Data underlying chapter 4 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels

This repository contains the code and data supporting the results presented in Chapter 4 of the dissertation "Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels" and the paper "Multi-fidelity design framework to support early-stage design exploration of the AXE frigates: the vertical bending moment case". The research explores the potential of harnessing multi-fidelity models for early-stage predictions of wave-induced loads, with a specific focus on wave-induced vertical bending moments. The assessed models include the application of both linear and nonlinear Gaussian processes and compositional kernels to improve predictions of wave-induced loads, specifically focusing on wave-induced vertical bending moments. The case study focuses on the early-stage exploration of the AXE frigates. Multi-fidelity models were constructed using both frequency- and time-domain methods to evaluate the vertical bending moments experienced by the hull.<br>The data include: (1) the parametric model developed in Rhino and Grasshopper used to generate the hull mesh, (2) the simulation data, (3) the data associated with the analyzed cases, and (4)the Python scripts can be found in this gitlab repository. The analysis solvers used to calculate the vertical bending moments for calculating the vertical bending moments are not included in this repository.

本代码仓库包含支撑两项研究成果的代码与数据：其一为学位论文《新型船舶前期设计用多保真度概率设计框架》（Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels）第4章内容，其二为论文《支持AXE型护卫舰前期设计探索的多保真度设计框架：以垂向弯矩工况为例》（Multi-fidelity design framework to support early-stage design exploration of the AXE frigates: the vertical bending moment case）。
本研究探索了利用多保真度模型开展波浪诱导载荷前期预测的潜力，研究重点聚焦于波浪诱导垂向弯矩。本次评估的模型涵盖线性与非线性高斯过程（Gaussian processes）及组合核函数（compositional kernels）的应用方案，旨在提升波浪诱导载荷的预测性能，研究仍以波浪诱导垂向弯矩作为核心关注点。
本案例研究围绕AXE型护卫舰的前期设计探索展开。研究采用频域及时域两种方法构建多保真度模型，以评估船体承受的垂向弯矩。
本仓库所包含的数据如下：(1) 借助Rhino与Grasshopper开发的、用于生成船体网格的参数化模型；(2) 仿真数据；(3) 与分析工况相关的数据集；(4) 相关Python脚本。所有内容均可在本GitLab仓库中获取。需注意，用于计算垂向弯矩的分析求解器未包含在本仓库中。