TY  - EJOU
AU  - Gong, Junhua 
AU  - Chen, Yujie 
AU  - Yu, Bo 
AU  - Chen, Bin 

TI  - Neural Network-Based Bubble Interface Prediction
T2  - The International Conference on Computational \& Experimental Engineering and Sciences

PY  - 2024
VL  - 29
IS  - 4
SN  - 1933-2815

AB  - Traditional interface reconstruction methods often rely on numerical approaches, which can be inefficient when dealing with large bubbles, requiring extensive computational resources. To address this issue, we propose a novel model based on convolutional neural networks aimed at rapidly and accurately predicting the equations governing circular bubbles. This model takes the volume fraction of the main-phase fluid surrounding each computational grid cell as input variables and is capable of precisely forecasting the coordinates and radii of bubbles. To further enhance model performance, we employ the Optuna hyperparameter optimizer to fine-tune the model's parameters. Upon training completion, the optimized model achieves prediction errors of less than 1%, demonstrating exceptional accuracy.
KW  - Neural network; interface construction; bubble

DO  - 10.32604/icces.2024.012531