ABSTRACT
Experimental results of sloshing model test have been investigated by data mining in the database of Seoul National University. More than 540 terabytes experimental data have been accumulated for various cargo holds, vessels, environmental conditions, operational conditions, and experimental conditions. The database was organized, cleaned, and analyzed. Model tests related to the floating units larger than standard size LNG carriers or LNG fueled vessels were selected. Then, the selected target data had been used for the artificial neural network to predict the model test results from the test conditions. The network successfully predicted the experimental results considering the various parameters.
Vessels carry many different liquids such as Liquefied Natural Gas (LNG), petroleum, and sea water in cargo tanks. When a vessel sails under the environmental loads, sloshing occurs in a tank containing partially filled liquid, which can cause serious damage to the structure. Therefore, when designing a ship, it is necessary to accurately estimate the sloshing loads (Faltinsen and Timokha, 2009; Malenica et al., 2017).
Recently, many studies have been conducted to estimate sloshing load. One reason is the increase in LNG volume and the rapid expansion of the LNG carrier construction market (BP, 2018; Kuo et al., 2009). LNG carriers have been enlarged for efficient transport. Since larger sloshing impact loads may occur in larger cargo holds, studies have been actively conducted to estimate them. With the expansion of the LNG carrier market, LNG fueled vessels have recently become one of the main products in the shipbuilding market (Congressional Research Service, 2019). Environmental regulations for ship operations have been tightened and LNG has begun to be used as a propulsion fuel for the vessels. Fuel tanks used in the LNG fueled vessels have a different shape from the cargo holds; therefore, partial loading conditions must be considered unlike the LNG carriers.
