Machine learning for closure models in multiphase flow applications
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.
|Keywords||Machine Learning, Closure Terms, Multiphase Flow, Two-Fluid Model|
|Journal||Proceedings of the International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP|
|Conference||ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering|
Buist, J.F.H, Sanderse, B, van Halder, Y, Koren, B, & van Heijst, G.J.F. (2019). Machine learning for closure models in multiphase flow applications. In Proceedings of the International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP (pp. 379–399).