def check(N=5000, param='D', fluid='R245fa'):
values = []
CP.enable_TTSE_LUT(fluid)
try:
CP.Props('D', 'P', CP.Props(fluid, 'ptriple') + 1, 'Q', 1, fluid)
except:
return []
# CP.set_TTSESinglePhase_LUT_size(fluid,500,500)
hmin, hmax, pmin, pmax = CP.get_TTSESinglePhase_LUT_range(fluid)
for i in range(N):
x1 = random.random()
h = x1 * hmin + (1 - x1) * hmax
x2 = random.random()
logp = x2 * log(pmin) + (1 - x2) * log(pmax)
p = exp(logp)
try:
CP.enable_TTSE_LUT(fluid)
value_withTTSE = CP.Props(param, 'P', p, 'H', h, fluid)
CP.disable_TTSE_LUT(fluid)
value_noTTSE = CP.Props(param, 'P', p, 'H', h, fluid)
values.append((h, p, value_withTTSE, value_noTTSE))
except ValueError:
pass
return values
def check_Props(parameter, SI_over_kSI):
CP.set_standard_unit_system(CoolProp.unit_systems_constants.UNIT_SYSTEM_SI)
val_SI = CP.Props(parameter,'T',300.0,'D',1.0,'R134a')
CP.set_standard_unit_system(CoolProp.unit_systems_constants.UNIT_SYSTEM_KSI)
val_kSI = CP.Props(parameter,'T',300.0,'D',1.0,'R134a')
try:
val_SI = val_SI()
val_kSI = val_kSI()
except:
pass
print(val_SI,val_kSI, val_SI/val_kSI - SI_over_kSI)
if abs(val_SI/val_kSI - SI_over_kSI) > 1e-12:
raise ValueError(val_SI/val_kSI-SI_over_kSI)
def critical_table(Fluid):
params = dict(
Tc=CP.Props(Fluid, 'Tcrit'),
rhoc=CP.Props(Fluid, 'rhocrit'),
pc=CP.Props(Fluid, 'pcrit'),
)
return textwrap.dedent("""
Critical Parameters
============================== ==============================
Temperature [K] {Tc:0.3f}
Density [kg/m\ :sup:`3`\ ] {rhoc:0.6f}
Pressure [kPa] {pc:0.5f}
============================== ==============================
""".format(**params))
def __sat_bounds(self, kind, smin=None, smax=None):
"""
Generates limits for the saturation line in either T or p determined
by 'kind'. If xmin or xmax are provided, values will be checked
against the allowable range for the EOS and an error might be
generated.
Returns a tuple containing (xmin, xmax)
"""
if kind == 'P':
name = 'pressure'
min_key = 'ptriple'
elif kind == 'T':
name = 'temperature'
min_key = 'Tmin'
fluid_min = CP.Props(self.fluid_ref, min_key)
fluid_crit = CP.Props(self.fluid_ref, ''.join([kind, 'crit']))
if smin is None:
smin = fluid_min + SMALL
elif smin > fluid_crit:
raise ValueError(''.join([
'Minimum ', name, ' cannot be greater than fluid critical ',
name, '.'
]))
if smax is None:
smax = fluid_crit - SMALL
elif smax > fluid_crit:
raise ValueError(''.join([
'Maximum ', name, ' cannot be greater than fluid critical ',
name, '.'
]))
smin = max(smin, fluid_min + SMALL)
smax = min(smax, fluid_crit - SMALL)
return (smin, smax)
def params_table(Fluid):
params = dict(mm=CP.Props(Fluid, 'molemass'),
Tt=CP.Props(Fluid, 'Ttriple'),
pt=CP.Props(Fluid, 'ptriple'),
Tmin=CP.Props(Fluid, 'Tmin'),
CAS=CP.get_CAS_code(Fluid),
ASHRAE=CP.get_ASHRAE34(Fluid))
return textwrap.dedent("""
Fluid Data
==========
Fluid Parameters
========================= ==============================
Mole Mass [kg/kmol] {mm:0.5f}
Triple Point Temp. [K] {Tt:0.3f}
Triple Point Press. [kPa] {pt:0.10g}
Minimum temperature [K] {Tmin:0.3f}
CAS number {CAS:s}
ASHRAE classification {ASHRAE:s}
========================= ==============================
""".format(**params))
def build_all_ancillaries():
for fluid in sorted(CoolProp.__fluids__):
print fluid
if fluid in ['SES36']:
build_ancillaries(fluid, Tlims = [CP.Props(fluid,'Ttriple'), CP.Props(fluid, 'Tcrit')-1])
elif fluid == 'R507A':
build_ancillaries(fluid, Tlims = [CP.Props(fluid,'Ttriple'), CP.Props(fluid, 'Tcrit')-0.1])
elif fluid == 'R407F':
build_ancillaries(fluid, Tlims = [CP.Props(fluid,'Ttriple'), CP.Props(fluid, 'Tcrit')-2])
else:
build_ancillaries(fluid)
def time_check(N, h, p, TTSE=False, mode='TTSE'):
if TTSE:
if mode == 'TTSE':
setup = "import CoolProp; import CoolProp.CoolProp as CP; CP.enable_TTSE_LUT('Water'); CP.set_TTSE_mode('Water','TTSE'); CP.Props('T','H',500,'P',10000,'Water'); IWater = CP.get_Fluid_index('Water'); from CoolProp.param_constants import iT,iH,iP,iD"
elif mode == 'BICUBIC':
setup = "import CoolProp; import CoolProp.CoolProp as CP; CP.enable_TTSE_LUT('Water'); CP.set_TTSE_mode('Water','BICUBIC'); CP.Props('T','H',500,'P',10000,'Water'); IWater = CP.get_Fluid_index('Water'); from CoolProp.param_constants import iT,iH,iP,iD"
else:
raise ValueError()
else:
setup = "import CoolProp.CoolProp as CP; IWater = CP.get_Fluid_index('Water'); CP.disable_TTSE_LUT('Water'); from CoolProp.param_constants import iT,iH,iP,iD"
time = timeit.Timer(
"CP.IProps(iD,iH," + str(h) + ",iP," + str(p) + ",IWater)",
setup).timeit(N) / N * 1e6
value = CP.Props('D', 'H', h, 'P', p, 'Water')
return time, value
def drawLines(Ref, lines, axis, plt_kwargs=None):
"""
Just an internal method to systematically plot values from
the generated 'line' dicts, method is able to cover the whole
saturation curve. Closes the gap at the critical point and
adds a marker between the two last points of bubble and
dew line if they reach up to critical point.
