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국회도서관 홈으로 정보검색 소장정보 검색
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목차

1. Introduction 13

2. Experimental Setup 15

2.1. Motion Platform 15

2.2. Model Tank 18

2.3. Data Acquisition System & Pressure Gauge 19

3. Experiment 22

4. Peak Detection Scheme 24

5. Probabilistic Analysis 26

5.1. Probability Density Function 26

5.1.1. 2-parameter Log-normal 28

5.1.2. 3-parameter Log-normal 31

5.1.3. 2-parameter Weibull 33

5.1.4. 3-Parameter Weibull 36

5.1.5. Generalized Pareto 40

5.2. χ² test 42

5.3. Verification of Results 44

6. Conclusion 78

참고문헌 80

Abstract 82

표목차

Table 2.1. Specification of Actuator 16

Table 2.2. Range of Displacement & Velocity 17

Table 2.3. Shape and Dimension of Model Tank 18

Table 2.4. Pressure Sensor Data Sheet (Kistler 701A) 19

Table 2.5. Amplifier Data Sheet (Kistler 5037B) 20

Table 2.6. Amplifier Data Sheet(Kistler 5011B) 20

Table 2.7. Specification of PXI-4472B 21

Table 5.1. χ² Test 43

Table 5.2. χ² Test (138K 10%H) 47

Table 5.3. χ² Test (138K 10%L) 47

Table 5.4. χ² Test (138K 70%H) 47

Table 5.5. χ² Test (138K 80%H) 48

Table 5.6. χ² Test (138K 95%H) 48

Table 5.7. χ² Test (Larger Model 10%L) 48

Table 5.8. χ² Test (Larger Model 30%H) 49

Table 5.9. χ² Test (Larger Model 50%H) 49

Table 5.10. χ² Test (The Largest Model 10%H) 49

Table 5.11. χ² Test (The Largest Model 70%H) 50

Table 5.12. χ² Test (The Largest Model 80%H) 50

Table 5.13. χ² Test (The Largest Model 95%H) 50

Table 5.14. χ² Test (Overall) 51

Table 5.15. Extreme Value (Threshold ＝ 0.005) 75

Table 5.16. Extreme Value (Threshold ＝ 0.01) 76

Table 5.17. Extreme Value (Threshold ＝ 0.02) 77

그림목차

Fig. 2.1. 6-DOF Sloshing Motion Platform 15

Fig. 2.2. Model Tank 18

Fig. 2.3. Calibration Graph for Pressure Sensor... 21

Fig. 3.1. Schematic Diagram of Experimental System 23

Fig. 4.1. Peak Detection Flowchart 24

Fig. 4.2. Schematic for Pressure Peak Detection Procedure 25

Fig. 5.1. Location Parameter Dependancy 27

Fig. 5.2. Scale Parameter Dependancy 27

Fig. 5.3. Shape Parameter Dependancy 27

Fig. 5.4. Shape of Weight Function 32

Fig. 5.5. Goodness-of-fit 42

Fig. 5.6. Normalized Probability Exceedence 46

Fig. 5.7. Normalized Probability Exceedence 52

Fig. 5.8. Normalized Probability Exceedence... 53

Fig. 5.9. Normalized Probability Exceedence... 53

Fig. 5.10. Normalized Probability Exceedence... 54

Fig. 5.11. Zoom up of Fig. 5.10 54

Fig. 5.12. 2-Parameter Log-normal (Threshold Value : 0.005) 56

Fig. 5.13. 2-Parameter Log-normal (Threshold Value : 0.005) 56

Fig. 5.14. 2-Parameter Log-normal (Threshold Value : 0.005) 56

Fig. 5.15. 2-Parameter Log-normal (Threshold Value : 0.005) 56

Fig. 5.16. 2-Parameter Log-normal (Threshold Value : 0.005) 56

Fig. 5.17. 2-Parameter Log-normal (Threshold Value : 0.005) 56

Fig. 5.18. 2-Parameter Log-normal (Threshold Value : 0.01) 57

Fig. 5.19. 2-Parameter Log-normal (Threshold Value : 0.01) 57

Fig. 5.20. 2-Parameter Log-normal (Threshold Value : 0.01) 57

Fig. 5.21. 2-Parameter Log-normal (Threshold Value : 0.01) 57

Fig. 5.22. 2-Parameter Log-normal (Threshold Value : 0.01) 57

Fig. 5.23. 2-Parameter Log-normal (Threshold Value : 0.01) 57

Fig. 5.24. 2-Parameter Log-normal (Threshold Value : 0.02) 58

Fig. 5.25. 2-Parameter Log-normal (Threshold Value : 0.02) 58

Fig. 5.26. 2-Parameter Log-normal (Threshold Value : 0.02) 58

Fig. 5.27. 2-Parameter Log-normal (Threshold Value : 0.02) 58

Fig. 5.28. 2-Parameter Log-normal (Threshold Value : 0.02) 58

Fig. 5.29. 2-Parameter Log-normal (Threshold Value : 0.02) 58

Fig. 5.30. 3-Parameter Log-normal (Threshold Value : 0.005) 59

Fig. 5.31. 3-Parameter Log-normal (Threshold Value : 0.005) 59

Fig. 5.32. 3-Parameter Log-normal (Threshold Value : 0.005) 59

