def stpv_pv_filter_fom(trans, ref, lam):
### set lambda_bg to 750 nm
lbg = 750e-9
### get AM1.5 spectrum
AM = datalib.AM(lam)
### get BB spectrum at 440 K
BBs = datalib.BB(lam, 440)
### There are three different integrands for numerator
num_integrand_1 = np.copy(AM * trans * lam / lbg)
num_integrand_2 = np.copy(AM * trans)
num_integrand_3 = np.copy(BBs * ref)
num1 = numlib.Integrate(num_integrand_1, lam, 300e-9, lbg)
num2 = numlib.Integrate(num_integrand_2, lam, lbg, 3200e-9)
num3 = numlib.Integrate(num_integrand_3, lam, 3200e-9, 3700e-9)
### denominator 1 comes from lam/lbg * AM1.5
den_integrand_1 = np.copy(AM * lam / lbg)
den1 = numlib.Integrate(den_integrand_1, lam, 300e-9, lbg)
### denominator 2 comes from AM1.5
den2 = numlib.Integrate(AM, lam, lbg, 3200e-9)
### denominator 3 comes from BB spectrum
return 0.5 * num1 / den1 + 0.5 * num2 / den2 + 2 * num2 / den2
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 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 stpv_pv_filter_grad(dim, trans_prime, ref_prime, lam):
lbg = 750e-9
grad = np.zeros(dim)
AM = datalib.AM(lam)
BBs = datalib.BB(lam, 440)
den_integrand_1 = np.copy(AM * lam / lbg)
den1 = numlib.Integrate(den_integrand_1, lam, 300e-9, lbg)
den2 = numlib.Integrate(AM, lam, lbg, 3200e-9)
den3 = numlib.Integrate(BBs, lam, 3200e-9, 3700e-9)
for i in range(0, dim):
integrand_1 = np.copy(trans_prime[i, :] * AM * lam / lbg)
integrand_2 = np.copy(trans_prime[i, :] * AM)
integrand_3 = np.copy(ref_prime[i, :] * BBs)
num_prime_1 = numlib.Integrate(integrand_1, lam, 300e-9, lbg)
num_prime_2 = numlib.Integrate(integrand_2, lam, lbg, 3200e-9)
num_prime_3 = numlib.Integrate(integrand_3, lam, 3200e-9, 3700e-9)
grad[
i] = 0.5 * num_prime_1 / den1 + 0.5 * num_prime_2 / den2 + 2 * num_prime_3 / den3
return grad
def Patm_prime(dim, eps_prime_p, eps_prime_s, T_amb, lam, theta, w):
dlam = np.abs(lam[1] - lam[0])
### get normal atmospheric transmissivity
atm_T = datalib.ATData(lam)
### get BB spectrum associated with ambient temperature
BBs = datalib.BB(lam, T_amb)
### initialize grad
grad = np.zeros(dim)
for i in range(0, dim):
x = 0
for j in range(0, len(w)):
patm_prime = 0
angular_mod = 1. / np.cos(theta[j])
for k in range(0, len(lam)):
patm_prime = patm_prime + 0.5 * (eps_prime_p[
i, j, k] + eps_prime_s[i, j, k]) * BBs[k] * np.cos(
theta[j]) * (1 - atm_T[k]**angular_mod) * dlam
x = x + patm_prime * np.sin(theta[j]) * w[j]
grad[i] = 2 * np.pi * x
return grad
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
layer = 1
np_slab.layer_alloy(layer,
fill_fraction,
'Air',
'SiO2',
'Bruggeman',
plot=True)
#np_slab.layer_alloy(layer,fill_fraction,'Air','SiO2','MG', plot = False)
np_slab.thermal_emission_ea()
np_slab.cooling_power()
#%%
# 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')