Returns the an array of line objects that can be used to change
the colour or style afterwards.
"""
if not plt_kwargs is None:
for line in lines:
line['opts'] = plt_kwargs
plottedLines = []
if len(lines) == 2 and (
'q' in str(lines[0]['type']).lower()
and 'q' in str(lines[1]['type']).lower()) and (
(0 == lines[0]['value'] and 1 == lines[1]['type']) or
(1 == lines[0]['value'] and 0 == lines[1]['type'])):
# We plot the saturation curve
bubble = lines[0]
dew = lines[1]
line, = axis.plot(bubble['x'], bubble['y'], **bubble['opts'])
plottedLines.extend([line])
line, = axis.plot(dew['x'], dew['y'], **dew['opts'])
plottedLines.extend([line])
# Do we need to test if this is T or p?
Tmax = min(bubble['kmax'], dew['kmax'])
if Tmax > CP.Props(Ref, 'Tcrit') - 2e-5:
axis.plot(numpy.r_[bubble['x'][-1], dew['x'][-1]],
numpy.r_[bubble['y'][-1],
dew['y'][-1]], **bubble['opts'])
#axis.plot((bubble['x'][-1]+dew['x'][-1])/2.,(bubble['y'][-1]+dew['y'][-1])/2.,'o',color='Tomato')
else:
for line in lines:
line, = axis.plot(line['x'], line['y'], **line['opts'])
plottedLines.extend([line])
return plottedLines
def _get_fluid_data(self, req_prop, prop1_name, prop2_name, prop1_vals,
prop2_vals):
"""
Calculates lines for constant iName (iVal) over an interval of xName
(xVal). Returns (x[],y[]) - a tuple of arrays containing the values
in x and y dimensions.
"""
if len(prop1_vals) != len(prop2_vals):
raise ValueError(''.join([
'We need the same number of x value ',
'arrays as iso quantities.'
]))
y_vals = []
x_vals = []
for i, p1_val in enumerate(prop1_vals):
x_vals.append(prop2_vals[i])
y_vals.append([
CP.Props(req_prop, prop1_name, p1_val, prop2_name, x,
self.fluid_ref) for x in prop2_vals[i]
])
return [x_vals, y_vals]
def check_Trho(N=5000, param='P', fluid='R245fa'):
values = []
CP.enable_TTSE_LUT(fluid)
try:
CP.Props('D', 'P', CP.Props(fluid, 'ptriple') + 1, 'Q', 1, fluid)
except:
return []
# CP.set_TTSESinglePhase_LUT_size(fluid,500,500)
hmin, hmax, pmin, pmax = CP.get_TTSESinglePhase_LUT_range(fluid)
for i in range(N):
x1 = random.random()
h = x1 * hmin + (1 - x1) * hmax
x2 = random.random()
logp = x2 * log(pmin) + (1 - x2) * log(pmax)
p = exp(logp)
try:
try:
# Get the T,rho from the EOS directly without the LUT
CP.disable_TTSE_LUT(fluid)
s = CP.Props('S', 'P', p, 'H', h, fluid)
T = CP.Props('T', 'P', p, 'H', h, fluid)
rho = CP.Props('D', 'P', p, 'H', h, fluid)
except:
print('EOS failed: %s %s' % (p, h))
raise
# Now get p,h from the T,rho
CP.enable_TTSE_LUT(fluid)
val = CP.Props(param, 'T', T, 'D', rho, fluid)
CP.disable_TTSE_LUT(fluid)
valREFPROP = CP.Props(param, 'T', T, 'D', rho, fluid)
# print T,rho,val,valREFPROP,(val/valREFPROP-1)*100
if abs(val - valREFPROP) > 0.00001:
raise ValueError
except ValueError:
print('TTSE failed: %s %s' % (T, rho))
values.append((T, rho, 0, 0))
pass
return values
def check_Pother(N=5000, param='T', other='S', fluid='R245fa'):
values = []
CP.enable_TTSE_LUT(fluid)
try:
CP.Props('D', 'P', CP.Props(fluid, 'ptriple') + 1, 'Q', 1, fluid)
except:
return []
# CP.set_TTSESinglePhase_LUT_size(fluid,500,500)
hmin, hmax, pmin, pmax = CP.get_TTSESinglePhase_LUT_range(fluid)
for i in range(N):
x1 = random.random()
h = x1 * hmin + (1 - x1) * hmax
x2 = random.random()
logp = x2 * log(pmin) + (1 - x2) * log(pmax)
p = exp(logp)
try:
try:
# Get the T,rho from the EOS directly without the LUT
CP.disable_TTSE_LUT(fluid)
s = CP.Props('S', 'P', p, 'H', h, fluid)
T = CP.Props('T', 'P', p, 'H', h, fluid)
rho = CP.Props('D', 'P', p, 'H', h, fluid)
except:
print('EOS failed: %s %s' % (p, h))
raise
# Now get p,h from the T,rho
CP.enable_TTSE_LUT(fluid)
if other == 'S':
