Session: 06-05-04 Marine Hydrodynamics IV

Paper Number: 127214

127214 - Analyzing Experimental Data of 6-Dof Motions and Sloshing Impacts in Lng Carriers 

This research aims to illustrate the correlation between the 6-degree-of-freedom (DOF) motion of LNG carriers and the sloshing impact loads. HD Hyundai Heavy Industries has conducted sloshing model tests at the Seoul National University's sloshing model test facility for over a decade. More than 30 different ship models underwent sloshing model experiments, resulting in a database built from tests under over 5,000 varying maritime environmental and operational conditions. All experimental data were collected using more than 100 Piezo electric sensors, measuring sloshing impact pressures in real-ship scale over a duration of 5 hours. The time series of the vessel's 6-DOF motions is also recorded. From the impact pressure time series, post-processing is undertaken to store the most probable maximum value of the sloshing impact pressure following the 3-parameter Weibull distribution. Based on the database, when an LNG carrier experiences a sloshing impact load, we analyzed the correlation between the 18 features of the 6-DOF motion's displacement, velocity, and acceleration and the sloshing impact pressure. Machine learning was employed to determine the importance of each feature through linear and nonlinear analyses. Using elimination methods, we identified parameters among the 18 features that could be synchronized to influence the sloshing impact pressure and discerned their interrelations. This study successfully clarified the ship motion factors affecting sloshing impact loads. Moreover, foundational data that can be used to navigate ships considering sloshing impact loads during operation were secured.

Presenting Author: Yangjun Ahn Sungshin Women's University

Presenting Author Biography: Professor Yangjun Ahn conducted sloshing model tests for 10 years at Seoul National University and Hanwha Ocean Engineering. He is currently majoring in artificial intelligence at Sungshin Women's University's AI department.

Authors:

Yangjun Ahn Sungshin Women's University
Mun-Gwan Choi HD Hyundai Heavy Industries
Cheonjin Park HD Hyundai Heavy Industries
Sang-Yeob Kim Korean Register
Taehyun Park Seoul National University
Woohyeon Shim Sungshin Women's University
Gyeongbin Ryoo Sungshin Women's University