An artificial intelligence-aided design (AIAD) of ship hull structures

IF 13 1区 工程技术 Q1 ENGINEERING, MARINE
Yu Ao , Yunbo Li , Jiaye Gong , Shaofan Li
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引用次数: 6

Abstract

Ship-hull design is a complex process because the any slight local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a ship. To find the optimum hull shape under the design requirements, the state-of-art of ship hull design combines computational fluid dynamics computation with geometric modeling. However, this process is very computationally intensive, which is only suitable at the final stage of the design process. To narrow down the design parameter space, in this work, we have developed an AI-based deep learning neural network to realize a real-time prediction of the total resistance of the ship-hull structure in its initial design process. In this work, we have demonstrated how to use the developed DNN model to carry out the initial ship hull design. The validation results showed that the deep learning model could accurately predict the ship hull’s total resistance accurately after being trained, where the average error of all samples in the testing dataset is lower than 4%. Simultaneously, the trained deep learning model can predict the hip’s performances in real-time by inputting geometric modification parameters without tedious preprocessing and calculation processes. The machine learning approach in ship hull design proposed in this work is the first step towards the artificial intelligence-aided design in naval architectures.

船体结构的人工智能辅助设计
船体设计是一个复杂的过程,因为船体结构的任何微小局部变化都可能显著改变船舶的静水压和水动力性能。为了找到符合设计要求的最佳船体形状,船体设计的最新技术将计算流体动力学计算与几何建模相结合。然而,这个过程的计算量非常大,只适用于设计过程的最后阶段。为了缩小设计参数空间,在这项工作中,我们开发了一种基于人工智能的深度学习神经网络,以实现对船体结构初始设计过程中总阻力的实时预测。在这项工作中,我们展示了如何使用开发的DNN模型来进行初始船体设计。验证结果表明,深度学习模型经过训练后可以准确预测船体的总阻力,测试数据集中所有样本的平均误差低于4%。同时,训练后的深度学习模型可以通过输入几何修改参数实时预测髋关节的性能,而无需繁琐的预处理和计算过程。本文提出的船体设计中的机器学习方法是海军建筑中人工智能辅助设计的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.50
自引率
19.70%
发文量
224
审稿时长
29 days
期刊介绍: The Journal of Ocean Engineering and Science (JOES) serves as a platform for disseminating original research and advancements in the realm of ocean engineering and science. JOES encourages the submission of papers covering various aspects of ocean engineering and science.
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