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123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
from math import sqrt, exp
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from CoolProp.CoolProp import Props
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.odr import *
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from math import log
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E_K = {'REFPROP-Ammonia': 386,
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'REFPROP-Argon': 143.2
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}
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SIGMA = {'REFPROP-Ammonia': 0.2957,
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'REFPROP-Argon': 0.335
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}
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E_K['REFPROP-Propane'] = 263.88
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SIGMA['REFPROP-Propane'] = 0.49748
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E_K['REFPROP-R32'] = 289.65
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SIGMA['REFPROP-R32'] = 0.4098
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E_K['REFPROP-R245fa'] = 329.72
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SIGMA['REFPROP-R245fa'] = 0.5529
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def viscosity_dilute(fluid, T, e_k, sigma):
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"""
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T in [K], e_k in [K], sigma in [nm]
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viscosity returned is in [Pa-s]
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"""
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Tstar = T / e_k
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molemass = Props(fluid, 'molemass')
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if fluid == 'Propane' or fluid == 'REFPROP-Propane':
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a = [0.25104574, -0.47271238, 0, 0.060836515, 0]
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theta_star = exp(a[0] * pow(log(Tstar), 0) + a[1] * pow(log(Tstar), 1) + a[3] * pow(log(Tstar), 3));
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eta_star = 0.021357 * sqrt(molemass * T) / (pow(sigma, 2) * theta_star) / 1e6;
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return eta_star
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# From Neufeld, 1972, Journal of Chemical Physics - checked coefficients
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OMEGA_2_2 = 1.16145 * pow(Tstar, -0.14874) + 0.52487 * exp(-0.77320 * Tstar) + 2.16178 * exp(-2.43787 * Tstar)
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# Using the leading constant from McLinden, 2000 since the leading term from Huber 2003 gives crazy values
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eta_star = 26.692e-3 * sqrt(molemass * T) / (pow(sigma, 2) * OMEGA_2_2) / 1e6
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return eta_star
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def viscosity_linear(fluid, T, rho, e_k, sigma):
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"""
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Implements the method of Vogel 1998 (Propane) for the linear part
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"""
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N_A = 6.02214129e23
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molemass = Props(fluid, 'molemass')
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Tstar = T / e_k
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b = [-19.572881, 219.73999, -1015.3226, 2471.01251, -3375.1717, 2491.6597, -787.26086, 14.085455, -0.34664158]
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s = sum([b[i] * pow(Tstar, -0.25 * i) for i in range(7)])
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B_eta_star = s + b[7] * pow(Tstar, -2.5) + b[8] * pow(Tstar, -5.5) # //[no units]
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B_eta = N_A * pow(sigma / 1e9, 3) * B_eta_star # [m3/mol]
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return viscosity_dilute(fluid, T, e_k, sigma) * B_eta * rho / molemass * 1000
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from PDSim.misc.datatypes import Collector
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RHO = Collector()
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TT = Collector()
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DELTA = Collector()
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TAU = Collector()
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VV = Collector()
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VV0 = Collector()
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VV1 = Collector()
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VVH = Collector()
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fluid = 'REFPROP-R32'
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Tc = Props(fluid, 'Tcrit')
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rhoc = Props(fluid, 'rhocrit')
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for T in np.linspace(290, Props(fluid, 'Tcrit') - 0.1, 100):
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rhoV = Props('D', 'T', T, 'Q', 1, fluid)
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rhoL = Props('D', 'T', T, 'Q', 0, fluid)
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rhomax = Props('D', 'T', Props(fluid, 'Tmin'), 'Q', 0, fluid)
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for rho in list(np.linspace(rhoL, rhomax, 100)): # +list(np.linspace(rhoV,0.0001,100)):
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# for rho in list(np.linspace(rhoV,0.0001,100)):
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mu_0 = viscosity_dilute(fluid, T, E_K[fluid], SIGMA[fluid])
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mu_1 = viscosity_linear(fluid, T, rho, E_K[fluid], SIGMA[fluid])
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mu = Props('V', 'T', T, 'D', rho, fluid)
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VV << mu
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VV0 << mu_0
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VV1 << mu_1
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VVH << mu - mu_0 - mu_1
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TT << T
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RHO << rho
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DELTA << rho / rhoc
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TAU << Tc / T
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def f_RHS(E, DELTA_TAU, VV):
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k = 0
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sum = 0
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DELTA = DELTA_TAU[0, :]
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TAU = DELTA_TAU[1, :]
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for i in range(2, 5):
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for j in range(3):
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sum += E[k] * DELTA**i / TAU**j
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k += 1
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# f1,f2,f3,g1,g2 = E[k],E[k+1],E[k+2],E[k+3],E[k+4]
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# DELTA0 = g1*(1+g2*np.sqrt(TAU))
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# sum += (f1+f2/TAU+f3/TAU/TAU)*(DELTA/(DELTA0-DELTA)-DELTA/DELTA0)
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print('%s %%' % np.mean(np.abs(((sum / VV - 1) * 100))))
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return sum
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log_muH = np.log(VVH.v().T)
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x = np.c_[DELTA.v().T, TAU.v().T].T
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y = VVH.v()
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linear = Model(f_RHS, extra_args=(y,))
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mydata = Data(x, y)
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myodr = ODR(mydata, linear, beta0=np.array([0.1] * 17),)
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myoutput = myodr.run()
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E = myoutput.beta
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print(E)
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#plt.plot(TT.vec, y,'b.',TT.vec, f_RHS(E, x, y),'r.')
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# plt.show()
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# plt.plot()
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plt.plot(y.T, f_RHS(E, x, y))
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plt.show()
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