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A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows
by M. Petrella and R. Abgrall and S. Mishra
(Report number 2023-18)
Abstract
We propose a novel Monte-Carlo based ab-initio algorithm for directly computing the statistics
for quantities of interest in an immiscible two-phase compressible flow. Our algorithm samples the
underlying probability space and evolves these samples with a sharp interface front-tracking scheme.
Consequently, statistical information is generated without resorting to any closure assumptions and
information about the underlying microstructure is implicitly included. The proposed algorithm is
tested on a suite of numerical experiments and we observe that the ab-initio procedure can simulate
a variety of flow regimes robustly and converges with respect of refinement of number of samples
as well as number of bubbles per volume. The results are also compared with a state-of-the-art
discrete equation method to reveal the inherent limitations of existing macroscopic models.
Keywords: Multiphase Flow, Baer-Nunziato model, Front-Tracking
BibTeX@Techreport{PAM23_1055, author = {M. Petrella and R. Abgrall and S. Mishra}, title = {A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows}, institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich}, number = {2023-18}, address = {Switzerland}, url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2023/2023-18.pdf }, year = {2023} }
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