def energy_dissipation_rate(self, H, U):
'''
c_ub = 100 = dimensionless empirical coefficient to correct
for non-Law-of-the-Wall results (Umlauf and Burchard, 2003)
u_c = water friction velocity (m/s)
sqrt(rho_air / rho_w) * u_a ~ .03 * u_a
u_a = air friction velocity (m/s)
z_0 = surface roughness (m) (Taylor and Yelland)
c_p = peak wave speed for Pierson-Moskowitz spectrum
w_p = peak angular frequency for Pierson-Moskowitz spectrum (1/s)
TODO: This implementation should be in a utility function.
It should not be part of the Waves management object itself.
'''
if H is 0 or U is 0:
return 0
c_ub = 100
c_p = PiersonMoskowitz.peak_wave_speed(U)
w_p = PiersonMoskowitz.peak_angular_frequency(U)
z_0 = 1200 * H * ((H / (2*np.pi*c_p)) * w_p)**4.5
u_a = .4 * U / np.log(10 / z_0)
u_c = .03 * u_a
eps = c_ub * u_c**3 / H
return eps
def percent_whitecap_coverage(cls, wind_speed):
'''
percent whitecap coverage
drag coefficient reduces linearly with wind speed
for winds less than 2.4 m/s
'''
if wind_speed is 0:
return 0
if wind_speed > 2.4:
C_D = .0008 + .000065 * wind_speed
else:
C_D = (.0008 + 2.4 * .000065) * wind_speed / 2.4
visc_air = 1.5 * 10**(-5) # m2/s
peak_ang_freq = PiersonMoskowitz.peak_angular_frequency(wind_speed)
R_Bw = C_D * wind_speed**2 / (visc_air * peak_ang_freq)
Wc = 3.88 * 10**(-5) * R_Bw**(1.09)
return Wc
