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In this study, a deep learning based conditional density estimation techniqueknown as conditional variational auto-encoder (CVAE) is used to fill gapstypically observed in particle image velocimetry (PIV) measurements incombustion systems. The proposed CVAE technique is trained using time resolvedgappy PIV fields, typically observed in industrially relevant combustors.Stereo-PIV (SPIV) data from a swirl combustor with very a high vector yield isused to showcase the accuracy of the proposed CVAE technique. Various errormetrics evaluated on the reconstructed velocity field in the gaps are presentedfrom data sets corresponding to three sets of combustor operating conditions.In addition to accurate data reproduction, the proposed CVAE technique offersdata compression by reducing the latent space dimension, enabling the efficientprocessing of large-scale PIV data.

Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps in Reacting PIV Data