Development of an Improved One-Hot Encoding Method for Bubbly Flow Image Prediction Generation under Continuous Superficial Gas Velocities

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In recent years, bubble segmentation and detection algorithms have significantly contributed to the study of two-phase interface parameters. Nevertheless, high-quality bubbly flow image data acquisition remains a major constraint to their development. The application of deep learning methods, especially Conditional StyleGAN2, has alleviated this problem to some extent. However, it is restricted to categorical conditions, which means it could only generate bubbly flow images at superficial gas velocities included in the training data. In this work, we propose an improved one-hot encoding method to extend our previous work to predict and synthesize bubbly flow images under continuous superficial gas velocity. A reference sample set (RSS) is constructed and encoded at first to train the StyleGAN2 model, and the linear interpolation method is then employed by referring to the RSS to encode sample sets with arbitrary flow velocities and to generate the corresponding bubbly flow images. The proposed method is validated by training on an RSS which includes 9 real superficial gas velocities to generate images at different superficial gas velocities (include or exclude in the training data). Interface parameters, such as bubble size distribution, average equivalent diameter, void fraction, and interfacial area concentration, are adopted as quantitative evaluation metrics to evaluate the model’s synthesis and prediction ability. The comparative analysis results indicate that the proposed method can generate bubbly flow images at an arbitrary superficial gas velocity in the studied range with an absolute relative error of no more than 9% under all metrics, which is potentially helpful for the advancement of two-phase flow investigation in practical implementations.

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