: A method for phase correction of Newmark’s \(\beta \) method. J. JSCE 268, 15–21 (1977). (in Japanese) Mizuta, Y., Nishiyama, K., Hirai, I.: A method for phase correction of Newmark’s \(\beta \) method. J. JSCE 268, 15–21 (1977). (in Japanese)
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Zurück zum Zitat Chen, F., Hesthaven, J.S., Zhu, X.: On the use of reduced basis methods to accelerate and stabilize the Parareal method. In: Rozza, G. (ed.) Reduced Order Methods for Modeling and Computational Reduction, MS and a Modeling, Simulation and Applications, vol. 9, pp. 187–214. Springer, Berlin (2014) Chen, F., Hesthaven, J.S., Zhu, X.: On the use of reduced basis methods to accelerate and stabilize the Parareal method. In: Rozza, G. (ed.) Reduced Order Methods for Modeling and Computational Reduction, MS and a Modeling, Simulation and Applications, vol. 9, pp. 187–214. Springer, Berlin (2014)
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Metadaten
Titel
Influence of the phase accuracy of the coarse solver calculation on the convergence of the parareal method iteration for hyperbolic PDEs
verfasst von
Mikio Iizuka
Kenji Ono
Publikationsdatum
27.06.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Computing and Visualization in Science / Ausgabe 3-4/2018
Print ISSN: 1432-9360
Elektronische ISSN: 1433-0369
DOI
https://doi.org/10.1007/s00791-018-0299-9