def saturation_pressure(Ref, ClassName, fName=None, LV=None):
if fName is None:
Tc = Props(Ref, 'Tcrit')
pc = Props(Ref, 'pcrit')
rhoc = Props(Ref, 'rhocrit')
Tmin = Props(Ref, 'Tmin')
TT = np.linspace(Tmin + 1e-6, Tc - 0.00001, 300)
pL = Props('P', 'T', TT, 'Q', 0, Ref)
pV = Props('P', 'T', TT, 'Q', 1, Ref)
rhoL = Props('D', 'T', TT, 'Q', 0, Ref)
rhoV = Props('D', 'T', TT, 'Q', 1, Ref)
else:
Tc = 423.27
pc = 3533
rhoc = 470
Tmin = 273
lines = open(fName, 'r').readlines()
TT, p, rhoL, rhoV = [], [], [], []
for line in lines:
_T, _p, _rhoL, _rhoV = line.split(' ')
TT.append(float(_T))
p.append(float(_p))
rhoL.append(float(_rhoL))
rhoV.append(float(_rhoV))
TT = np.array(TT)
Np = 60
n = range(1, Np)
max_abserror = 0
while len(n) > 3:
def f_p(B, x):
# B is a vector of the parameters.
# x is an array of the current x values.
return sum([_B * x**(_n / 2.0) for _B, _n in zip(B, n)])
x = 1.0 - TT / Tc
if LV == 'L':
y = (np.log(pL) - np.log(pc)) * TT / Tc
elif LV == 'V' or LV is None:
y = (np.log(pV) - np.log(pc)) * TT / Tc
linear = Model(f_p)
mydata = Data(x, y)
myodr = ODR(mydata, linear, beta0=[0] * len(n))
myoutput = myodr.run()
beta = myoutput.beta
sd = myoutput.sd_beta
p_fit = np.exp(f_p(myoutput.beta, x) * Tc / TT) * pc
if LV == 'L':
max_abserror = np.max(np.abs((p_fit / pL) - 1) * 100)
elif LV == 'V' or LV is None:
max_abserror = np.max(np.abs((p_fit / pV) - 1) * 100)
print(max_abserror)
psat_error = max_abserror
dropped_indices = [i for i in range(len(n)) if abs(sd[i]) < 1e-15]
if dropped_indices:
# for i in reversed(dropped_indices):
# randomly drop one of them
n.pop(random.choice(dropped_indices))
continue
if max_abserror < 0.5: # Max error is 0.5%
Ncoeffs = str(list(myoutput.beta)).lstrip('[').rstrip(']')
tcoeffs = str(n).lstrip('[').rstrip(']')
maxerror = max_abserror
N = len(n)
else:
break
# Find the least significant entry (the one with the largest standard error)
# and remove it
n.pop(np.argmax(sd))
# Remove elements that are not
import textwrap
template = textwrap.dedent(
"""
double {name:s}Class::psat{LV:s}(double T)
{{
// Maximum absolute error is {psat_error:f} % between {Tmin:f} K and {Tmax:f} K
const double t[]={{0, {tcoeffs:s}}};
const double N[]={{0, {Ncoeffs:s}}};
double summer=0,theta;
theta=1-T/reduce.T;
for (int i=1;i<={N:d};i++)
{{
summer += N[i]*pow(theta,t[i]/2);
}}
return reduce.p.Pa*exp(reduce.T/T*summer);
}}
""")
the_string = template.format(N=len(n) + 1,
tcoeffs=tcoeffs,
Ncoeffs=Ncoeffs,
name=ClassName,
Tmin=Tmin,
Tmax=TT[-1],
psat_error=maxerror,
LV=LV if LV in ['L', 'V'] else ''
)
f = open('anc.txt', 'a')
f.write(the_string)
f.close()
return the_string
def saturation_pressure(Ref, ClassName, fName=None, LV=None):
if fName is None:
Tc = Props(Ref, 'Tcrit')
pc = Props(Ref, 'pcrit')
rhoc = Props(Ref, 'rhocrit')
Tmin = Props(Ref, 'Tmin')
TT = np.linspace(Tmin + 1e-6, Tc - 0.00001, 300)
pL = Props('P', 'T', TT, 'Q', 0, Ref)
pV = Props('P', 'T', TT, 'Q', 1, Ref)
rhoL = Props('D', 'T', TT, 'Q', 0, Ref)
rhoV = Props('D', 'T', TT, 'Q', 1, Ref)
else:
Tc = 423.27
pc = 3533
rhoc = 470
Tmin = 273
lines = open(fName, 'r').readlines()
TT, p, rhoL, rhoV = [], [], [], []
for line in lines:
_T, _p, _rhoL, _rhoV = line.split(' ')
TT.append(float(_T))
p.append(float(_p))
rhoL.append(float(_rhoL))
rhoV.append(float(_rhoV))
TT = np.array(TT)
Np = 60
n = range(1, Np)
max_abserror = 0
while len(n) > 3:
def f_p(B, x):
