  Computational Fluid Dynamics (CFD) simulations are a very important tool for
many industrial applications, such as aerodynamic optimization of engineering
designs like cars shapes, airplanes parts etc. The output of such simulations,
in particular the calculated flow fields, are usually very complex and hard to
interpret for realistic three-dimensional real-world applications, especially
if time-dependent simulations are investigated. Automated data analysis methods
are warranted but a non-trivial obstacle is given by the very large
dimensionality of the data. A flow field typically consists of six measurement
values for each point of the computational grid in 3D space and time (velocity
vector values, turbulent kinetic energy, pressure and viscosity). In this paper
we address the task of extracting meaningful results in an automated manner
from such high dimensional data sets. We propose deep learning methods which
are capable of processing such data and which can be trained to solve relevant
tasks on simulation data, i.e. predicting drag and lift forces applied on an
airfoil. We also propose an adaptation of the classical hand crafted features
known from computer vision to address the same problem and compare a large
variety of descriptors and detectors. Finally, we compile a large dataset of 2D
simulations of the flow field around airfoils which contains 16000 flow fields
with which we tested and compared approaches. Our results show that the deep
learning-based methods, as well as hand crafted feature based approaches, are
well-capable to accurately describe the content of the CFD simulation output on
the proposed dataset.
