Deep learning model for two-fluid flows

Abstract

Various industries rely on numerical tools to simulate multiphase flows due to the wide occurrence of this phenomenon in nature, manufacturing processes, or the human body. However, the significant computation burden required for such simulations directs the research interest toward incorporating data-based approaches in the solution loop. Although these approaches returned significant results in various domains, incorporating them in the computational fluid dynamics (CFD) field is wrangled by their casting aside of the already known governing constitutional laws along with the natural incompatibility of various models with unstructured irregular discretization spaces. This work suggests a coupling framework, between a traditional finite element CFD solver and a deep learning model, for tackling multiphase fluid flows without migrating the benefits of physics-enriched traditional solvers. The tailored model architecture, along with the coupling framework, allows tackling the required problem with a dynamically adapted unstructured irregular triangular mesh, thus dodging the limitation of traditional convolution neural networks. Moreover, the various ingredients that allowed the model to simulate the complex and computation-demanding Navier-Stokes flow equation, such as relying on a sequential validation dataset while exposing the model training to a noise inherited from the quality of its inferring, along with the proper choice of model inputs, are highlighted and elaborated throughout this paper. To the authors' knowledge, this work is the first of its type to introduce a data-based graph-based approach for solving multiphase flow problems with a level-set interface capturing method.