Determination of Correlation for Extreme Metocean Variables

Metocean environmental load includes wind, wave and currents. Offshore structures are designed for two environmental load design conditions i.e. extreme and operational load conditions of environmental loads are evaluated. The ccorrelation between load variables using Joint probability distribution, Pearson correlation coefficient and Spearman’s rank correlation coefficients methods in Peninsular Malaysia (PM), Sabah and Sarawak are computed. Joint probability distribution method is considered as a reliable method among three different methods to determine the relationship between load variables. The PM has good correlation between the wind-wave and wave-current; Sabah has both strong relationships of wind-wave and wind-current with 50 year return period; Sarawak has good correlation between wind and current in both 50 years and 100 years return period. Since Sabah has good correlation between the associated load variables, no matter in 50 years or 100 years of return period of load combination. Thus, method 1 of ISO 19901-1, specimen provides guideline for metocean loading conditions, can be adopted for design for offshore structure in Sabah. However, due to weak correlations in PM and Sarawak, this method cannot be applied and method 2, which is current practice in offshore industry, should continueto be used.

[1]  S. Christian Albright,et al.  Data Analysis & Decision Making with Microsoft Excel- Text Only , 2006 .

[2]  M. S. Liew,et al.  Statistical modelling of environmental load uncertainty for jacket platforms in Malaysia , 2012, 2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER).

[3]  J. N. Sharma,et al.  A Comprehensive Wind , Wave and Current Measurement Program in the South China Sea , 2010 .

[4]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[5]  Jan Hauke,et al.  Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data , 2011 .

[6]  M. K. Abu Husain,et al.  Efficient Derivation of the Probability Distribution of Extreme Responses due to Random Wave Loading From the Probability Distribution of Extreme Surface Elevations , 2013 .

[7]  Eugenio Carminati,et al.  Apennines subduction‐related subsidence of Venice (Italy) , 2003 .

[8]  Nian Shong Chok PEARSON'S VERSUS SPEARMAN'S AND KENDALL'S CORRELATION COEFFICIENTS FOR CONTINUOUS DATA , 2010 .

[9]  Subrata K. Chakrabarti,et al.  Hydrodynamics of Offshore Structures , 1987 .

[10]  Philip Jonathan,et al.  Statistical modelling of extreme ocean environments for marine design: A review , 2013 .

[11]  Elzbieta M. Bitner-Gregersen,et al.  Joint met-ocean description for design and operations of marine structures , 2015 .

[12]  Yiquan Qi,et al.  Extreme Wind, Wave And Current In Deep Water of South China Sea , 2010 .

[13]  Philip Jonathan,et al.  Evaluating environmental joint extremes for the offshore industry , 2012, 1211.1365.

[14]  Michael C. Johnson,et al.  Some uncertainties associated with wind and wave description and their importance for engineering applications , 2014 .

[15]  Chih-Pei Chang,et al.  Typhoon Vamei: An equatorial tropical cyclone formation , 2003 .

[16]  Kevin Ewans,et al.  Statistical estimation of extreme ocean environments: The requirement for modelling directionality and other covariate effects , 2008 .

[17]  Chua Yan Piaw,et al.  Mastering Research Statistics , 2013 .