PhD candidate Kyle Marlantes for his National Science Foundation (NSF) Fellowship Award for the presentation of his work on hybrid force-correcting machine learning methods at the IACM 2023 Mechanic Machine Learning and Digital Engineering for Computational Science Engineering and Technology Conference. The work demonstrated how a force-correcting approach can be used to learn nonlinear damping,…
PhD candidate Kyle Marlantes for his National Science Foundation (NSF) Fellowship Award for the presentation of his work on hybrid force-correcting machine learning methods at the IACM 2023 Mechanic Machine Learning and Digital Engineering for Computational Science Engineering and Technology Conference.
The work demonstrated how a force-correcting approach can be used to learn nonlinear damping, restoring, and added mass terms from roll decay time series data, and in turn, improve the accuracy of predicted roll response amplitude operators (RAOs) which are commonly used by industry to perform seakeeping analysis on vessels. Roll is often the most important motion when it comes to ship operability and safety, but it is also one of the most difficult to predict accurately due to nonlinearity.
Read the paper now published in the Journal of Ocean Engineering. https://bit.ly/3WysTqG