other_val = s
elif other == 'T':
other_val = T
elif other == 'D':
other_val = rho
else:
raise ValueError
val = CP.Props(param, 'P', p, other, other_val, fluid)
except ValueError:
print('TTSE failed: %s %s' % (p, other_val))
values.append((p, other_val, 0, 0))
pass
return values
import CoolProp.CoolProp as CP
fluid = 'Propane'
print(CP.enable_TTSE_LUT(fluid))
print(CP.isenabled_TTSE_LUT(fluid))
print(CP.Props('H', 'P', 300, 'Q', 0, fluid))
print(CP.Props('H', 'P', 310, 'Q', 0, fluid))
print(CP.Props('H', 'P', 315, 'Q', 0, fluid))
#
fluid = 'TestSolution-0.3'
print(CP.enable_TTSE_LUT(fluid))
print(CP.isenabled_TTSE_LUT(fluid))
print(CP.Props('H', 'P', 3000, 'T', 280, fluid))
from CoolProp import CoolProp as CP
from PDSim.misc.datatypes import Collector
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from scipy.odr import *
import textwrap
fluid_REF = 'Propane'
Tcrit_REF = CP.Props(fluid_REF, 'Tcrit')
omega_REF = CP.Props(fluid_REF, "accentric")
molemass_REF = CP.Props(fluid_REF, 'molemass')
rhocrit_REF = CP.Props(fluid_REF, 'rhocrit')
Zcrit_REF = CP.DerivTerms('Z', Tcrit_REF, rhocrit_REF, fluid_REF)
fluid = 'DimethylEther'
molemass = CP.Props(fluid, 'molemass')
Ttriple = CP.Props(fluid, 'Ttriple')
Tcrit = CP.Props(fluid, 'Tcrit')
omega = CP.Props(fluid, "accentric")
rhocrit = CP.Props(fluid, 'rhocrit')
pcrit = CP.Props(fluid, 'pcrit')
Zcrit = CP.DerivTerms('Z', Tcrit, rhocrit, fluid)
N = 12
RHO, TTT, RHO0, TTT0 = Collector(), Collector(), Collector(), Collector()
rhomax = CP.Props('D', 'T', Ttriple, 'Q', 0, fluid)
#Build a database of "experimental" data
for T in np.linspace(Ttriple, Tcrit + 50, 80):
def Tr():
global ip, irho
for fluid, values in polynomial_in_Tr.iteritems():
axp = figp.add_subplot(Nrow, Ncol, ip)
ip += 1
axrho = figrho.add_subplot(Nrow, Ncol, irho)
irho += 1
axp.set_xlabel('T [K]')
axp.set_ylabel('p [Pa]')
axrho.set_xlabel('T [K]')
axrho.set_ylabel('rho [mol/m$^3$]')
axp.set_title(fluid + ' - ' +
str(round(CP.Props(fluid, "molemass"), 2)))
axrho.set_title(fluid)
fname = os.path.join('fluids', fluid + '.json')
j = json.load(open(fname, 'r'))
for part in values['parts']:
if 'T_min' not in part:
part['T_min'] = round(CP.Props(fluid, "Tmin"), 4)
values['type'] = 'polynomial_in_Tr'
j['ANCILLARIES']['melting_line'] = values
fp = open(fname, 'w')
from package_json import json_options
fp.write(json.dumps(j, **json_options))
fp.close()
if fluid == 'Ethylene':
T = [
104.003, 104.059, 104.13, 104.2, 104.27, 104.41, 104.55,
104.69, 104.83, 104.969, 105.108, 105.386, 106.077, 106.764,
107.446, 111.384, 119.283, 127.136, 158.146, 188.621
]
p = np.array([
0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 10, 15, 20, 25, 50, 75,
100, 200, 300
]) * 1e6
axp.plot(T, p, '*')
# if not isinstance(values, list):
# values = [values]
# df = pandas.read_csv('melting_curves/'+fluid+'.mlt',names=['T','p','rho'])
# axp.plot(df['T'], df['p'], 'o', mfc='none')
# x,y = plot_rho(df['T'],df['rho'],fit = True)
# axrho.plot(x,y, 'o', mfc='none')
#
# else:
# for i in ['I','II']:
# df = pandas.read_csv('melting_curves/'+fluid+'-'+i+'.mlt',names=['T','p','rho'])
# axp.plot(df['T'], df['p'], 'o', mfc='none')
# x,y = plot_rho(df['T'],df['rho'],fit = True)
# axrho.plot(x,y, 'o', mfc='none')
T_m = values['T_m']
for i, value in enumerate(values['parts']):
Tmin = value.get('T_min', CP.Props(fluid, "Tmin"))
Tmax = value['T_max']
T = np.linspace(Tmin, Tmax, 200)
a = value['a']
t = value['t']
T_t = value['T_0']
p_t = value['p_0']
RHS = 0
for i in range(len(a)):
RHS += a[i] * ((T / T_t)**t[i] - 1)
p = p_t * (RHS + 1)
axp.plot(T, p)
cc = 1.75
aa = 3e8 #(101325-p_0)/((T_m/T_0)**cc-1)
