Information Matrix-Based Adaptive Sampling in Hull Form Optimisation

Xuyu Ouyang,Zuyuan Liu,Chengsheng Zhan,Xide Cheng,Baiwei Feng,Haichao Chang

doi:10.3390/jmse9090973

Xuyu Ouyang, Zuyuan Liu + Show 4 more

Open Access

https://doi.org/10.3390/jmse9090973

Copy DOI

Abstract

Hull form optimisation involves challenges such as large design spaces, numerous design variables, and high nonlinearity. Therefore, optimisation that only use global approximate models alone cannot yield desirable results. An information matrix-based method is proposed for dynamically embedded local approximate models (IM-DEAM) in this paper, which uses the Gaussian-function information matrix to extract one or more subspaces for additional sampling and a Latin hypercube design (LHD) for adaptive sampling. In addition, to prevent overfitting by global approximate models in some spaces because of the uneven distribution of the samples, local approximate models are embedded in the subspaces identified for additional sampling to enable accurate description of subspaces. The effectiveness and robustness of the method are validated and analysed by applying the proposed method to optimise mathematical functions and the hull form of the DTMB 5415. The results demonstrate that the proposed method is effective for improving the accuracies and can produce reliable optimisation results.

Highlights

IntroductionOne-shot sampling determines the number of samples for the experimental design, that is, sampling only once
It can be seen that the optimised hull form leads to 0.73% increase in displacement volume and a large decrease (54.36%) in wave-making resistance
A method was proposed for the identification and dynamic sampling of subspaces based on the sample information matrix

Summary

Introduction

One-shot sampling determines the number of samples for the experimental design, that is, sampling only once. In view of the limitations and deficiencies of the one-shot sampling strategy, a dynamic sampling (or adaptive sampling) strategy is proposed to improve the accuracy and development of the approximate model in the entire design space, which allows the selection of sample points through the approximate model or the data it learns. For engineering optimisation problems without a priori knowledge, one-shot sampling may result in an undersampling of the objective function or in an excessive number of training points [14]. Dynamic sampling, which does not have the limitations and deficiencies of one-shot sampling, involves sampling of additional points in regions with significant errors [15,16,17]

Objectives

Methods

Results

Conclusion