scientimate.wavespectrum2timeseries#

Eta, t, Hm0, fp, fEta, SxxEta, a, w, Phi = scientimate.wavespectrum2timeseries(f, Sxx, fs=2, dispout='no')

Description#

Generate random water wave data from a given water wave spectrum using wave superposition

For more options use psd2timeseries

Inputs#

f: Frequency (Hz)
Sxx: Wave power spectral density (m^2s)
fs=2: Sampling frequency that data are collected at in (Hz)
dispout=’no’: Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs#

Eta: Water surface level time series in (m)
t: Time in (s)
Hm0: Zero moment wave height (m)
fp: Peak wave frequency (Hz), fp=1/Tp (Tp: Peak wave period (s))
fEta: Frequency from generated time series(Hz)
SxxEta: Power spectral density from generated time series (m^2s)
a: Wave amplitude for for one-sided spectrum (0<fEta<fs/2) from generated time series (m)
w: Wave angular frequency for for one-sided spectrum (0<fEta<fs/2) from generated time series (rad/s)
Phi: Wave random phase for for one-sided spectrum (0<fEta<fs/2) from generated time series (rad)

Examples#

import scientimate as sm
import numpy as np

N=2**11
fs=8
df=fs/N #Frequency difference
f=np.arange(0,fs/2+df,df) #Frequency vector
f[0]=f[1]/2 #Assign temporarily non-zero value to fisrt element of f to prevent division by zero
Sxx=0.016*9.81**2/((2*np.pi)**4*(f**5))*np.exp(-1.25*(0.33/f)**4) #Calculating Spectrum
f[0]=0
Sxx[0]=0
Eta,t,Hm0,fp,fEta,SxxEta,a,w,Phi=sm.wavespectrum2timeseries(f,Sxx,fs,'yes')

References#

Branlard, E. (2010). Generation of time series from a spectrum. Technical University Denmark. National Laboratory for Sustainable Energy.

scientimate.wavespectrum2timeseries

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