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TECHNICAL PAPERS

Probabilistic Analysis of Characteristic Pressure for LNG Tanks

[+] Author and Article Information
Mateusz Graczyk

Centre for Ships and Ocean Structures, Norwegian University of Science and Technology, Department of Marine Technology, Otto Nielsens vei 10, 7491 Trondheim, Norwaymateusz@ntnu.no

Torgeir Moan

Centre for Ships and Ocean Structures, Norwegian University of Science and Technology, Department of Marine Technology, Otto Nielsens vei 10, 7491 Trondheim, Norwaytormo@ntnu.no

Olav Rognebakke

Centre for Ships and Ocean Structures, Norwegian University of Science and Technology, Department of Marine Technology, Otto Nielsens vei 10, 7491 Trondheim, Norwayolarogn@ntnu.no

J. Offshore Mech. Arct. Eng 128(2), 133-144 (Nov 08, 2005) (12 pages) doi:10.1115/1.2185128 History: Received May 15, 2005; Revised November 08, 2005

Background. New challenges in LNG shipping, such as ship size growth, trading routes with more severe weather conditions, need for operating with unrestricted filling level and new propulsion systems attract very much attention in marine and offshore oriented community. One of the main concerns is the prediction of loads caused by violent fluid motion in cargo tanks. In the paper we address the problem of determining characteristic extreme values of sloshing pressures for structural design. This involves estimating ship motion in a long-term period, fluid motion in the tank, excited pressures, and relevant structural responses. Method of Approach. Ship motion analysis is based on linear strip theory. In order to investigate the dependence of the sloshing response on sea conditions, an approach based on statistical characteristics of the tank motion is utilized as well as a multimodal approach for fluid motion in a tank. However, an appropriate theoretical/numerical approach, which can be used for a realistic prediction of the most extreme pressure has not yet been developed. Thus, experiments are utilized for the most severe sea states for a chosen tank filling level. Results. Our main contribution in the paper includes the statistical analysis of experimental short term pressure distribution. The choice and fit of probability distribution models is addressed, with due account of different physical mechanisms causing impacts. The models are evaluated. The most critical tank areas for sloshing loads are briefly discussed. Appropriate dynamic response of the tank structure needs to be investigated by accounting for temporal and spatial distribution of sloshing loads. These two factors are also addressed in the paper. The variability of results obtained by processing data from multiple test runs is discussed. Conclusions. The three-parameter Weibull and generalized Pareto statistical models are fitted to the data and evaluated. They prove to accurately describe sloshing excited pressures. However, the highest data points are underestimated by the both distributions. Generalized Pareto model results in more conservative estimates. The threshold level of peaks used in a fit of generalized Pareto distribution was investigated. Based on this, it is set to a level of a 0.85–0.87 quantile of peak values. The big influence of spatial and temporal distribution on the estimates is reported. Uncertainty in measured pressures originating from inherent fluid motion variability exceeds the uncertainty resulting form ship motions' variability. Moreover, generalized Pareto model results in higher variability.

Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Standard deviation of the tank center acceleration in pitch for combination of sea states and wave headings. The most critical conditions for headings less than 30° marked with an ellipse

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Figure 2

Calculated impact pressures

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Figure 3

Distribution of the ten highest impact pressures

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Figure 4

Tank model and location of panel pressure sensors; internal dimensions, full scale, mm. A sketch of Technigaz containment system Mark III

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Figure 5

Results of experimental critical sea states analysis

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Figure 6

A peak registered on the center panel during the test in head sea, as an example of hydrodynamic impact; values full scale in real time

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Figure 7

The second highest peak registered during the tests in head sea, as an example of air cushioned impact; values full scale in real time

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Figure 8

Estimates of characteristic extreme value obtained from generalized Pareto model with the threshold level as parameter

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Figure 9

POE by the Weibull (—) and generalized Pareto (- - -) models with sample data (∘) and the 90% fractile level of maximum pressure in a 3h period for a sample A1B1C1D1; evaluation of the models by probability papers

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Figure 10

POE by the Weibull (—) and generalized Pareto (- - -) models with sample data (∘) and a 90% fractile level of maximum pressure in a 3h period for a sample A1B1C1D2; evaluation of the models by probability papers

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Figure 11

POE by the Weibull (—) and generalized Pareto (- - -) models with sample data (∘) and a 90% fractile level of maximum pressure in a 3h period for a sample A1B1C1D3; an evaluation of the models by probability papers

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Figure 12

POE by the Weibull (—) and generalized Pareto (- - -) models with sample data (∘) and a 90% fractile level of maximum pressure in a 3h period for a sample A1B1C3D1; evaluation of the models by probability papers

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Figure 13

POE by the Weibull (—) and generalized Pareto (- - -) models with sample data (∘) and a 90% fractile level of maximum pressure in a 3h period for a sample A1B2C1D1; evaluation of the models by probability papers

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Figure 14

POE by the Weibull (—) and generalized Pareto (- - -) models with sample data (∘) and a 90% fractile level of maximum pressure in a 3h period for a sample A1B2C3D1; evaluation of the models by probability papers

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Figure 15

Visualization of spatial distribution of a high pressure peak; ordinate—measured pressure, kPa

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