THE LIFE RAFT SAFETY MODEL
SENSITIVITY STUDIES
Smolarek L.
Gdynia Maritime University, Gdynia, Poland
Abstract: Simulation modeling provides an effective and powerful approach for capturing and
analyzing the life raft system [10]. The safety analysis can be based on computer generated data derived from simulation. This paper presents the sensitivity studies of the safety model that was developed to estimate the life raft safety parameters and to classify the hazard events importance.
1. Introduction
1Scientific progress based on theoretical analysis (deduction) and empirical analysis. The computer simulation is the third way of doing science.
The first known computer simulation was used to design the atomic bomb in the Manhattan Project during World War II. The complex system of equation used in design could not be solved analytically and the experiment was impossibly because there was not enough fissionable material (at the time) even for one atomic explosion, [5].
The sensitivity of the safety to a particular input parameter is defined as the partial derivative of the safety with respect to that parameter. This measure can be used to estimate the effect of parameter changes on the model result, without necessitating a full model solution for each parameter change [1].
Several measures have been defined to assess component importance, in an attempt to capture the combined effect of environment and probabilistic contributions to system safety from basic parameters. The importance measure, which is defined as the partial derivative, attributable to Birnbaum, has remained popular. Birnbaum's measure of importance of component k at time t (with m components) is defined as [12]
1 This research was conducted within the research grant number 4T12C 03827 financially
2. Safety model description and input
A computer simulation is a computational model of life raft behavior coupled with an experiment results. At first a description of the model needs to be presented. The safety model that is developed consists of four modules.
In first module the input data are collected and survivor loading is estimated. According to lack of information the number of survivors at the life raft and their deployment should be modeled by random variable [11].
Table 1. The input parameters
Wave-param. Wind-param. Life raft-param. Drogue-param. Line
H 1,5 V 20 M 855 N 25 L 25 RB DD 1,2 KK 200 Num.surv. 10 0 X2 18 X1 1 V2 0,4 V1 0,4 t 0,05 typ T0 0,1 T1 2,55
Fig. 1. The probability distribution of survivor’s number at the life raft
At the second module life raft stability parameters for static and dynamic cases are count using the finish element method [9].
.
Fig. 3. Critical angles (Φ-bottom above water, γ- life raft board in water) and vertical position of center of gravity G – example for 10-life raft [7]
The third module is used to estimate the probability distribution of the life raft heeling, rolling, acceleration, and pitching parameters according to wave and wind characteristic, [8].
Fig. 4. The life raft motion parameters (heeling, rolling, acceleration, pitching) – example for 10-life raft
Fig. 5. Characteristic parameters for random generated wave
The fourth module is used to estimate the probability of occurrence of the life raft failure, [10].
Fig. 6. The safety model graph
Fig. 7. The example of safety process realizations, state frequency histograms (failure type capsizing [7,8]) and final state frequency histogram
Fig. 8. The dependency between capsizing angle and survivor’s deployment
During the sensitivity analysis the initial parameters estimations were taken as reference values and the parameters perturbations were chosen as realistic ranges for the data set.
Fig. 10. The fluctuation of life raft rolling angle according to wave high changes
Fig. 11. The life raft failure probability as a function of parameter model .
4. Conclusion
Computer simulation can be a powerful approach to analyzing a life raft risk and safety. Simulation makes it possible to study situations that are may be impossible to test in real conditions. The simulation process can be repeated with different parameters variations. After completing the simulation run further analysis can be proceed. In stochastic models the simulation outcomes will vary from run to run so the general characteristic like
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