Hull Form Optimization with Principal Component Analysis and Deep Neural Network
This addresses the challenge of designing efficient hull forms for naval architects, though it appears incremental by combining existing methods.
The study tackled hull form optimization for vessels by using Principal Component Analysis to generate derived hull forms and training a Deep Neural Network to predict their calm-water performances, enabling a large-scale search for optimal designs.
Designing and modifying complex hull forms for optimal vessel performances have been a major challenge for naval architects. In the present study, Principal Component Analysis (PCA) is introduced to compress the geometric representation of a group of existing vessels, and the resulting principal scores are manipulated to generate a large number of derived hull forms, which are evaluated computationally for their calm-water performances. The results are subsequently used to train a Deep Neural Network (DNN) to accurately establish the relation between different hull forms and their associated performances. Then, based on the fast, parallel DNN-based hull-form evaluation, the large-scale search for optimal hull forms is performed.