Fig. 5.33. 3-Parameter Log-normal (Threshold Value : 0.005) 59

Fig. 5.34. 3-Parameter Log-normal (Threshold Value : 0.005) 59

Fig. 5.35. 3-Parameter Log-normal (Threshold Value : 0.005) 59

Fig. 5.36. 3-Parameter Log-normal (Threshold Value : 0.01) 60

Fig. 5.37. 3-Parameter Log-normal (Threshold Value : 0.01) 60

Fig. 5.38. 3-Parameter Log-normal (Threshold Value : 0.01) 60

Fig. 5.39. 3-Parameter Log-normal (Threshold Value : 0.01) 60

Fig. 5.40. 3-Parameter Log-normal (Threshold Value : 0.01) 60

Fig. 5.41. 3-Parameter Log-normal (Threshold Value : 0.01) 60

Fig. 5.42. 3-Parameter Log-normal (Threshold Value : 0.02) 61

Fig. 5.43. 3-Parameter Log-normal (Threshold Value : 0.02) 61

Fig. 5.44. 3-Parameter Log-normal (Threshold Value : 0.02) 61

Fig. 5.45. 3-Parameter Log-normal (Threshold Value : 0.02) 61

Fig. 5.46. 3-Parameter Log-normal (Threshold Value : 0.02) 61

Fig. 5.47. 3-Parameter Log-normal (Threshold Value : 0.02) 61

Fig. 5.48. 2-Parameter Weibull (Threshold Value : 0.005) 62

Fig. 5.49. 2-Parameter Weibull (Threshold Value : 0.005) 62

Fig. 5.50. 2-Parameter Weibull (Threshold Value : 0.005) 62

Fig. 5.51. 2-Parameter Weibull (Threshold Value : 0.005) 62

Fig. 5.52. 2-Parameter Weibull (Threshold Value : 0.005) 62

Fig. 5.53. 2-Parameter Weibull (Threshold Value : 0.005) 62

Fig. 5.54. 2-Parameter Weibull (Threshold Value : 0.01) 63

Fig. 5.55. 2-Parameter Weibull (Threshold Value : 0.01) 63

Fig. 5.56. 2-Parameter Weibull (Threshold Value : 0.01) 63

Fig. 5.57. 2-Parameter Weibull (Threshold Value : 0.01) 63

Fig. 5.58. 2-Parameter Weibull (Threshold Value : 0.01) 63

Fig. 5.59. 2-Parameter Weibull (Threshold Value : 0.01) 63

Fig. 5.60. 2-Parameter Weibull (Threshold Value : 0.02) 64

Fig. 5.61. 2-Parameter Weibull (Threshold Value : 0.02) 64

Fig. 5.62. 2-Parameter Weibull (Threshold Value : 0.02) 64

Fig. 5.63. 2-Parameter Weibull (Threshold Value : 0.02) 64

Fig. 5.64. 2-Parameter Weibull (Threshold Value : 0.02) 64

Fig. 5.65. 2-Parameter Weibull (Threshold Value : 0.02) 64

Fig. 5.66. 3-Parameter Weibull with Maximum Likelihood Estimation... 65

Fig. 5.67. 3-Parameter Weibull with Maximum Likelihood Estimation... 65

Fig. 5.68. 3-Parameter Weibull with Maximum Likelihood Estimation... 65

Fig. 5.69. 3-Parameter Weibull with Maximum Likelihood Estimation... 65

Fig. 5.70. 3-Parameter Weibull with Maximum Likelihood Estimation... 65

Fig. 5.71. 3-Parameter Weibull with Maximum Likelihood Estimation... 65

Fig. 5.72. 3-Parameter Weibull with Maximum Likelihood Estimation... 66

Fig. 5.73. 3-Parameter Weibull with Maximum Likelihood Estimation... 66

Fig. 5.74. 3-Parameter Weibull with Maximum Likelihood Estimation... 66

Fig. 5.75. 3-Parameter Weibull with Maximum Likelihood Estimation... 66

Fig. 5.76. 3-Parameter Weibull with Maximum Likelihood Estimation... 66

Fig. 5.77. 3-Parameter Weibull with Maximum Likelihood Estimation... 66

Fig. 5.78. 3-Parameter Weibull with Maximum Likelihood Estimation... 67

Fig. 5.79. 3-Parameter Weibull with Maximum Likelihood Estimation... 67

Fig. 5.80. 3-Parameter Weibull with Maximum Likelihood Estimation... 67

Fig. 5.81. 3-Parameter Weibull with Maximum Likelihood Estimation... 67

Fig. 5.82. 3-Parameter Weibull with Maximum Likelihood Estimation... 67

Fig. 5.83. 3-Parameter Weibull with Maximum Likelihood Estimation... 67

Fig. 5.84. 3-Parameter Weibull with Method of Moment... 68

Fig. 5.85. 3-Parameter Weibull with Method of Moment... 68

Fig. 5.86. 3-Parameter Weibull with Method of Moment... 68

Fig. 5.87. 3-Parameter Weibull with Method of Moment... 68

Fig. 5.88. 3-Parameter Weibull with Method of Moment... 68

Fig. 5.89. 3-Parameter Weibull with Method of Moment... 68

Fig. 5.90. 3-Parameter Weibull with Method of Moment... 69