pt = CP.Props(fluid, 'ptriple')
pp = pt + aa * ((T / Tmin)**cc - 1)
axp.plot(T_m, 101325, '*')
axp.plot(T, pp, '--')
print fluid, CP.Props(fluid, "molemass"), CP.Props(
fluid, 'accentric'), pp[-1] / p[-1] - 1
def theta():
global ip, irho
for fluid, values in polynomial_in_theta.iteritems():
axp = figp.add_subplot(Nrow, Ncol, ip)
ip += 1
axrho = figrho.add_subplot(Nrow, Ncol, irho)
irho += 1
axp.set_xlabel('T [K]')
axp.set_ylabel('p [Pa]')
axrho.set_xlabel('T [K]')
axrho.set_ylabel('rho [mol/m$^3$]')
axp.set_title(fluid + ' - ' +
str(round(CP.Props(fluid, "molemass"), 2)))
axrho.set_title(fluid)
fname = os.path.join('fluids', fluid + '.json')
j = json.load(open(fname, 'r'))
for part in values['parts']:
if 'T_min' not in part:
part['T_min'] = round(CP.Props(fluid, "Tmin"), 4)
values['type'] = 'polynomial_in_Theta'
j['ANCILLARIES']['melting_line'] = values
fp = open(fname, 'w')
from package_json import json_options
fp.write(json.dumps(j, **json_options))
fp.close()
T_m = values['T_m']
for value in values['parts']:
a = value['a']
t = value['t']
T_t = value['T_0']
p_t = value['p_0']
Tmin = T_t
Tmax = value['T_max']
T = np.linspace(Tmin, Tmax, 200)
RHS = 0
for i in range(len(a)):
RHS += a[i] * (T / T_t - 1)**t[i]
p = p_t * (RHS + 1)
#df = pandas.read_csv('melting_curves/' + fluid + '.mlt', names=['T','p','rho'])
#axp.plot(df['T'], df['p'], 'o', mfc='none')
axp.plot(T, p)
#x,y = plot_rho(df['T'],df['rho'],fit = True)
#axrho.plot(x,y, 'o', mfc='none')
cc = 1.75
aa = 3e8 #(101325-p_0)/((T_m/T_0)**cc-1)
pt = CP.Props(fluid, 'ptriple')
pp = pt + aa * ((T / Tmin)**cc - 1)
axp.plot(T_m, 101325, '*')
axp.plot(T, pp, '--')
print fluid, CP.Props(fluid, "molemass"), CP.Props(
fluid, 'accentric'), pp[-1] / p[-1] - 1
def __set_axis_limits(self, swap_xy):
"""
Generates limits for the axes in terms of x,y defined by 'plot'
based on temperature and pressure.
Returns a tuple containing ((xmin, xmax), (ymin, ymax))
"""
# Get current axis limits, be sure to set those before drawing isolines
# if no limits are set, use triple point and critical conditions
X = [
CP.Props(self.graph_type[1], 'T',
1.5 * CP.Props(self.fluid_ref, 'Tcrit'), 'P',
CP.Props(self.fluid_ref, 'ptriple'), self.fluid_ref),
CP.Props(self.graph_type[1], 'T',
1.1 * CP.Props(self.fluid_ref, 'Tmin'), 'P',
1.5 * CP.Props(self.fluid_ref, 'pcrit'), self.fluid_ref),
CP.Props(self.graph_type[1], 'T',
1.5 * CP.Props(self.fluid_ref, 'Tcrit'), 'P',
1.5 * CP.Props(self.fluid_ref, 'pcrit'), self.fluid_ref),
CP.Props(self.graph_type[1], 'T',
1.1 * CP.Props(self.fluid_ref, 'Tmin'), 'P',
CP.Props(self.fluid_ref, 'ptriple'), self.fluid_ref)
]
Y = [
CP.Props(self.graph_type[0], 'T',
1.5 * CP.Props(self.fluid_ref, 'Tcrit'), 'P',
CP.Props(self.fluid_ref, 'ptriple'), self.fluid_ref),
CP.Props(self.graph_type[0], 'T',
1.1 * CP.Props(self.fluid_ref, 'Tmin'), 'P',
1.5 * CP.Props(self.fluid_ref, 'pcrit'), self.fluid_ref),
CP.Props(self.graph_type[0], 'T',
1.1 * CP.Props(self.fluid_ref, 'Tcrit'), 'P',
1.5 * CP.Props(self.fluid_ref, 'pcrit'), self.fluid_ref),
CP.Props(self.graph_type[0], 'T',
1.5 * CP.Props(self.fluid_ref, 'Tmin'), 'P',
CP.Props(self.fluid_ref, 'ptriple'), self.fluid_ref)
]
limits = [[min(X), max(X)], [min(Y), max(Y)]]
if not self.axis.get_autoscalex_on():
limits[0][0] = max([limits[0][0], min(self.axis.get_xlim())])
limits[0][1] = min([limits[0][1], max(self.axis.get_xlim())])
limits[1][0] = max([limits[1][0], min(self.axis.get_ylim())])
limits[1][1] = min([limits[1][1], max(self.axis.get_ylim())])
self.axis.set_xlim(limits[0])
self.axis.set_ylim(limits[1])
return limits
def simon():
global ip, irho
