Application of dimension reduction methods on propeller performance prediction model

Efficient and effective prediction of propeller performance, especially for hydrodynamic performance and fluctuating pressure, have always been areas of interest for ship engineers and researchers. With the rapid blooming of machine learning based surrogate models, it is important to identify proper features that can represent crucial information from numerous features of propellers' geometry design. This paper used five different dimension reduction methods to conduct the feature selection process from propellers geometry. The reduced features are then used as inputs for a random forest based surrogate model to predict both the hydrodynamic performance and fluctuation pressure under cavitation. The experiment results on three test propellers showed that dimension reduction methods such as factor analysis, principal component analysis and random forest feature selection could yield a higher precision for the surrogate model compare to using all features or the main features selected by experience, while manifold learning methods such as isometric feature mapping and locally linear embedding were not good at extracting propellers' geometry. This paper innovatively proposes dimension reduction methods that can automatically extract main features from propellers' complex geometry. With these main features selected, Artificial Intelligence models can yield a higher precision on propellers’ performance predication. Furthermore, these dimension reduction methods have high generalization ability on different types of propellers, which enables a smarter and more cost-efficient way for the preliminary design process of ship propellers. © 2023 Elsevier Ltd