def test_Wilke():
mu = Wilke([0.05, 0.95], [1.34E-5, 9.5029E-6], [64.06, 46.07])
assert_close(mu, 9.701614885866193e-06, rtol=1e-10)
# with pytest.raises(Exception):
# Wilke([0.05], [1.34E-5, 9.5029E-6], [64.06, 46.07])
mu = Wilke_large([0.05, 0.95], [1.34E-5, 9.5029E-6], [64.06, 46.07])
assert_close(mu, 9.701614885866193e-06, rtol=1e-10)
mu = Wilke_prefactored([0.05, 0.95], [1.34E-5, 9.5029E-6],
*Wilke_prefactors([64.06, 46.07]))
assert_close(mu, 9.701614885866193e-06, rtol=1e-10)
# Large composition test
zs = [
0.10456352460469782, 0.10472506156674823, 0.10347781516834291,
1.4089716440797791e-05, 0.10488254481011455, 0.10078888107401028,
0.09902003237540975, 0.09045109410107957, 0.08642540418108867,
0.1043609364553231, 0.10129061594674436
]
mus = [
1.1601665408586192e-05, 9.408370570946896e-06, 8.19709294177777e-06,
1.4314548719058091e-05, 1.5057441002481923e-05, 7.5434795308593725e-06,
7.447082353139856e-06, 7.0365592301967965e-06, 6.720364621681796e-06,
1.2157004301638695e-05, 1.3006463728382868e-05
]
MWs = [
16.04246, 30.06904, 44.09562, 2.01588, 44.0095, 58.1222, 58.1222,
72.14878, 72.14878, 34.08088, 64.0638
]
# Make a large set of data, but don't actually use it anywhere; for easy of bencharmking
zs_new = []
mus_new = []
MWs_new = []
for i in range(20):
zs_new.extend(zs)
mus_new.extend(mus)
MWs_new.extend(MWs)
zs_large = normalize(zs_new)
mus_large = mus_new
MWs_large = MWs_new
mu_expect = 9.282311227289109e-06
assert_close(Wilke(zs, mus, MWs), mu_expect, rtol=1e-10)
assert_close(Wilke_large(zs, mus, MWs), mu_expect, rtol=1e-10)
prefactors = Wilke_prefactors(MWs)
assert_close(Wilke_prefactored(zs, mus, *prefactors),
mu_expect,
rtol=1e-10)
# Test that the prefactors really work - use a different composition
zs_diff = normalize([.1, .2, .3, .4, .5, .6, .7, .8, .9, .10, .2])
mu_expect = 8.238656569251283e-06
assert_close(Wilke(zs_diff, mus, MWs), mu_expect, rtol=1e-10)
assert_close(Wilke_large(zs_diff, mus, MWs), mu_expect, rtol=1e-10)
assert_close(Wilke_prefactored(zs_diff, mus, *prefactors),
mu_expect,
rtol=1e-10)
def test_fire_mixing():
LFL = fire_mixing(ys=normalize([0.0024, 0.0061, 0.0015]),
FLs=[.012, .053, .031])
assert_close(LFL, 0.02751172136637642, rtol=1e-13)
UFL = fire_mixing(ys=normalize([0.0024, 0.0061, 0.0015]),
FLs=[.075, .15, .32])
assert_close(UFL, 0.12927551844869378, rtol=1e-13)
def gammas_infinite_dilution(self):
r'''Calculate and return the infinite dilution activity coefficients
of each component.
Returns
-------
gammas_infinite : list[float]
Infinite dilution activity coefficients, [-]
Notes
-----
The algorithm is as follows. For each component, set its composition to
zero. Normalize the remaining compositions to 1. Create a new object
with that composition, and calculate the activity coefficient of the
component whose concentration was set to zero.
'''
T, N = self.T, self.N
xs_base = self.xs
if self.scalar:
gammas_inf = [0.0] * N
copy_fun = list
else:
gammas_inf = zeros(N)
copy_fun = array
for i in range(N):
xs = copy_fun(xs_base)
xs[i] = 0.0
xs = normalize(xs)
gammas_inf[i] = self.to_T_xs(T, xs=xs).gammas()[i]
return gammas_inf
