摘要
Because of the intrinsic difficulty in determining distributions for wave periods, previous studies on wave period distribution models have not taken nonlinearity into account and have not performed well in terms of describing and statistically analyzing the probability density distribution of ocean waves. In this study, a statistical model of random waves is developed using Stokes wave theory of water wave dynamics. In addition, a new nonlinear probability distribution function for the wave period is presented with the parameters of spectral density width and nonlinear wave steepness, which is more reasonable as a physical mechanism. The magnitude of wave steepness determines the intensity of the nonlinear effect, while the spectral width only changes the energy distribution. The wave steepness is found to be an important parameter in terms of not only dynamics but also statistics. The value of wave steepness reflects the degree that the wave period distribution skews from the Cauchy distribution, and it also describes the variation in the distribution function, which resembles that of the wave surface elevation distribution and wave height distribution. We found that the distribution curves skew leftward and upward as the wave steepness increases. The wave period observations for the SZFII-1 buoy, made off the coast of Weihai （37°27.6′N, 122°15.1′ E）, China, are used to verify the new distribution. The coefficient of the correlation between the new distribution and the buoy data at different spectral widths （v=0.3-3.5） is within the range of 0.9686 to 0.991 7. In addition, the Longuet-Higgins （1975） and Sun （1988） distributions and the new distribution presented in this work are compared. The validations and comparisons indicate that the new nonlinear probability density distribution fits the buoy measurements better than the Longuet-Higgins and Sun distributions do. We believe that adoption of the new wave period distribution would improve traditional statistical wave theory.
Because of the intrinsic difficulty in determining distributions for wave periods,previous studies on wave period distribution models have not taken nonlinearity into account and have not performed well in terms of describing and statistically analyzing the probability density distribution of ocean waves.In this study,a statistical model of random waves is developed using Stokes wave theory of water wave dynamics.In addition,a new nonlinear probability distribution function for the wave period is presented with the parameters of spectral density width and nonlinear wave steepness,which is more reasonable as a physical mechanism.The magnitude of wave steepness determines the intensity of the nonlinear effect,while the spectral width only changes the energy distribution.The wave steepness is found to be an important parameter in terms of not only dynamics but also statistics.The value of wave steepness reflects the degree that the wave period distribution skews from the Cauchy distribution,and it also describes the variation in the distribution function,which resembles that of the wave surface elevation distribution and wave height distribution.We found that the distribution curves skew leftward and upward as the wave steepness increases.The wave period observations for the SZFII-1 buoy,made off the coast of Weihai (37°27.6' N,122°15.1' E),China,are used to verify the new distribution.The coefficient of the correlation between the new distribution and the buoy data at different spectral widths (ν=0.3-0.5) is within the range of 0.968 6 to 0.991 7.In addition,the Longuet-Higgins (1975) and Sun (1988) distributions and the new distribution presented in this work are compared.The validations and comparisons indicate that the new nonlinear probability density distribution fits the buoy measurements better than the Longuet-Higgins and Sun distributions do.We believe that adoption of the new wave period distribution would improve traditional statistical wave theory.
基金
Supported by the National Natural Science Foundation of China (No.40476018)
the Knowledge Innovation Program of Chinese Academy of Sciences (KZCX2-YW201)
