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+ | ==Summary== | ||
+ | In order to make the numerical simulation of atherosclerotic plaque growth feasible, a temporal homogenization approach is employed. The resulting macro-scale problem for the plaque growth can be further accelerated by using parallel time integration schemes, such as the parareal algorithm. However, the parallel scalability is dominated by the computational cost of the coarse propagator. Therefore, in this paper, an interpolation-based coarse propagator, which uses growth values from previously computed micro-scale problems, is introduced. For a simple model problem, it is shown that this approach reduces both the computational work for a single parareal iteration as well as the required number of parareal iterations. | ||
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+ | == Abstract == | ||
+ | <pdf>Media:Draft_Sanchez Pinedo_150798201790_abstract.pdf</pdf> | ||
+ | |||
+ | == Full Paper == | ||
+ | <pdf>Media:Draft_Sanchez Pinedo_150798201790_paper.pdf</pdf> |
In order to make the numerical simulation of atherosclerotic plaque growth feasible, a temporal homogenization approach is employed. The resulting macro-scale problem for the plaque growth can be further accelerated by using parallel time integration schemes, such as the parareal algorithm. However, the parallel scalability is dominated by the computational cost of the coarse propagator. Therefore, in this paper, an interpolation-based coarse propagator, which uses growth values from previously computed micro-scale problems, is introduced. For a simple model problem, it is shown that this approach reduces both the computational work for a single parareal iteration as well as the required number of parareal iterations.
Published on 24/11/22
Accepted on 24/11/22
Submitted on 24/11/22
Volume Computational Applied Mathematics, 2022
DOI: 10.23967/eccomas.2022.015
Licence: CC BY-NC-SA license
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