# B is a vector of the parameters.
# x is an array of the current x values.
return sum([_B * x**(_n / 2.0) for _B, _n in zip(B, n)])
x = 1.0 - TT / Tc
if LV == 'L':
y = (np.log(pL) - np.log(pc)) * TT / Tc
elif LV == 'V' or LV is None:
y = (np.log(pV) - np.log(pc)) * TT / Tc
linear = Model(f_p)
mydata = Data(x, y)
myodr = ODR(mydata, linear, beta0=[0] * len(n))
myoutput = myodr.run()
beta = myoutput.beta
sd = myoutput.sd_beta
p_fit = np.exp(f_p(myoutput.beta, x) * Tc / TT) * pc
if LV == 'L':
max_abserror = np.max(np.abs((p_fit / pL) - 1) * 100)
elif LV == 'V' or LV is None:
max_abserror = np.max(np.abs((p_fit / pV) - 1) * 100)
print(max_abserror)
psat_error = max_abserror
dropped_indices = [i for i in range(len(n)) if abs(sd[i]) < 1e-15]
if dropped_indices:
#for i in reversed(dropped_indices):
# randomly drop one of them
n.pop(random.choice(dropped_indices))
continue
if max_abserror < 0.5: #Max error is 0.5%
Ncoeffs = str(list(myoutput.beta)).lstrip('[').rstrip(']')
tcoeffs = str(n).lstrip('[').rstrip(']')
maxerror = max_abserror
N = len(n)
else:
break
# Find the least significant entry (the one with the largest standard error)
# and remove it
n.pop(np.argmax(sd))
#Remove elements that are not
import textwrap
template = textwrap.dedent("""
double {name:s}Class::psat{LV:s}(double T)
{{
// Maximum absolute error is {psat_error:f} % between {Tmin:f} K and {Tmax:f} K
const double t[]={{0, {tcoeffs:s}}};
const double N[]={{0, {Ncoeffs:s}}};
double summer=0,theta;
theta=1-T/reduce.T;
for (int i=1;i<={N:d};i++)
{{
summer += N[i]*pow(theta,t[i]/2);
}}
return reduce.p.Pa*exp(reduce.T/T*summer);
}}
""")
the_string = template.format(N=len(n) + 1,
tcoeffs=tcoeffs,
Ncoeffs=Ncoeffs,
name=ClassName,
Tmin=Tmin,
Tmax=TT[-1],
psat_error=maxerror,
LV=LV if LV in ['L', 'V'] else '')
f = open('anc.txt', 'a')
f.write(the_string)
f.close()
return the_string
def saturation_density(Ref, ClassName, form='A', LV='L', perc_error_allowed=0.3, fName=None, add_critical=True):
"""
Parameters
----------
Ref : string
The fluid name for the fluid that will be used to generate the saturation data
ClassName : The name of the class that will be used in the C++ code
form : string
If ``'A'``, use a term of the form
"""
if fName is None:
Tc = Props(Ref, 'Tcrit')
pc = Props(Ref, 'pcrit')
rhoc = Props(Ref, 'rhocrit')
Tmin = Props(Ref, 'Tmin')
print("%s %s %s" % (Ref, Tmin, Props(Ref, 'Ttriple')))
TT = np.linspace(Tmin, Tc - 1, 1000)
TT = list(TT)
# Add temperatures around the critical temperature
if add_critical:
for dT in [1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]:
TT.append(Tc - dT)
TT = np.array(sorted(TT))
p = Props('P', 'T', TT, 'Q', 0, Ref)
rhoL = Props('D', 'T', TT, 'Q', 0, Ref)
rhoV = Props('D', 'T', TT, 'Q', 1, Ref)
else:
Tc = 423.27
pc = 3533
rhoc = 470
Tmin = 273
lines = open(fName, 'r').readlines()
TT, p, rhoL, rhoV = [], [], [], []
for line in lines:
_T, _p, _rhoL, _rhoV = line.split(' ')
TT.append(float(_T))
p.append(float(_p))
rhoL.append(float(_rhoL))
rhoV.append(float(_rhoV))
TT = np.array(TT)
# Start with a large library of potential powers
n = [i / 6.0 for i in range(1, 200)] # +[0.35+i/200.0 for i in range(1,70)]+[0.05+0.01*i for i in range(1,70)]
x = 1.0 - TT / Tc
if LV == 'L':
rho_EOS = rhoL
elif LV == 'V':
rho_EOS = rhoV
else:
raise ValueError
if form == 'A':
y = np.array(rho_EOS) / rhoc - 1
elif form == 'B':
y = (np.log(rho_EOS) - np.log(rhoc)) * TT / Tc
else:
raise ValueError
max_abserror = 0
while len(n) > 3:
print("%s %s" % (max_abserror, len(n)))
def f_p(B, x):
