The Wave Heights Distribution of Random Wave Based on Ocean Basin

*Nurman Firdaus  -  Indonesian Hydrodynamics Laboratory, Center for Hydrodynamics Technology, Agency for the Assessment and Application of Technology (BPPT), Indonesia
Baharuddin Ali scopus  -  Indonesian Hydrodynamics Laboratory, Center for Hydrodynamics Technology, Agency for the Assessment and Application of Technology (BPPT), Indonesia
Mochammad Nasir  -  Indonesian Hydrodynamics Laboratory, Center for Hydrodynamics Technology, Agency for the Assessment and Application of Technology (BPPT), Indonesia
M Muryadin  -  Indonesian Hydrodynamics Laboratory, Center for Hydrodynamics Technology, Agency for the Assessment and Application of Technology (BPPT), Indonesia
Received: 19 Jun 2020; Revised: 8 Sep 2020; Accepted: 1 Oct 2020; Published: 31 Oct 2020.
Open Access License URL: http://creativecommons.org/licenses/by-sa/4.0

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Abstract

The wave height parameter in ocean waves is one of the important information for a marine structure design. The present paper investigates the results of wave heights distribution from laboratory-generated for single sea state. Data of the random wave time series collected at the ocean basin are analyzed using the wave spectrum and compared with the theoretical spectrum in this study. The random wave data is varied with four sea states consisting of sea states 3, 4, 5 and 6 obtained from laboratory measurements. The parameter conditions of generated sea waves are represented by a value of significant wave height and wave peak period in the range of sea states. The individual wave heights data in each sea state are presented in the form of exceedance probability distribution and the predictions using a linear model. This study aims to estimate the wave heights distribution using the Rayleigh and Weibull distribution model. Furthermore, the accuracy of the wave heights distribution data's prediction results in each sea state has been compared and examined for both models. The applied linear models indicate similar and reasonable estimations on the observed data trends.

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