Journal of Advanced Research in Ocean Engineering

Korean Society of Ocean Engineers (한국해양공학회)
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Prediction of Extreme Sloshing Pressure Using Different Statistical Models

  • Cetin, Ekin Ceyda (Department of Naval Architecture & Ocean Engineering, Seoul National University) ;
  • Lee, Jeoungkyu (Department of Naval Architecture & Ocean Engineering, Seoul National University) ;
  • Kim, Sangyeob (Department of Naval Architecture & Ocean Engineering, Seoul National University) ;
  • Kim, Yonghwan (Department of Naval Architecture & Ocean Engineering, Seoul National University)
  • Received : 2018.10.11
  • Accepted : 2018.11.28
  • Published : 2018.12.31
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Abstract

In this study, the extreme sloshing pressure was predicted using various statistical models: three-parameter Weibull distribution, generalized Pareto distribution, generalized extreme value distribution, and three-parameter log-logistic distribution. The estimation of sloshing impact pressure is important in design of liquid cargo tank in severe sea state. In order to get the extreme values of local impact pressures, a lot of model tests have been carried out and statistical analysis has been performed. Three-parameter Weibull distribution and generalized Pareto distribution are widely used as the statistical analysis method in sloshing phenomenon, but generalized extreme value distribution and three-parameter log-logistic distribution are added in this study. Additionally, statistical distributions are fitted to peak pressure data using three different parameter estimation methods. The data were obtained from a three-dimensional sloshing model text conducted at Seoul National University. The loading conditions were 20%, 50%, and 95% of tank height, and the analysis was performed based on the measured impact pressure on four significant panels with large sloshing impacts. These fittings were compared by observing probability of exceedance diagrams and probability plot correlation coefficient test for goodness-of-fit.

Keywords

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Fig. 1 Experiment setup

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Fig. 3 POE diagrams of 0.20H, P.19, No.5

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Fig. 2 Layout of sensor cluster panels

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Fig. 4 POE curves for 5-hour test

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Fig. 6 POE curves of 5-hour experiment(left, 0.20H, P.14, No.09) and 100-hour experiment(right)

Table 1. Parameter estimation method for distribution and notation for each fit

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Table 2. Plotting position formulas

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Table 3. Test conditions

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Table 4. Selected four panels according to load condition

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Table 5. PPCC test results of (0.20H, P.19, No.5)

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Table 6. PPCC test results for whole data

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Fig. 5 POE curves that show the difference of GP

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Table 7. PPCC test results of tail-only data

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Table 8. PPCC test result for 100 hours of whole data and tail-only data

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