Machine learning for closure models in multiphase flowapplications

Jurriaan Buist, Benjamin Sanderse, Yous van Halder, Barry Koren, Gertjan van Heijst

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Multiphase flows are described by the multiphase Navier-Stokes equations. Numerically solving these equations is computationally expensive, and performing many simulations for the purpose of design, optimization and uncertainty quantification is often prohibitively expensive. A simplified model, the so-called two-fluid model, can be derived from a spatial averaging process. The averaging process introduces a closure problem, which is represented by unknown friction terms in the two-fluid model. Correctly modeling these friction terms is a long-standing problem in two-fluid model development. In this work we take a new approach, and learn the closure terms in the two-fluid model from a set of unsteady high-fidelity simulations conducted with the open source code Gerris. These form the training data for a neural network. The neural network provides a functional relation between the two-fluid model's resolved quantities and the closure terms, which are added as source terms to the two-fluid model. With the addition of the locally defined interfacial slope as an input to the closure terms, the trained two-fluid model reproduces the dynamic behavior of high fidelity simulations better than the two-fluid model using a conventional set of closure terms.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019
EditorsM. Papadrakakis, V. Papadopoulos, G. Stefanou
PublisherNational Technical University of Athens
Pages379-399
Number of pages21
ISBN (Print)9786188284494
DOIs
Publication statusPublished - 2019
Event3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 - Crete, Greece
Duration: 24 Jun 201926 Jun 2019

Conference

Conference3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019
CountryGreece
CityCrete
Period24/06/1926/06/19

Keywords

  • Closure Terms
  • Machine Learning
  • Multiphase Flow
  • Two-Fluid Model

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  • Cite this

    Buist, J., Sanderse, B., van Halder, Y., Koren, B., & van Heijst, G. (2019). Machine learning for closure models in multiphase flowapplications. In M. Papadrakakis, V. Papadopoulos, & G. Stefanou (Eds.), Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 (pp. 379-399). National Technical University of Athens. https://doi.org/10.7712/120219.6348.18409