  Simultaneous measurements, such as the combination of particle image
velocimetry (PIV) for velocity fields with planar laser induced fluorescence
(PLIF) for species fields, are widely used in experimental turbulent combustion
applications for the analysis of a plethora of complex physical processes. Such
physical analyses are driven by the interpretation of spatial correlations
between these fields by the experimenter. However, these correlations also
imply some amount of intrinsic redundancy; the simultaneous fields contain
overlapping information content. The goal of this work lies in the quantitative
extraction of this overlapping information content in simultaneous field
measurements. Specifically, the amount of PIV information contained in
simultaneously measured OH-PLIF fields in the domain of a swirl-stabilized
combustor is sought. This task is accomplished using machine learning
techniques based on artificial neural networks designed to optimize PLIF-to-PIV
mappings. It was found that most of the velocity information content could be
retrieved when considering linear combinations of neighborhoods of OH-PLIF
signal spanning roughly two integral lengthscales (half of the considered
domain), and that PLIF signal interactions contained in smaller, local regions
(less than half of the domain) contained no PIV information. Further, by
visualizing the coherent structures contained within the neural network
parameters, the role of multi-scale interactions related to velocity field
retrieval from the OH-PLIF signal became more apparent. Overall, this study
reveals a useful pathway (in the form of overlapping information content
extraction) to develop diagnostic tools that capture more information using the
same experimental resources by minimizing redundancy.
