Learning Fluid Flows

Theodoros Georgiou, Sebastian Schmitt, Markus Olhofer, Yu Liu, Thomas Bäck, Michael S. Lew

Abstract: Computational Fluid Dynamics (CFD) simulations are able to produce complex and large outputs that accurately describe the physical properties of fluids and gases in various domains, such as air flow around a car, or the multi-phase flow inside an internal combustion engine. The simulation results, i.e. the flow fields, are often too complex to be analyzed directly. With the increasing number of simulations as well as their complexity, there is a need of automated processes that can analyze these complex outputs. In this paper, inspired by the success of convolutional neural networks (CNNs) in Computer Vision, we apply for the first time CNNs on CFD output. We show their capabilities in capturing and processing flow patterns. Furthermore, we design a novel CNN architecture tailored to the data produced by CFD simulations, as well as two conventional architectures and compare them. We propose and construct a new dataset of turbulent flow, within the application domain of steady flow around passenger cars. We use that dataset to evaluate and compare the proposed methods, on different tasks that depend on flow patterns. Finally, we compare our methods with a baseline k-nearest neighbor approach, tuned to be comparable to the state-of-the-art.