def __init__(self, structure):
self.slab = multilayer(structure)
self.slab.tmm()
self.T_atm = datalib.ATData(self.slab.lambda_array)
self.BBamb = datalib.BB(self.slab.lambda_array, self.slab.T_amb)
self.e_amb = self.BBamb * (1 - self.T_atm)
self.e_struct = self.slab.thermal_emission_array
self.BBml = datalib.BB(self.slab.lambda_array, self.slab.T_ml)
self.lda = self.slab.lambda_array
def Abs_eff(lam, EM, solarconc, T):
AM = datalib.AM(lam)
upper = np.amax(lam)
BBs = datalib.BB(lam, T)
TE = BBs * EM
alpha = solarconc * (numlib.Integrate(AM * EM, lam, 100e-9, upper))
beta = np.pi * numlib.Integrate(TE, lam, 100e-9, upper)
return (alpha - beta) / (alpha)
def IdealSource(lam, T):
### compute blackbody spectrum at current T
rho = datalib.BB(lam, T)
### get photopic luminosity function
ph = datalib.PhLum(lam)
### ideal thermal emission is product of the two
TE_ideal = ph * rho
return TE_ideal
def __init__(self, structure):
self.slab = multilayer(structure)
self.slab.tmm()
self.T_atm = datalib.ATData(self.slab.lambda_array)
self.BBamb = datalib.BB(self.slab.lambda_array, self.slab.T_amb)
self.e_amb = np.empty([len(self.slab.t),len(self.slab.lambda_array)])
for i in range(0,len(self.slab.t)):
for j in range(0,len(self.slab.lambda_array)):
angular_mod = 1./np.cos(self.slab.t[i])
self.e_amb[i][j] = self.BBamb[j]*(1-self.T_atm[j]**angular_mod)
#self.e_struct = self.slab.thermal_emission_array
#self.BBml = datalib.BB(self.slab.lambda_array, self.slab.T_ml)
self.lda = self.slab.lambda_array
def thermal_emission_ea(self):
### Temperature might change, update BB spectrum
self.BBs = datalib.BB(self.lambda_array, self.T_ml)
#temp = np.zeros(len(self.lambda_array))
for i in range(0, len(self.t)):
### Thermal emission goes like BBs(lambda) * eps(theta, lambda) * cos(theta)
for j in range(0, len(self.lambda_array)):
self.thermal_emission_array_p[i][j] = self.BBs[
j] * self.emissivity_array_p[i][j] * np.cos(self.t[i])
self.thermal_emission_array_s[i][j] = self.BBs[
j] * self.emissivity_array_s[i][j] * np.cos(self.t[i])
return 1
def Patm(EPS_P, EPS_S, T_amb, lam, theta, w):
dlam = np.abs(lam[0] - lam[1])
### Get normal atmospheric transmissivity
atm_T = datalib.ATData(lam)
### Get BB spectrum associated with ambient temperature
BBs = datalib.BB(lam, T_amb)
x = 0
for i in range(0, len(w)):
patm_som = 0
angular_mod = 1. / np.cos(theta[i])
for j in range(0, len(lam)):
patm_som = patm_som + (
0.5 * EPS_P[i][j] + 0.5 * EPS_S[i][j]) * BBs[j] * np.cos(
theta[i]) * (1 - atm_T[j]**angular_mod) * dlam
x = x + patm_som * np.sin(theta[i]) * w[i]
return 2 * np.pi * x
def optimal_spectrum(self, T):
self.slab.T_ml = T
self.slab.update()
self.BBml = datalib.BB(self.slab.lambda_array, self.slab.T_ml)
for i in range(0,len(self.slab.t)):
for j in range(0,len(self.slab.lambda_array)):
if (self.BBml[j]-self.e_amb[i][j]) > 0: # self.e_amb[j]:
self.slab.emissivity_array_p[i,j] = self.slab.emissivity_array_s[i,j] = 1
else:
self.slab.emissivity_array_p[i,j] = self.slab.emissivity_array_s[i,j] = 0
self.slab.emissivity_array = self.slab.emissivity_array_p[0,:]
self.slab.thermal_emission_ea()
self.slab.thermal_emission()
self.slab.cooling_power()
self.e_struct = self.slab.thermal_emission_array
def thermal_emission(self):
### Temperature might change, update BB spectrum
self.BBs = datalib.BB(self.lambda_array, self.T_ml)
### Emissivity doesn't change unless structure changes
self.thermal_emission_array = self.BBs * self.emissivity_array
return 1
# Change one of the layers to an effective index
fill_fraction = 0.3
layer = 1
np_slab.layer_alloy(layer,0.3,'Air','Si3N4','Bruggeman', plot = False)
layer = 2
