  We present EngineBench, the first machine learning (ML) oriented database to
use high quality experimental data for the study of turbulent flows inside
combustion machinery. Prior datasets for ML in fluid mechanics are synthetic or
use overly simplistic geometries. EngineBench is comprised of real-world
particle image velocimetry (PIV) data that captures the turbulent airflow
patterns in a specially-designed optical engine. However, in PIV data from
internal flows, such as from engines, it is often challenging to achieve a full
field of view and large occlusions can be present. In order to design optimal
combustion systems, insight into the turbulent flows in these obscured areas is
needed, which can be provided via inpainting models. Here we propose a novel
inpainting task using random edge gaps, a technique that emphasises realism by
introducing occlusions at random sizes and orientations at the edges of the PIV
images. We test five ML methods on random edge gaps using pixel-wise,
vector-based, and multi-scale performance metrics. We find that UNet-based
models are more accurate than the industry-norm non-parametric approach and the
context encoder at this task on both small and large gap sizes. The dataset and
inpainting task presented in this paper support the development of more
general-purpose pre-trained ML models for engine design problems. The method
comparisons allow for more informed selection of ML models for problems in
experimental flow diagnostics. All data and code are publicly available at
https://eng.ox.ac.uk/tpsrg/research/enginebench/.