for fluid, values in Simon_curves.iteritems():
axp = figp.add_subplot(Nrow, Ncol, ip)
ip += 1
axrho = figrho.add_subplot(Nrow, Ncol, irho)
irho += 1
axp.set_xlabel('T [K]')
axp.set_ylabel('p [Pa]')
axrho.set_xlabel('T [K]')
axrho.set_ylabel('rho [mol/m$^3$]')
axp.set_title(fluid + ' - ' +
str(round(CP.Props(fluid, "molemass"), 2)))
axrho.set_title(fluid)
fname = os.path.join('fluids', fluid + '.json')
j = json.load(open(fname, 'r'))
for part in values['parts']:
if 'T_min' not in part:
part['T_min'] = round(CP.Props(fluid, "Tmin"), 4)
values['type'] = 'Simon'
j['ANCILLARIES']['melting_line'] = values
fp = open(fname, 'w')
from package_json import json_options
fp.write(json.dumps(j, **json_options))
fp.close()
# if not isinstance(values, list):
# values = [values]
# df = pandas.read_csv('melting_curves/'+fluid+'.mlt',names=['T','p','rho'])
# axp.plot(df['T'], df['p'], 'o', mfc='none')
# x,y = plot_rho(df['T'],df['rho'],fit = True)
# axrho.plot(x,y, 'o', mfc='none')
# else:
# for i in ['I','II']:
# df = pandas.read_csv('melting_curves/'+fluid+'-'+i+'.mlt',names=['T','p','rho'])
# axp.plot(df['T'], df['p'], 'o', mfc='none')
# x,y = plot_rho(df['T'],df['rho'],fit = True)
# axrho.plot(x,y, 'o', mfc='none')
T_m = values['T_m']
for i, value in enumerate(values['parts']):
Tmin = value.get('T_min', CP.Props(fluid, "Tmin"))
Tmax = value['T_max']
T = np.linspace(Tmin, Tmax, 200)
T_0 = value['T_0']
p_0 = value['p_0']
a = value['a']
c = value['c']
p = p_0 + a * ((T / T_0)**c - 1)
axp.plot(T, p)
cc = 1.75
aa = 3e8 #(101325-p_0)/((T_m/T_0)**cc-1)
pt = CP.Props(fluid, 'ptriple')
pp = pt + aa * ((T / Tmin)**cc - 1)
axp.plot(T_m, 101325, '*')
axp.plot(T, pp, '--')
print fluid, CP.Props(fluid, "molemass"), CP.Props(
fluid, 'accentric'), pp[-1] / p[-1] - 1
from pyrp.refpropClasses import RefpropSI
import CoolProp.CoolProp as cp
p = 30000
T = 273.15
ref = False
if ref:
xkg = [0.473194694453358, 0.205109095413331, 0.321696210133311]
names = "R32|R125|R134a"
RP = RefpropSI()
RP.SETUPFLEX(xkg=xkg, FluidNames=names)
T_A, p_A, D_A, Dl_A, Dv_A, q_A, e_A, h_A, s_A, cv_A, cp_A, w_A = RP.PQFLSH(
p, 0)
T_B, p_B, D_B, Dl_B, Dv_B, q_B, e_B, h_B, s_B, cv_B, cp_B, w_B = RP.PQFLSH(
p, 1)
T_C, p_C, D_C, Dl_C, Dv_C, q_C, e_C, h_C, s_C, cv_C, cp_C, w_C = RP.TQFLSH(
T, 0)
hlb = h_A / 1000.
hrb = h_B / 1000.
h200 = h_C / 1000.
print "Refprop: ", hlb, hrb, h200
else:
R407F = 'REFPROP-MIX:R32[0.473194694453358]&R125[0.205109095413331]&R134a[0.321696210133311]'
#R407F='REFPROP-MIX:R32[0.651669604033581]&R125[0.122438378639971]&R134a[0.225892017326446]'
hlb = cp.Props('H', 'P', 30, 'Q', 0, R407F) # 30 kPa saturated liquid
hrb = cp.Props('H', 'P', 30, 'Q', 1, R407F) # 30 kPa saturated vapour
h200 = cp.Props('H', 'T', 273.15, 'Q', 0,
R407F) # saturated liquid at 0C IIR
print "CoolProp: ", hlb, hrb, h200
def PropertyConsistency(Fluid):
Tmax = CP.Props(Fluid, "Tcrit") - 1 if Fluid == 'SES36' else CP.Props(
Fluid, "Tcrit") - 0.01
return textwrap.dedent("""
Check of p,h and p,s as inputs (X: Failure .: Success)
=================================================================
.. plot::
from CoolProp.Plots.Plots import Ph,Ps
from CoolProp.CoolProp import Props
from matplotlib import pyplot as plt
import numpy as np
Ref = "{Fluid:s}"
fig = plt.figure(figsize=(10,5))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
Tmin = Props(Ref,'Tmin')+3
pmin = Props('P','T',Tmin,'Q',0,Ref)
pmax = Props(Ref,'pcrit')*2
hmin = Props('H','T',Tmin,'Q',0,Ref)
hmax = 2*Props('H','T',Props(Ref,'Tcrit')-1,'Q',1,Ref)-hmin
smin = Props('S','T',Tmin,'Q',0,Ref)
smax = 2*Props('S','T',Props(Ref,'Tcrit')-1,'Q',1,Ref)-smin
