Development of advanced AI-based segmentation and prediction method for air entrainment in plunging water jets

Abstract

Understanding air entrainment phenomena induced by plunging water jets is critical in the fields of nuclear and hydraulic engineering. Air entrainment is one of the key safety design parameters for nuclear systems. However, most existing studies rely on empirical correlations or curve-fitting models to estimate bubble penetration depth, and no agreed-upon calculation principle exists for varying jet conditions. To address these limitations, this research developed two advanced AI approaches: an improved YOLOv5 for segmenting air entrainment and the NSGA-III-BPNN method for predicting penetration depth. The improved YOLOv5 enables real-time segmentation and extraction of air entrainment motion and dynamics under diverse conditions, demonstrating high scalability and robustness. The penetration depth estimated using the improved YOLOv5 shows greater accuracy compared to conventional empirical correlationsand is more efficient than traditional image post-processing techniques for classifying shape regimes based on dynamic air entrainment patterns. To overcome the limitations of object segmentation, which typically relies on video or image data, the NSGA-III-BPNN method predicts maximum penetration depths with greater accuracy than YOLOv5, offering a more effective prediction model for air entrainment penetration depth. By leveraging advanced AI techniques, the research not only provides valuable segmentation data for refining computational fluid dynamics (CFD) modeling but also paves the way for significant advancements in both nuclear and hydraulic engineering.