Fig. 5.91. 3-Parameter Weibull with Method of Moment... 69

Fig. 5.92. 3-Parameter Weibull with Method of Moment... 69

Fig. 5.93. 3-Parameter Weibull with Method of Moment... 69

Fig. 5.94. 3-Parameter Weibull with Method of Moment... 69

Fig. 5.95. 3-Parameter Weibull with Method of Moment... 69

Fig. 5.96. 3-Parameter Weibull with Method of Moment... 70

Fig. 5.97. 3-Parameter Weibull with Method of Moment... 70

Fig. 5.98. 3-Parameter Weibull with Method of Moment... 70

Fig. 5.99. 3-Parameter Weibull with Method of Moment... 70

Fig. 5.100. 3-Parameter Weibull with Method of Moment... 70

Fig. 5.101. 3-Parameter Weibull with Method of Moment... 70

Fig. 5.102. Generalized Pareto (Threshold Value : 0.005) 71

Fig. 5.103. Generalized Pareto (Threshold Value : 0.005) 71

Fig. 5.104. Generalized Pareto (Threshold Value : 0.005) 71

Fig. 5.105. Generalized Pareto (Threshold Value : 0.005) 71

Fig. 5.106. Generalized Pareto (Threshold Value : 0.005) 71

Fig. 5.107. Generalized Pareto (Threshold Value : 0.005) 71

Fig. 5.108. Generalized Pareto (Threshold Value : 0.01) 72

Fig. 5.109. Generalized Pareto (Threshold Value : 0.01) 72

Fig. 5.110. Generalized Pareto (Threshold Value : 0.01) 72

Fig. 5.111. Generalized Pareto (Threshold Value : 0.01) 72

Fig. 5.112. Generalized Pareto (Threshold Value : 0.01) 72

Fig. 5.113. Generalized Pareto (Threshold Value : 0.01) 72

Fig. 5.114. Generalized Pareto (Threshold Value : 0.02) 73

Fig. 5.115. Generalized Pareto (Threshold Value : 0.02) 73

Fig. 5.116. Generalized Pareto (Threshold Value : 0.02) 73

Fig. 5.117. Generalized Pareto (Threshold Value : 0.02) 73

Fig. 5.118. Generalized Pareto (Threshold Value : 0.02) 73

Fig. 5.119. Generalized Pareto (Threshold Value : 0.02) 73

초록보기

This paper presents the stochastic approach on the impact pressure acted on the LNGC tank model tank wall. The experiments were done with 6 degree of freedom motion platform. The purpose of the experiment is to establish the design procedure of estimation of impact pressure on the LNGC tank whose capacity is larger than 138,000㎥. The tank models tested were No. 2 tank of LNGC which are larger than 138,000㎥ tank. The experiments can be classified into screening test and critical test. For both of the test cases the ship motion reflects the critical real sea simulation. The sampling frequency used was 20 kHz and 27 pressure gauges were used. As a result the total data acquired are equivalent to 30 Giga bytes for the critical test. The analysis of this tremendous amount of data can be done only by stochastic analysis. The final output we need is the maximum impact pressure equivalent to real scale 3 hour return period.

This can be done by estimating an exceedence probability function. The exceedence probability function can be derived from the probability density function. The choice of the right probability density functions is a very important issue. There are many kinds of probability density functions. Five probability density functions are studied in this study. They are two parameter Log-normal distribution, three parameter Log-normal distribution, two parameter Weibull distribution, three parameter Wei bull distribution, and Generalized Pareto distribution. When it comes to the three parameter Wei bull distribution, two approaches are studied to estimate the parameters. This leads to 6 different ways of analysis. The results showed that 3 parameter Wei bull distribution whose parameters are calculated by moment method was turned out to be the most efficient one. The next one was the Generalized Pareto. Rest of the methods are believed to yield large errors when they are compared with the experimental data.

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