Ph(Ref, axis = ax1, Tmin = Tmin, Tmax = {Tmax:f})
Ps(Ref, axis = ax2, Tmin = Tmin, Tmax = {Tmax:f})
for p in np.linspace(pmin,pmax,10):
for h in np.linspace(hmin,hmax):
_bad = False
try:
T = Props('T','H',h,'P',p,Ref)
rho = Props('D','H',h,'P',p,Ref)
hEOS = Props('H','T',T,'D',rho,Ref)
except ValueError:
_bad = True
if _bad or abs(hEOS/h-1)>1e-6:
ax1.plot(h,p,'x',ms = 10)
else:
ax1.plot(h,p,'k.', ms = 1)
for p in np.linspace(pmin,pmax,10):
for s in np.linspace(smin,smax):
_bad = False
try:
T = Props('T','S',s,'P',p,Ref)
rho = Props('D','S',s,'P',p,Ref)
sEOS = Props('S','T',T,'D',rho,Ref)
except ValueError:
_bad = True
if _bad or abs(sEOS/s-1)>1e-6:
ax2.plot(s,p,'x',ms = 10)
else:
ax2.plot(s,p,'k.', ms = 1)
plt.tight_layout()
""".format(Fluid=Fluid, Tmax=Tmax))
gaseous = CP.DerivTerms(key, T[2], D[2], fluid)
supercrit = CP.DerivTerms(key, T[3], D[3], fluid)
print '{:<25}: {:>10.5f}; {:>10.5f}; {:>10.5f}; {:>10.5f}'.format(
key, liquid, twophase, gaseous, supercrit)
for key in keys:
liquid = CP.DerivTermsU(key, T[0], D[0], fluid, 'SI')
twophase = CP.DerivTermsU(key, T[1], D[1], fluid, 'SI')
gaseous = CP.DerivTermsU(key, T[2], D[2], fluid, 'SI')
supercrit = CP.DerivTermsU(key, T[3], D[3], fluid, 'SI')
print '{:<25}: {:>10.5f}; {:>10.5f}; {:>10.5f}; {:>10.5f}'.format(
key, liquid, twophase, gaseous, supercrit)
print
print "Testing Props: "
for key in keys:
liquid = CP.Props(key, "T", float(T[0]), "D", float(D[0]), fluid)
twophase = CP.Props(key, "T", float(T[1]), "D", float(D[1]), fluid)
gaseous = CP.Props(key, "T", float(T[2]), "D", float(D[2]), fluid)
supercrit = CP.Props(key, "T", float(T[3]), "D", float(D[3]), fluid)
print '{:<25}: {:>10.5f}; {:>10.5f}; {:>10.5f}; {:>10.5f}'.format(
key, liquid, twophase, gaseous, supercrit)
for key in keys:
liquid = CP.PropsU(key, "T", float(T[0]), "D", float(D[0]), fluid, 'SI')
twophase = CP.PropsU(key, "T", float(T[1]), "D", float(D[1]), fluid, 'SI')
gaseous = CP.PropsU(key, "T", float(T[2]), "D", float(D[2]), fluid, 'SI')
supercrit = CP.PropsU(key, "T", float(T[3]), "D", float(D[3]), fluid, 'SI')
print '{:<25}: {:>10.5f}; {:>10.5f}; {:>10.5f}; {:>10.5f}'.format(
key, liquid, twophase, gaseous, supercrit)
#!/usr/bin/python
import sys
import CoolProp.CoolProp as CP
# print "{0:14.8f}".format(CP.Props('V','D',13,'P',500,'n-Pentane'))
# print "{0:14.8f}".format(CP.Props('V','H',158,'P',1000,'TX22'))
#T = 300
T = float(sys.argv[1])
P = float(sys.argv[2])
print("Temperature: " + str(T))
print("Pressure: " + str(P))
print("")
print("Viscosity: ")
print("{0:14.8f}".format(CP.Props('V', 'T', T, 'P', P, 'SecCoolSolution-20%')))
print("{0:14.8f}".format(CP.Props('V', 'T', T, 'P', P, 'MEG-20%')))
print("{0:14.8f}".format(CP.Props('V', 'T', T, 'P', P, 'EG-20%')))
print("{0:14.8f}".format(CP.Props('V', 'T', T, 'P', P, 'water')))
# print
# print "{0:14.8f}".format(CP.Props('L','D',13,'P',500,'n-Pentane'))
# print "{0:14.8f}".format(CP.Props('L','H',158,'P',P,'TX22'))
print("")
print("Conductivity: ")
print("{0:14.8f}".format(CP.Props('L', 'T', T, 'P', P, 'SecCoolSolution-20%')))
print("{0:14.8f}".format(CP.Props('L', 'T', T, 'P', P, 'MEG-20%')))
print("{0:14.8f}".format(CP.Props('L', 'T', T, 'P', P, 'EG-20%')))
print("{0:14.8f}".format(CP.Props('L', 'T', T, 'P', P, 'water')))
print("")
print("Density: ")
print("{0:14.8f}".format(CP.Props('D', 'T', T, 'P', P, 'SecCoolSolution-20%')))
print("{0:14.8f}".format(CP.Props('D', 'T', T, 'P', P, 'MEG-20%')))
print("{0:14.8f}".format(CP.Props('D', 'T', T, 'P', P, 'EG-20%')))
print("{0:14.8f}".format(CP.Props('D', 'T', T, 'P', P, 'water')))
from CoolProp.Plots import Ph
import CoolProp.CoolProp as CP
