Date of Award
Doctor of Philosophy (PhD)
Ayodeji O. Demuren
Planing craft operating in waves at high speeds can experience high, repetitive vertical accelerations that are random and nonlinear in relation to the sea condition. A proper understanding of vertical accelerations is critical to meet statistically based criteria for structural design, habitability, and equipment selection. Historically, it was assumed that planing craft vertical accelerations fit the Exponential distribution, and design methods adopted this conclusion. However, several published papers have raised doubts regarding the accuracy and validity of this Exponential distribution assumption.
The statistical behavior of planing craft vertical accelerations are examined for the Parent and Peak data sets from twenty-eight (28) full-scale and model-scale tests of different hulls operating in irregular waves. Comparisons are made with Exponential, Rayleigh, Gumbel, and Lognormal distributions. Sensitivity studies regarding Peak Identification methods and threshold values are considered. Methods to extend legacy data, including the use of the Monte Carlo simulation technique and correlations between statistical parameters of Parent data sets and Peak data sets are examined.
The results of this research prove that the Exponential distribution is not appropriate for Peak or Parent vertical accelerations. For modern planing craft, the best fit for both the Peak and Parent vertical accelerations is the Gumbel distribution. The Monte Carlo method proved to be accurate in simulating the experimental data using the Gumbel distribution and only limited knowledge of the statistics of the experimental data. A strong linear correlation was found between statistical parameters of the Parent and Peak data sets and relationships are provided as guidance to planing craft designers. Additional statistical values, including Probability of Exceedance values, are included.
Grimsley, Jennifer S..
"Methodology to Quantify Vertical Accelerations of Planing Craft in Irregular Waves"
(2010). Doctor of Philosophy (PhD), dissertation, Mechanical Engineering, Old Dominion University, DOI: 10.25777/n2n4-nm53