Artificial neural network based prediction of ultimate buckling strength of liquid natural gas cargo containment system under sloshing loads considering onboard boundary conditions

• Published in: Ocean engineering 2022-04, Vol.249, p.110981, Article 110981
• Main Authors: Park, Young IL, Kim, Jeong-Hwan
• Format: Article
• Language: English
• Subjects: Artificial neural network (ANN); LNG cargo containment system; Nonlinear FE simulations; Sloshing; Ultimate buckling strength
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2022.110981

In this study, the ultimate buckling strength of a GTT NO96 liquid natural gas (LNG) cargo containment system under a sloshing impact load was investigated while considering the design environmental conditions thereof, such as the cryogenic temperature and flexible boundary conditions owing to the inner hull structures. As the dynamic buckling capacities of LNG cargo containment boxes show a large nonlinearity against several design parameters, we applied an artificial neural network to generalize the numerical analysis results and develop design ultimate capacities of the system for early design purposes. Through one-hot encoding, a character variable was converted into a variable form suitable for the artificial neural network, and a prediction model optimized for the current system was constructed through a model optimization process. A comparison of the design values with finite element (FE) simulation results shows a very good agreement. Finally, we provide a prediction model: a GitHub repository, BUNO96, which is suitable for the calculation of ultimate buckling strength of NO96 box. •The ultimate buckling strength of a GTT NO96 liquid natural gas (LNG) cargo containment system under a sloshing impact load was investigated while considering the design environmental conditions.•A machine learning-based prediction model was developed based on the corresponding numerical analysis results.•The model was trained by various parameters (i.e. type, temperature, boundary condition, load profile and rise time) to investigate their influence.