# B is a vector of the parameters.
# x is an array of the current x values.
return sum([_B * x**(_n) for _B, _n in zip(B, n)])
linear = Model(f_p)
mydata = Data(x, y)
myodr = ODR(mydata, linear, beta0=[0] * len(n))
myoutput = myodr.run()
beta = myoutput.beta
sd = myoutput.sd_beta
if form == 'A':
rho_fit = (f_p(myoutput.beta, x) + 1) * rhoc
elif form == 'B':
rho_fit = np.exp(f_p(myoutput.beta, x) * Tc / TT) * rhoc
else:
raise ValueError
print('first,last %s %s %s %s %s %s' % (TT[0], TT[-1], rho_fit[0], rho_fit[-1], rho_EOS[0], rho_EOS[-1]))
max_abserror = np.max(np.abs(rho_fit / rho_EOS - 1)) * 100
dropped_indices = [i for i in range(len(n)) if abs(sd[i]) < 1e-15]
if dropped_indices:
for i in reversed(sorted(dropped_indices)):
n.pop(i)
print('popping... %s terms remaining' % len(n))
continue
if max_abserror > perc_error_allowed:
break # The last good run will be used
else:
print(max_abserror)
Ncoeffs = str(list(myoutput.beta)).lstrip('[').rstrip(']')
tcoeffs = str(n).lstrip('[').rstrip(']')
maxerror = max_abserror
if form == 'A':
code_template = textwrap.dedent(
"""
for (int i=1; i<={count:d}; i++)
{{
summer += N[i]*pow(theta,t[i]);
}}
return reduce.rho*(summer+1);
""".format(count=len(n))
)
elif form == 'B':
code_template = textwrap.dedent(
"""
for (int i=1; i<={count:d}; i++)
{{
summer += N[i]*pow(theta,t[i]);
}}
return reduce.rho*exp(reduce.T/T*summer);
""".format(count=len(n))
)
else:
raise ValueError
# Find the least significant entry (the one with the largest relative standard error)
# and remove it
n.pop(np.argmax(np.abs(sd / beta)))
# Remove elements that are not
template = textwrap.dedent(
"""
double {name:s}Class::rhosat{LV:s}(double T)
{{
// Maximum absolute error is {error:f} % between {Tmin:f} K and {Tmax:f} K
const double t[] = {{0, {tcoeffs:s}}};
const double N[] = {{0, {Ncoeffs:s}}};
double summer=0,theta;
theta=1-T/reduce.T;
\t{code:s}
}}
""")
the_string = template.format(tcoeffs=tcoeffs,
Ncoeffs=Ncoeffs,
name=ClassName,
Tmin=Tmin,
Tmax=TT[-1],
error=maxerror,
code=code_template,
LV=LV
)
f = open('anc.txt', 'a')
f.write(the_string)
f.close()
return the_string
def saturation_density(Ref,
ClassName,
form='A',
LV='L',
perc_error_allowed=0.3,
fName=None,
add_critical=True):
"""
Parameters
----------
Ref : string
The fluid name for the fluid that will be used to generate the saturation data
ClassName : The name of the class that will be used in the C++ code
form : string
If ``'A'``, use a term of the form
"""
if fName is None:
Tc = Props(Ref, 'Tcrit')
pc = Props(Ref, 'pcrit')
rhoc = Props(Ref, 'rhocrit')
Tmin = Props(Ref, 'Tmin')
print("%s %s %s" % (Ref, Tmin, Props(Ref, 'Ttriple')))
TT = np.linspace(Tmin, Tc - 1, 1000)
TT = list(TT)
# Add temperatures around the critical temperature
if add_critical:
for dT in [
1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1
]:
TT.append(Tc - dT)
TT = np.array(sorted(TT))
p = Props('P', 'T', TT, 'Q', 0, Ref)
rhoL = Props('D', 'T', TT, 'Q', 0, Ref)
