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.

使用基于深度学习的条件变分自编码器 (CVAE) 技术填补燃烧系统中常见的粒子图像测速 (PIV) 测量中的空白，通过时间分辨率的可观测商业相关燃烧器中的缺失 PIV 场进行训练，通过对三组燃烧器操作条件数据集的重建速度场评估不同误差指标来展示该技术的准确性，除了精确的数据重建，该方法还通过减少潜在空间维度实现数据压缩，实现对大规模 PIV 数据的高效处理。