Advanced Deep Learning-Based Bubbly Flow Image Generator under Different Superficial Gas Velocities

Bubble synthesis technology is beneficial to reduce the cost of visualization research on gas-liquid two-phase flow and provides an effective tool to benchmark data for the development of advanced image processing algorithms. In this work, we proposed an advanced StyleGAN2-based bubbly flow image generator, which was trained on 15 000 images obtained at 10 different superficial gas velocities. The main factors that restrict the network synthesis quality have been investigated, and quantitative evaluation methods based on the Yolov3 detector are also developed to validate the generator's reliability and ability. With the optimized inputs and truncated latent vector, the generator can synthesize high-diversity bubbly flows with 512 x 512 pixel resolution, meanwhile, synthesizing more realistic and higher fidelity bubbly flows than existing technologies in terms of location distribution, size distribution characteristics, and morphology revivification. Such generation and detection methods will be useful for the development of two-phase flow research in practical applications.