  This work utilizes data-driven methods to morph a series of time-resolved
experimental OH-PLIF images into corresponding three-component planar PIV
fields in the closed domain of a premixed swirl combustor. The task is carried
out with a fully convolutional network, which is a type of convolutional neural
network (CNN) used in many applications in machine learning, alongside an
existing experimental dataset which consists of simultaneous OH-PLIF and PIV
measurements in both attached and detached flame regimes. Two types of models
are compared: 1) a global CNN which is trained using images from the entire
domain, and 2) a set of local CNNs, which are trained only on individual
sections of the domain. The locally trained models show improvement in creating
mappings in the detached regime over the global models. A comparison between
model performance in attached and detached regimes shows that the CNNs are much
more accurate across the board in creating velocity fields for attached flames.
Inclusion of time history in the PLIF input resulted in small noticeable
improvement on average, which could imply a greater physical role of
instantaneous spatial correlations in the decoding process over temporal
dependencies from the perspective of the CNN. Additionally, the performance of
local models trained to produce mappings in one section of the domain is tested
on other, unexplored sections of the domain. Interestingly, local CNN
performance on unseen domain regions revealed the models' ability to utilize
symmetry and antisymmetry in the velocity field. Ultimately, this work shows
the powerful ability of the CNN to decode the three-dimensional PIV fields from
input OH-PLIF images, providing a potential groundwork for a very useful tool
for experimental configurations in which accessibility of forms of simultaneous
measurements are limited.