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
import matplotlib.ticker
import numpy as np
import random
fig = plt.figure(figsize=(10,5))
ax1 = fig.add_axes((0.08,0.1,0.32,0.83))
ax2 = fig.add_axes((0.50,0.1,0.32,0.83))
Ref = 'R245fa'
T = np.linspace(CP.Props(Ref,'Tmin')+0.1,CP.Props(Ref,'Tcrit')-0.01,300)
pV = CP.Props('P','T',T,'Q',1,Ref)
hL = CP.Props('H','T',T,'Q',0,Ref)
hV = CP.Props('H','T',T,'Q',1,Ref)
HHH1, PPP1, EEE1 = [], [], []
HHH2, PPP2, EEE2 = [], [], []
cNorm = colors.LogNorm(vmin=1e-12, vmax=10)
scalarMap = cmx.ScalarMappable(norm = cNorm, cmap = plt.get_cmap('jet'))
for a_useless_counter in range(40000):
h = random.uniform(100,590)
p = 10**random.uniform(np.log10(100),np.log10(7000))
import numpy as np
import matplotlib.pyplot as plt
from scipy.odr import *
import textwrap
#
#fluid = 'Propane'
#Rfluid = 'REFPROP-propane'
#e_k = 263.88
#sigma = 0.49748
#
fluid = 'DimethylEther'
Rfluid = 'REFPROP-DME'
e_k = 329.72
sigma = 0.5529
molemass = CP.Props(fluid, 'molemass')
Ttriple = CP.Props(fluid, 'Ttriple')
Tcrit = CP.Props(fluid, 'Tcrit')
rhocrit = CP.Props(fluid, 'rhocrit')
n = 6
m = 3
NP = 1
Nb = 0
N = (n - 1) * (m + 1) + 3 + Nb
mu, mu_dilute, RHO, TTT = Collector(), Collector(), Collector(), Collector()
rhomax = CP.Props('D', 'T', Ttriple, 'Q', 0, fluid)
# Build a database of "experimental" data
for T in np.linspace(Ttriple, Tcrit + 30, 400):
if not os.path.exists('hsancillaries.json'):
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('multipage.pdf')
jj = {}
for i, fluid in enumerate(sorted(CoolProp.__fluids__)):
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
plt.suptitle(fluid)
print i, fluid
N = 10000
Tc = CP.Props(fluid, 'Tcrit')
rhoc = CP.Props(fluid, 'rhocrit')
MM = CP.Props(fluid, 'molemass')
try:
T = np.r_[np.linspace(Tc - 0.1, CP.Props(fluid, 'Tmin'),
N)] #, np.linspace(Tc-0.1, Tc-1e-1, N)]
hfg = (CP.PropsSI('H', 'T', T, 'Q', 1, fluid) -
CP.PropsSI('H', 'T', T, 'Q', 0, fluid)) * MM / 1000
sfg = (CP.PropsSI('S', 'T', T, 'Q', 1, fluid) -
CP.PropsSI('S', 'T', T, 'Q', 0, fluid)) * MM / 1000
except BaseException as E:
print E
continue
try:
p = CP.PropsSI('P', 'T', T, 'Q', 0, fluid)
from CoolProp import CoolProp as CP
from PDSim.misc.datatypes import Collector
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fluid = 'R245fa'
Ttriple = CP.Props(fluid, 'Ttriple')
Tcrit = CP.Props(fluid, 'Tcrit')
RHO, TTT, RHO0, TTT0, ERR = Collector(), Collector(), Collector(), Collector(
), Collector()
rhomax = CP.Props('D', 'T', Ttriple, 'Q', 0, 'R245fa')
# Build a database of "experimental" data
for T in np.linspace(Ttriple, Tcrit + 50, 80):
for rho in np.linspace(1e-10, rhomax, 80):
if (T > Tcrit or rho > CP.rhosatL_anc(fluid, T)
or rho < CP.rhosatV_anc(fluid, T)):
h = CP.Props('H', 'T', T, 'D', rho, 'R245fa')
p = CP.Props('P', 'T', T, 'D', rho, 'R245fa')
RHO << rho
TTT << T
ERR << h
TTT0 << p
fig = plt.figure()
ax1 = fig.add_subplot(121, projection='3d')
ax1.scatter(np.array(RHO.vec), np.array(TTT.vec), ERR.vec)
ax2 = fig.add_subplot(122, projection='3d')
from __future__ import print_function
import CoolProp
import CoolProp.CoolProp as CP
print('CoolProp version: ', CoolProp.__version__)
print('CoolProp gitrevision: ', CoolProp.__gitrevision__)
print('CoolProp fluids: ', CoolProp.__fluids__)
print(' ')
print('************ USING EOS *************')
print(' ')
print('FLUID STATE INDEPENDENT INPUTS')
print('Critical Density Propane:', CP.Props('Propane', 'rhocrit'), 'kg/m^3')
print('TWO PHASE INPUTS (Pressure)')
print('Density of saturated liquid Propane at 101.325 kPa:',
CP.Props('D', 'P', 101.325, 'Q', 0, 'Propane'), 'kg/m^3')
