Xinkai Lv
ABSTRACT
Aerodynamic performance analysis is crucial for the design and maneuvering of sailboats. The calculation of aerodynamic coefficients using Computational Fluid Dynamics (CFD) methods requires a significant amount of time and computational resources. Therefore, this article proposes a Multi-Scale Residual 3D UNet (MSR UNet) model to rapidly predict the overall aerodynamic coefficients (i.e. the longitudinal force coefficient and lateral force coefficient) of a sailboat. First, multi-scale residual learning is applied to extract multi-scale features of the pressure field, channel and spatial attention are introduced to enhance the perception of key features such as pressure field boundary and pressure distribution. Subsequently, Residual-Subsampled Blocks (RSB) as well as Residual-Inception Blocks (RIB) are designed to extract local and global information of the pressure field by merging feature maps of different scales. Finally, using the high-dimensional pressure field as a transition, a mapping from the operating parameters (i.e. the ship relative wind angle, main sail angle and foresail angle) to the aerodynamic coefficients is established based on the feedforward neural network (FNN). The model is trained and tested using pressure fields and aerodynamic data obtained from 3D steady RANS CFD analysis. Experimental results demonstrate that our model not only possesses stronger multi-scale feature extraction performance but also achieves high-precision and real-time prediction of sailboat aerodynamic coefficients.
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Index Terms
- Fast Prediction of Sailboat Aerodynamic Coefficients using Multi-Scale Residual 3D Unet
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