Machine learning for closure models in multiphase flowapplications

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
TitelProceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019
RedacteurenM. Papadrakakis, V. Papadopoulos, G. Stefanou
UitgeverijNational Technical University of Athens
Pagina's379-399
Aantal pagina's21
ISBN van geprinte versie9786188284494
DOI's
StatusGepubliceerd - 2019
Evenement3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 - Crete, Griekenland
Duur: 24 jun 201926 jun 2019

Congres

Congres3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019
LandGriekenland
StadCrete
Periode24/06/1926/06/19

Vingerafdruk Duik in de onderzoeksthema's van 'Machine learning for closure models in multiphase flowapplications'. Samen vormen ze een unieke vingerafdruk.

  • Citeer dit

    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 (editors), Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019 (blz. 379-399). National Technical University of Athens. https://doi.org/10.7712/120219.6348.18409