print('Density of saturated vapor R290 at 101.325 kPa:',
CP.Props('D', 'P', 101.325, 'Q', 1, 'R290'), 'kg/m^3')
print('TWO PHASE INPUTS (Temperature)')
print('Density of saturated liquid Propane at 300 K:',
CP.Props('D', 'T', 300, 'Q', 0, 'Propane'), 'kg/m^3')
print('Density of saturated vapor R290 at 300 K:',
CP.Props('D', 'T', 300, 'Q', 1, 'R290'), 'kg/m^3')
p = CP.Props('P', 'T', 300, 'D', 1, 'Propane')
h = CP.Props('H', 'T', 300, 'D', 1, 'Propane')
T = CP.Props('T', 'P', p, 'H', h, 'Propane')
D = CP.Props('D', 'P', p, 'H', h, 'Propane')
def compareProperty(fluid="", p=0, what=""):
global c_diff, c_unit, c_exce
if p == 0:
p = 0.75 * CP.Props(fluid, "pcrit")
Delta_T = 50
T_bub = CP.Props("T", "P", p, "Q", 0, fluid)
T_dew = CP.Props("T", "P", p, "Q", 1, fluid)
h_bub = CP.Props("H", "P", p, "Q", 0, fluid)
h_dew = CP.Props("H", "P", p, "Q", 1, fluid)
T_1 = T_bub - Delta_T
if T_1 < CP.Props(fluid, "Tmin"):
T_1 = CP.Props(fluid, "Tmin") + 0.5 * (T_bub - CP.Props(fluid, "Tmin"))
T_2 = T_dew + Delta_T
h_1 = CP.Props("H", "P", p, "T", T_1, fluid)
h_2 = CP.Props("H", "P", p, "T", T_2, fluid)
h_liq = numpy.linspace(h_1, h_bub, num=100)
h_vap = numpy.linspace(h_dew, h_2, num=100)
T_liq = CP.Props("T", "P", p, "H", h_liq, fluid) - T_bub
T_vap = CP.Props("T", "P", p, "H", h_vap, fluid) - T_dew
X_liq_STDV = CP.Props(what, "P", p, "H", h_liq, fluid)
X_vap_STDV = CP.Props(what, "P", p, "H", h_vap, fluid)
CP.enable_TTSE_LUT(fluid)
X_liq_TTSE = CP.Props(what, "P", p, "H", h_liq, fluid)
X_vap_TTSE = CP.Props(what, "P", p, "H", h_vap, fluid)
if numpy.max([X_liq_STDV / X_liq_TTSE, X_vap_STDV / X_vap_TTSE
]) > 1.25 or numpy.min([
X_liq_STDV / X_liq_TTSE, X_vap_STDV / X_vap_TTSE
]) < 0.75:
c_diff += 1
print ""
print "There were problems with " + what + " for " + fluid
print "Relative difference liquid: " + str(
numpy.mean((X_liq_STDV - X_liq_TTSE) / X_liq_STDV))
print "Relative difference vapour: " + str(
numpy.mean((X_vap_STDV - X_vap_TTSE) / X_vap_STDV))
print "Average factor liquid: " + str(
numpy.mean(X_liq_STDV / X_liq_TTSE))
print "Average factor vapour: " + str(
numpy.mean(X_vap_STDV / X_vap_TTSE))
def props(cp1,cp2,cp3,cp4,cp5,cp6):
return cp.Props(cp1,cp2,cp3,cp4,cp5,cp6)
import matplotlib.pyplot as plt
import CoolProp.CoolProp as CP
import itertools, numpy as np
variables = ['T','P','H','S','U','D']
for fluid in ['Water','CO2','n-Propane']:
T = np.linspace(CP.Props(fluid,'Tmin'),CP.Props(fluid,'Tcrit'),1000)
fig = plt.figure(1, figsize=(10, 10), dpi=100)
for i, types in enumerate(itertools.combinations(variables,2)):
ax = plt.subplot(5, 3, i+1)
types = list(types)
if types[0] in ['T','P'] and types != ['T','P']:
types[0],types[1] = types[1],types[0]
xL = CP.Props(types[0],'T',T,'Q',0,fluid)
yL = CP.Props(types[1],'T',T,'Q',0,fluid)
xV = CP.Props(types[0],'T',T,'Q',1,fluid)
yV = CP.Props(types[1],'T',T,'Q',1,fluid)
xc = CP.Props(types[0],'T',CP.Props(fluid,'Tcrit'),'Q',1,fluid)
yc = CP.Props(types[1],'T',CP.Props(fluid,'Tcrit'),'Q',1,fluid)
ax.plot(xL,yL,'k')
ax.plot(xV,yV,'k')
ax.plot(xc,yc,'o')
if types[0] in ['P','D']:
ax.set_xscale('log')
from __future__ import division
from CoolProp import CoolProp as CP
Fluid = 'CO2'
R = 8.314472 / CP.Props(Fluid, 'molemass')
Tc = CP.Props(Fluid, 'Tcrit')
pc = CP.Props(Fluid, 'pcrit')
w = CP.Props(Fluid, 'accentric')
a = 0.457235 * R**2 * Tc**2 / pc
b = 0.077796 * R * Tc / pc
kappa = 0.37464 + 1.54226 * w - 0.26992 * w**2
T = 298.15
rho = 1000
v = 1 / rho
Tr = T / Tc
alpha = (1 + kappa * (1 - Tr**0.5))**2
p = R * T / (v - b) - a * alpha / (v**2 + 2 * b * v - b**2)
print("%s %s" % (p, CP.Props('P', 'T', T, 'D', rho, Fluid)))
