def E_atm(theta, lam):
### emissivity starts with 1 - T of atmosphere
T = datalib.ATData(lam)
### angular part is the emissivity raised to 1/cos(theta):
b = 1. / np.cos(theta)
Eatm = (1 - T)**b
return Eatm
def plot_cooling(slab):
# Look at cooling properties
T_atm = datalib.ATData(slab.lambda_array)
AM = datalib.AM(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, T_atm[mask]*100, 'k', alpha = 0.1, label = 'AM1.5 or \n Atmospheric \n transmittance')
# plt.plot(slab.lambda_array[mask]*1e6, slab.emissivity_array[mask]*100, 'r', label = 'Structure \n absorption')
# #plt.plot(np_slab.lambda_array[mask]*1e6, np_slab.thermal_emission_array[mask], 'red')
# plt.xlabel('Wavelength (nm)')
# plt.ylabel('Absorption (%)')
# plt.tight_layout(rect=[-0.10,0,0.75,1])
# plt.legend(bbox_to_anchor=(1.04, 1))
# plt.show()
#
# plt.figure()
# mask = (slab.lambda_array >= 250e-9) & (slab.lambda_array <= 3000e-9)
# plt.plot(slab.lambda_array[mask]*1e6, slab.emissivity_array[mask]*100, 'r', label = 'Structure \n absorption')
# plt.plot(slab.lambda_array[mask]*1e6, 100*AM[mask]/(1.4*1e9), 'k', alpha = 0.1, label = 'AM1.5 or \n Atmospheric \n transmittance')
# plt.xlabel('Wavelength (um)')
# plt.ylabel('Absorption (%)')
# plt.tight_layout(rect=[-0.10,0,0.75,1])
# plt.legend(bbox_to_anchor=(1.04, 1))
# 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")
print("Net Power flux out of the structure is ", slab.cooling_power_val,
"W/m^2")
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 __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 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
#plt.plot(lda, A[:,1]*100,':', label = 'Abs. layer 5 \n (Ag reflector)')
#plt.xlabel('Wavelength (nm)')
#plt.ylabel('%')
#plt.title('Transmission, reflection, and absorption at normal incidence')
#plt.legend()
#plt.show()
##############################################################################
##############################################################################
#%%
"""
Plot TMM and measured absorption
"""
if (min(lda) > 1999):
t_atmosphere = datalib.ATData(lda * 1e-9)
fig = plt.figure()
plt.plot(lda * 1e-3,
t_atmosphere * 100,
'k',
alpha=0.1,
label='Atmospheric \n transmittance')
plt.plot(lda * 1e-3, (1 - T - R) * 100, 'r', label='Device absorption')
plt.plot(lda * 1e-3,
A[:, 1] * 100,
':',
label='Abs. $Si_{3}N_{4}$ NP \n (30%, Brugg.)')
plt.plot(lda * 1e-3,
A[:, 2] * 100,
':',
label='Abs. $SiO_{2}$ NP \n (23.8%, Brugg.)')
# 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,
plt.ylabel('Net power flux (W/m^2)')
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')
'Thickness_List':
[0, 0, 0], # You can not have the back reflector as the last layer!!!
### range of wavelengths optical properties will be calculated for
### values are stored in the array self.lam
'Lambda_List': [250 * nm, 30 * um, 1000],
## Calculate for explicit angular dependence
'EXPLICIT_ANGLE': 1,
## Calculate quantities related to radiative cooling
'COOLING': 1
}
slab = multilayer(structure)
slab.tmm()
slab.T_ml = 300
slab.T_amb = 300
T_atm = datalib.ATData(slab.lambda_array)
spec = np.zeros(len(T_atm))
for i in range(0, len(slab.t)):
for j in range(0, len(slab.lambda_array)):
# if T_atm[j]>0.01:
# #spec[j] = random.randint(1,101)/100
# spec[j] = (0.3*np.cos(0.002*np.pi*j)+0.7)*0.1
# else:
# spec[j] = 0
# if slab.lambda_array[j] > 6.3*um and slab.lambda_array[j] < 15*um:
# slab.emissivity_array_p[i,j] = slab.emissivity_array_s[i,j] = spec[j]
# slab.emissivity_array[j] = spec[j]
# else:
# 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:
