Research reports

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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