Three-Dimensional Ship Hull Encoding and Optimization via Deep Neural Networks

Abstract

Design and optimization of hull shapes for optimal hydrodynamic performance have been a major challenge for naval architectures. Deep learning bears the promise of comprehensive geometric representation and new design synthesis. In this work, we develop a deep neural network (DNN)-based approach to encode the hull designs to condensed representations, synthesize novel designs, and optimize the synthetic design based on the hydrodynamic performance. A variational autoencoder (VAE) with the hydro-predictor is developed to learn the representation through reconstructing the Laplacian parameterized hulls and encode the geometry-drag function simulated through computational fluid dynamics (CFD). Two data augmentation techniques, Perlin noise mapping and free-form deformation (FFD), are implemented to create the training set from a parent hull. The trained VAE is leveraged to efficiently optimize from massive synthetic hull vessels toward the optimal predicted drag performance. The selected geometries are further investigated and virtually screened under CFD simulations. Experiments show that our convolutional neural network (CNN) model accurately reconstructs the input vessels and predicts the corresponding drag coefficients. The proposed framework is demonstrated to synthesize realistic hull designs and optimize toward new hull designs with the drag coefficient decreased by 35% comparing to the parent design.