Determination of cryogenic temperature loads for finite-element model of LNG bunkering ship under LNG release accident

The rising demand for liquefied natural gas (LNG)-fueled ships requires the LNG bunkering facility that partially uses a ship-to-ship operation. The bunkering process of LNG fuel may have a greater risk due to LNG volatility. The cryogenic temperature of LNG poses a threat to the personnel and structural embrittlement to ships. Therefore, cryogenic spill protection optimization was introduced concerning the structural strength analysis using finite element (FE) by utilizing cryogenic temperature loads provided by the computational fluid dynamics (CFD) model of an LNG release. This study aims to build a platform for transferring the temperature load profile from CFD to FE software accurately. The CFD model usually uses a structured Cartesian grid, and the FE method adopts an unstructured tetrahedral or hexahedral mesh. As a result, both configurations store results at different positions, and it is not preferred for the load profile to be transferred directly. The error will be greater due to the variance of positions. Random Forest, a machine learning method, has been employed that uses a regression technique to deal with a continuous variable. An accurate load profile for the FE model can be obtained by adopting decision tree learning in Random Forest. The procedure for determining the temperature load profile is presented in this article.

Automated summary

This study optimizes the cryogenic spill protection in the bunkering process of LNG-fueled ships using finite element (FE) analysis combined with computational fluid dynamics (CFD) modeling. A machine learning method, particularly decision tree learning and random forest techniques, is employed to accurately determine the temperature load profile and provide an effective platform for transferring the load profile.