In this study, we developed the first linear Joint North Sea Wave Project(JONSWAP) spectrum(JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient...In this study, we developed the first linear Joint North Sea Wave Project(JONSWAP) spectrum(JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging(LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin(LH) random-phase method to generate the time series of wave records and used the fast Fourier transform(FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors(wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.展开更多
- This paper presents the method of determining JONSWAP spectrum by using measured wave data. If Hs, Tz and Tc are the measured significant wave height, average zero-up crossing wave period and average period between ...- This paper presents the method of determining JONSWAP spectrum by using measured wave data. If Hs, Tz and Tc are the measured significant wave height, average zero-up crossing wave period and average period between wave crests respectively and let y = T Z / TC; this paper provides equation to solve y fromf(y,y) = 0. From the solutions of this equation and by using LSM, the expression relating y as a function of y (for 2.54<y< 15.34 and 1.6 <y < 1,79) may be written as y -5546.721 - 9586.533y + 5568.168/- 1089/+ 2/, for other intervals the related formulas are also given. When y is known, the rest of parameters in JONSWAP spectrum can be obtained. In addition, this paper also provides alternatives for determining JONSWAP spectral parameters by using Hs, Tz and (, or Hs, Tz and S(w0) or other three given data. The JONSWAP spectra given in this paper satisfy the following formulas HS= 4.0 = 2 Tc = 2展开更多
Numerical simulations of freak wave generation are studied in random oceanic sea states described by JONSWAP spectrum. The evolution of initial random wave trains is namerically carried out within the framework of the...Numerical simulations of freak wave generation are studied in random oceanic sea states described by JONSWAP spectrum. The evolution of initial random wave trains is namerically carried out within the framework of the modified fourorder nonlinear Schroedinger equation （mNLSE）, and some involved influence factors are also discussed. Results show that if the sideband instability is satisfied, a random wave train may evolve into a freak wave train, and simultaneously the setting of the Phillips paranleter and enhancement coefficient of JONSWAP spectrum and initial random phases is very important for the formation of freak waves. The way to increase the generation efficiency of freak waves thsough changing the involved parameters is also presented.展开更多
Spectral characteristics of wind-generated waves in labortaory are presented on the basis of a systematic measurement in a large-scale wind-wave channel and compared with those in the field. A marked characteristics o...Spectral characteristics of wind-generated waves in labortaory are presented on the basis of a systematic measurement in a large-scale wind-wave channel and compared with those in the field. A marked characteristics of the measured spetra is the existence of secondary spectrum-peak. The dependence of spectral peak-frequency, peak-value and zeroth-order moment on wind speed and fetch are presented and found roughly similar to those in the field represented by the JONSWAP spectrum, regardless of the differences in coefficient. The spectral slope beta at high-frequencies are found somewhat greater than those of field wind-waves in both cases of deep and shallow waters. Except for the low-frequency part, the spectral forms measured in different wind conditions are similar and fit for the JONSWAP spectrum with gamma = 6 and beta = 5.5. Some relevant problems are discussed.展开更多
基金supported by the Scientific Instruments Development Program of NSFC (No.615278010)the National Key Basic Research Program of China (973 program) under grant No.2014CB845301/2/3
文摘In this study, we developed the first linear Joint North Sea Wave Project(JONSWAP) spectrum(JS), which involves a transformation from the JS solution to the natural logarithmic scale. This transformation is convenient for defining the least squares function in terms of the scale and shape parameters. We identified these two wind-dependent parameters to better understand the wind effect on surface waves. Due to its efficiency and high-resolution, we employed the airborne Light Detection and Ranging(LIDAR) system for our measurements. Due to the lack of actual data, we simulated ocean waves in the MATLAB environment, which can be easily translated into industrial programming language. We utilized the Longuet-Higgin(LH) random-phase method to generate the time series of wave records and used the fast Fourier transform(FFT) technique to compute the power spectra density. After validating these procedures, we identified the JS parameters by minimizing the mean-square error of the target spectrum to that of the estimated spectrum obtained by FFT. We determined that the estimation error is relative to the amount of available wave record data. Finally, we found the inverse computation of wind factors(wind speed and wind fetch length) to be robust and sufficiently precise for wave forecasting.
文摘- This paper presents the method of determining JONSWAP spectrum by using measured wave data. If Hs, Tz and Tc are the measured significant wave height, average zero-up crossing wave period and average period between wave crests respectively and let y = T Z / TC; this paper provides equation to solve y fromf(y,y) = 0. From the solutions of this equation and by using LSM, the expression relating y as a function of y (for 2.54<y< 15.34 and 1.6 <y < 1,79) may be written as y -5546.721 - 9586.533y + 5568.168/- 1089/+ 2/, for other intervals the related formulas are also given. When y is known, the rest of parameters in JONSWAP spectrum can be obtained. In addition, this paper also provides alternatives for determining JONSWAP spectral parameters by using Hs, Tz and (, or Hs, Tz and S(w0) or other three given data. The JONSWAP spectra given in this paper satisfy the following formulas HS= 4.0 = 2 Tc = 2
基金supported by the International Science and Technology Cooperation Program(Grant No.2007DFA60490)the National Natural Science Foundation of China(Grant No.50679078)the Innovation Foundation of Guangzhou Institute of Energy Conversion (Grant No.0807r51001)
文摘Numerical simulations of freak wave generation are studied in random oceanic sea states described by JONSWAP spectrum. The evolution of initial random wave trains is namerically carried out within the framework of the modified fourorder nonlinear Schroedinger equation （mNLSE）, and some involved influence factors are also discussed. Results show that if the sideband instability is satisfied, a random wave train may evolve into a freak wave train, and simultaneously the setting of the Phillips paranleter and enhancement coefficient of JONSWAP spectrum and initial random phases is very important for the formation of freak waves. The way to increase the generation efficiency of freak waves thsough changing the involved parameters is also presented.
基金This work was financially supported by the National Science Foundation of China(No.4967277)
文摘Spectral characteristics of wind-generated waves in labortaory are presented on the basis of a systematic measurement in a large-scale wind-wave channel and compared with those in the field. A marked characteristics of the measured spetra is the existence of secondary spectrum-peak. The dependence of spectral peak-frequency, peak-value and zeroth-order moment on wind speed and fetch are presented and found roughly similar to those in the field represented by the JONSWAP spectrum, regardless of the differences in coefficient. The spectral slope beta at high-frequencies are found somewhat greater than those of field wind-waves in both cases of deep and shallow waters. Except for the low-frequency part, the spectral forms measured in different wind conditions are similar and fit for the JONSWAP spectrum with gamma = 6 and beta = 5.5. Some relevant problems are discussed.