np_slab.layer_alloy(layer,0.3,'Air','RC0_1B_SiO2','Bruggeman', plot = False)
##############################################################################
##############################################################################
#%%
### Plot the emission properties
if calc_cooling:
np_slab.tmm()
T_atm = datalib.ATData(np_slab.lambda_array)
BBamb = datalib.BB(np_slab.lambda_array, np_slab.T_amb)
BBml = datalib.BB(np_slab.lambda_array, np_slab.T_ml)
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
ax1 = plt.figure()
mask = (np_slab.lambda_array >= 3000e-9) & (np_slab.lambda_array <= 30000e-9)
plt.plot(np_slab.lambda_array[mask]*1e6, BBamb[mask]*1e-6, 'k', label = 'Black body emittance')
plt.plot(np_slab.lambda_array[mask]*1e6, BBamb[mask]*(1-T_atm[mask])*1e-6, 'b', label = 'Atmospheric emittance')
plt.plot(np_slab.lambda_array[mask]*1e6, BBml[mask]*np_slab.emissivity_array[mask]*1e-6,'g',
label = 'Structure emittance \n ($P_{300K} = 65 W/m^2$, $\Delta T_{max} = 30K$)')
plt.fill_between(np_slab.lambda_array[mask]*1e6,0,BBamb[mask]*(1-T_atm[mask])*1e-6,
color = 'b',
alpha=0.5)
plt.fill_between(np_slab.lambda_array[mask]*1e6,BBamb[mask]*(1-T_atm[mask])*1e-6,BBml[mask]*np_slab.emissivity_array[mask]*1e-6,
where = BBml[mask]*np_slab.emissivity_array[mask]*1e-6 > BBamb[mask]*(1-T_atm[mask])*1e-6,
color = 'g',
plt.xlabel('SiO2 nanoparticle film thickness (um)')
plt.title('30% F.F. NP SiO2 Film on 900nm Si3N4 on 200nm Ag')
#%%
plt.figure()
plt.plot(thickness / um, CP)
plt.ylabel('Net power flux (W/m^2)')
plt.xlabel('SiO2 nanoparticle film thickness (um)')
plt.title('35% F.F. NP SiO2 Film on 900nm Si3N4 on 200nm Ag')
#%%
# Calculate standard spectra related to radiative cooling
AM = datalib.AM(np_slab.lambda_array)
T_atm = datalib.ATData(np_slab.lambda_array)
BB = datalib.BB(np_slab.lambda_array, np_slab.T_ml)
### plot results!
plt.figure()
mask = (np_slab.lambda_array >= 3000e-9) & (np_slab.lambda_array <= 30000e-9)
plt.plot(np_slab.lambda_array[mask] * 1e6, T_atm[mask], 'cyan')
plt.plot(np_slab.lambda_array[mask] * 1e6, np_slab.emissivity_array[mask],
'red')
#plt.plot(np_slab.lambda_array[mask]*1e6, np_slab.thermal_emission_array[mask], 'red')
plt.figure()
mask = (np_slab.lambda_array >= 250e-9) & (np_slab.lambda_array <= 3000e-9)
plt.plot(np_slab.lambda_array[mask] * 1e6, np_slab.emissivity_array[mask],
'blue')
plt.plot(np_slab.lambda_array[mask] * 1e6, AM[mask] / (1.4 * 1e9), 'red')
# slab.emissivity_array_p[i,j] = slab.emissivity_array_s[i,j] = spec[j]
# slab.emissivity_array[j] = spec[j]
if slab.lambda_array[j] > 7 * um and slab.lambda_array[j] < 14 * um:
slab.emissivity_array_p[i, j] = slab.emissivity_array_s[i, j] = 1
slab.emissivity_array[j] = 1
else:
slab.emissivity_array_p[i, j] = slab.emissivity_array_s[i, j] = 0
slab.emissivity_array[j] = 0
slab.thermal_emission_ea()
slab.thermal_emission()
slab.cooling_power()
Tss = slab.steady_state_temperature()
T_atm = datalib.ATData(slab.lambda_array)
BBamb = datalib.BB(slab.lambda_array, slab.T_amb)
BBml = datalib.BB(slab.lambda_array, slab.T_ml)
plt.figure()
mask = (slab.lambda_array >= 3000e-9) & (slab.lambda_array <= 30000e-9)
plt.plot(slab.lambda_array[mask] * 1e6, BBamb[mask] * (1 - T_atm[mask]))
plt.plot(slab.lambda_array[mask] * 1e6,
BBml[mask] * slab.emissivity_array[mask])
plt.plot(slab.lambda_array[mask] * 1e6, slab.thermal_emission_array[mask],
'r:')
plt.show()
print("Radiative Power (cooling) is ", slab.radiative_power_val, "W/m^2")
print("Absorbed Solar Power (warming) is ", slab.solar_power_val, "W/m^2")
print("Absorbed Atmospheric Radiation (warming) is ",
slab.atmospheric_power_val, "W/m^2")
