Inverse Design of Optimal Stern Shape with Convolutional Neural Network-based Pressure Distribution

TL;DR

This paper introduces a deep learning-based inverse design method for optimal stern shapes, using CNNs to analyze pressure distributions and estimate hull geometry, streamlining hull form design processes.

Contribution

It presents a novel inverse design algorithm employing CNNs and multi-task learning to estimate stern shapes from pressure distribution contours, reducing iterative design efforts.

Findings

The CNN effectively extracts pressure distribution features.

The multi-task model accurately estimates stern shape sections.

The method reduces the need for repeated CFD simulations.

Abstract

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