Reconstruction of Missing Flow Vectors Using Deep Image Inpainting Based on Partial Convolution Layer

Experimentally obtained velocity data from techniques such as Particle Image Velocimetry (PIV) suffer from various imperfections like reflections, background noise, and seed accumulation. Consequently, PIV data have significant numbers of randomly placed “holes” of various sizes. The current work utilizes deep image inpainting based on a partial convolution (PConv) layer (Liu et al. (2018)) allowing for mask convolution over irregularly shaped and randomly placed missing regions, closely resembling realistic PIV imperfections. To this end, PIV measurements for Mach 1.4, L/D = 6 open cavity flow are analyzed for reconstructing missing vectors. Preliminary testing with a model trained on natural images has shown promising results with overall reconstruction errors within 10% of U_∞, also showing robustness towards missing data percentage. Next, pre-training of the model is performed with existing open-source flow data to fine-tune model weights after which the final training on experimental cavity flow data is performed. The overall goal of this work is to develop an effective and robust data reconstruction tool that can be extended to other experimental and numerical flows.