rhoV = Props('D', 'T', TT, 'Q', 1, Ref)
else:
Tc = 423.27
pc = 3533
rhoc = 470
Tmin = 273
lines = open(fName, 'r').readlines()
TT, p, rhoL, rhoV = [], [], [], []
for line in lines:
_T, _p, _rhoL, _rhoV = line.split(' ')
TT.append(float(_T))
p.append(float(_p))
rhoL.append(float(_rhoL))
rhoV.append(float(_rhoV))
TT = np.array(TT)
# Start with a large library of potential powers
n = [
i / 6.0 for i in range(1, 200)
] #+[0.35+i/200.0 for i in range(1,70)]+[0.05+0.01*i for i in range(1,70)]
x = 1.0 - TT / Tc
if LV == 'L':
rho_EOS = rhoL
elif LV == 'V':
rho_EOS = rhoV
else:
raise ValueError
if form == 'A':
y = np.array(rho_EOS) / rhoc - 1
elif form == 'B':
y = (np.log(rho_EOS) - np.log(rhoc)) * TT / Tc
else:
raise ValueError
max_abserror = 0
while len(n) > 3:
print("%s %s" % (max_abserror, len(n)))
def f_p(B, x):
# B is a vector of the parameters.
# x is an array of the current x values.
return sum([_B * x**(_n) for _B, _n in zip(B, n)])
linear = Model(f_p)
mydata = Data(x, y)
myodr = ODR(mydata, linear, beta0=[0] * len(n))
myoutput = myodr.run()
beta = myoutput.beta
sd = myoutput.sd_beta
if form == 'A':
rho_fit = (f_p(myoutput.beta, x) + 1) * rhoc
elif form == 'B':
rho_fit = np.exp(f_p(myoutput.beta, x) * Tc / TT) * rhoc
else:
raise ValueError
print(
'first,last %s %s %s %s %s %s' %
(TT[0], TT[-1], rho_fit[0], rho_fit[-1], rho_EOS[0], rho_EOS[-1]))
max_abserror = np.max(np.abs(rho_fit / rho_EOS - 1)) * 100
dropped_indices = [i for i in range(len(n)) if abs(sd[i]) < 1e-15]
if dropped_indices:
for i in reversed(sorted(dropped_indices)):
n.pop(i)
print('popping... %s terms remaining' % len(n))
continue
if max_abserror > perc_error_allowed:
break # The last good run will be used
else:
print(max_abserror)
Ncoeffs = str(list(myoutput.beta)).lstrip('[').rstrip(']')
tcoeffs = str(n).lstrip('[').rstrip(']')
maxerror = max_abserror
if form == 'A':
code_template = textwrap.dedent("""
for (int i=1; i<={count:d}; i++)
{{
summer += N[i]*pow(theta,t[i]);
}}
return reduce.rho*(summer+1);
""".format(count=len(n)))
elif form == 'B':
code_template = textwrap.dedent("""
for (int i=1; i<={count:d}; i++)
{{
summer += N[i]*pow(theta,t[i]);
}}
return reduce.rho*exp(reduce.T/T*summer);
""".format(count=len(n)))
else:
raise ValueError
# Find the least significant entry (the one with the largest relative standard error)
# and remove it
n.pop(np.argmax(np.abs(sd / beta)))
#Remove elements that are not
template = textwrap.dedent("""
double {name:s}Class::rhosat{LV:s}(double T)
{{
// Maximum absolute error is {error:f} % between {Tmin:f} K and {Tmax:f} K
const double t[] = {{0, {tcoeffs:s}}};
const double N[] = {{0, {Ncoeffs:s}}};
double summer=0,theta;
theta=1-T/reduce.T;
\t{code:s}
}}
""")
the_string = template.format(tcoeffs=tcoeffs,
Ncoeffs=Ncoeffs,
name=ClassName,
Tmin=Tmin,
Tmax=TT[-1],
error=maxerror,
code=code_template,
LV=LV)
f = open('anc.txt', 'a')
f.write(the_string)
f.close()
return the_string
