Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps in Reacting PIV Data
This addresses data gaps in PIV measurements for combustion systems, offering incremental improvements in processing efficiency.
The study tackled the problem of filling gaps in particle image velocimetry (PIV) data from combustion systems using a conditional variational auto-encoder (CVAE) technique, achieving accurate reconstruction with various error metrics evaluated across three combustor operating conditions and enabling data compression.
In this study, a deep learning based conditional density estimation technique known as conditional variational auto-encoder (CVAE) is used to fill gaps typically observed in particle image velocimetry (PIV) measurements in combustion systems. The proposed CVAE technique is trained using time resolved gappy PIV fields, typically observed in industrially relevant combustors. Stereo-PIV (SPIV) data from a swirl combustor with very a high vector yield is used to showcase the accuracy of the proposed CVAE technique. Various error metrics evaluated on the reconstructed velocity field in the gaps are presented from data sets corresponding to three sets of combustor operating conditions. In addition to accurate data reproduction, the proposed CVAE technique offers data compression by reducing the latent space dimension, enabling the efficient processing of large-scale PIV data.