Effect of feature extraction on underwater moving body cavitation pressure reconstruction and prediction

The analysis of cavitation pressure for high-speed underwater moving bodies is critical for hydrodynamic performance and stability. However, traditional methods relying on physical experiments or high-fidelity CFD simulations are computationally expensive and time-consuming, often resulting in small datasets that challenge data-driven approaches. This study aimed to evaluate efficient feature extraction techniques to overcome the limitations of small-sample scenarios. This paper systematically evaluated three feature extraction methods-Principal Component Analysis (PCA), Fast Independent Component Analysis (Fast ICA), and a customized 1-dimensional Convolutional Auto-encoder (Conv1D AE)-for processing cavitation pressure data. The evaluation was conducted through two experiments: the first assessed the ability of these methods to reconstruct the pressure evolution across the body surface in an unsupervised manner, while the second investigated their performance in predicting the peak pressure using a small set of labeled samples.The findings demonstrated a clear trade-off: Fast ICA exhibited the best performance in reconstructing the overall pressure evolution, followed closely by PCA, while the Conv1D AE showed limitations in capturing sharp pressure gradients. Conversely, for the critical task of peak pressure prediction from limited labeled data, the Conv1D AE model achieved a significant 10% increase in accuracy compared to the baseline model without feature extraction, with PCA providing a 3% improvement. Fast ICA, however, was less effective for this specific prediction task.These results underscore the effectiveness of tailored feature extraction in automating cavitation analysis. By reducing reliance on manual intervention and accelerating the extraction of key features like peak pressure, these methods offer a practical pathway to enhance the design and analysis cycle of underwater moving bodies. The study establishes a foundation for applying machine learning to small-sample fluid mechanics problems, with future work focused on optimizing network architectures for improved precision.