Leveraging a Small Dataset to Predict Nonlinear Global Loads in Irregular Waves

Authors

  • Kyle E. Marlantes Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, USA
  • Kevin J. Maki Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, USA

DOI:

https://doi.org/10.59490/imdc.2024.873

Keywords:

Wave-induced, global loads, shear forces, bending moments, hybrid machine learning

Abstract

In this work, a hybrid machine learning method, which uses ML strategies to model high-order force components within a low-order equation of motion, is considered in the context of the global wave-induced loads of a ship in irregular waves. It is shown that the method can make predictions in a range of wave conditions even when the training data set only includes a single seaway. The proposed method offers a data-leveraging technique which may be useful in the design space, where a small data set derived from a high-fidelity source can be leveraged to make similar fidelity predictions in a larger number of wave conditions.

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Published

2024-05-23

How to Cite

Marlantes, K. E., & Maki, K. J. (2024). Leveraging a Small Dataset to Predict Nonlinear Global Loads in Irregular Waves. International Marine Design Conference. https://doi.org/10.59490/imdc.2024.873

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