Inverse Design of Optimal Stern Shape with Convolutional Neural Network-based Pressure Distribution
This work addresses the time-consuming hull design process in naval architecture by automating stern shape estimation, though it is incremental as it builds on existing deep learning methods for a specific domain.
The study tackled the iterative hull form design process by proposing an inverse design algorithm that uses a convolutional neural network to estimate the stern shape from a target pressure distribution, achieving performance verification through comparison of actual and converted offsets.
Hull form designing is an iterative process wherein the performance of the hull form needs to be checked via computational fluid dynamics calculations or model experiments. The stern shape has to undergo a process wherein the hull form variations from the pressure distribution analysis results are repeated until the resistance and propulsion efficiency meet the design requirements. In this study, the designer designed a pressure distribution that meets the design requirements; this paper proposes an inverse design algorithm that estimates the stern shape using deep learning. A convolutional neural network was used to extract the features of the pressure distribution expressed as a contour, whereas a multi-task learning model was used to estimate various sections of the stern shape. We estimated the stern shape indirectly by estimating the control point of the B-spline and comparing the actual and converted offsets for each section; the performance was verified, and an inverse design is proposed herein