Ship design performance and cost optimization with machine learning

Winter, R. de; Stein, B. van; Bäck, T.H.W. (2021)

Article in monograph or in proceedings

This contribution shows how, in the preliminary design stage, naval architects can make more informed decisions by using machine learning. In this ship design phase, little information is available, and decisions need to be made in a limited amount of time. However, it is in the preliminary design phase where the most influential decisions are made regarding the global dimensions, the machinery, and therefore the performance and costs. In this paper it is shown that a machine learning algorithm trained with data from reference vessels are more accurate when estimating key performance indicators compared to existing empirical design formulas. Finally, the combination of the trained models with optimization algorithms shows to be a powerful tool for finding Pareto-optimal designs from which the naval architect can learn.

• All authors: Winter, R. de; Stein, B. van; Bäck, T.H.W.
• Editor(s): Bertram, V.
• Date: 2021
• Title of host publication: COMPIT'21
• Pages: 185 - 196
• ISBN (print): 9783892207245
• Link: http://data.hiper-conf.info/compit2021_muelheim.pdf
• Name: 20