def initialise_fluids(self):
"""Fluid initialisation for fluid mixture at outlet of the node."""
num_fl = {}
for o in self.outl:
num_fl[o] = num_fluids(o.fluid.val)
for i in self.inl:
num_fl[i] = num_fluids(i.fluid.val)
ls = []
if any(num_fl.values()) and not all(num_fl.values()):
for conn, num in num_fl.items():
if num == 1:
ls += [conn]
for c in ls:
for fluid in self.nw_fluids:
for o in self.outl:
if not o.fluid.val_set[fluid]:
o.fluid.val[fluid] = c.fluid.val[fluid]
for i in self.inl:
if not i.fluid.val_set[fluid]:
i.fluid.val[fluid] = c.fluid.val[fluid]
for o in self.outl:
o.target.propagate_fluid_to_target(o, o.target)
def initialise_fluids(self, nw):
r"""
Fluid initialisation for fluid mixture at outlet of the node.
Parameters
----------
nw : tespy.networks.network
Network using this component object.
"""
num_fl = {}
for o in self.outl:
num_fl[o] = num_fluids(o.fluid.val)
for i in self.inl:
num_fl[i] = num_fluids(i.fluid.val)
ls = []
if any(num_fl.values()) and not all(num_fl.values()):
for conn, num in num_fl.items():
if num == 1:
ls += [conn]
for c in ls:
for fluid in nw.fluids:
for o in self.outl:
if not o.fluid.val_set[fluid]:
o.fluid.val[fluid] = c.fluid.val[fluid]
for i in self.inl:
if not i.fluid.val_set[fluid]:
i.fluid.val[fluid] = c.fluid.val[fluid]
def s_mix_ph(flow, T0=300):
r"""
Calculates the entropy from pressure and enthalpy.
Parameters
----------
flow : list
Fluid property vector containing mass flow, pressure, enthalpy and
fluid composition.
Returns
-------
s : float
Specific entropy s / (J/(kgK)).
Note
----
First, check if fluid property has been memorised already.
If this is the case, return stored value, otherwise calculate value and
store it in the memorisation class.
Uses CoolProp interface for pure fluids, newton algorithm for mixtures:
.. math::
s_{mix}\left(p,h\right) = s\left(p,T_{mix}(p,h)\right)
"""
# check if fluid properties have been calculated before
fl = tuple(flow[3].keys())
a = memorise.s_ph[fl][:, 0:-1]
b = np.array([flow[1], flow[2]] + list(flow[3].values()))
ix = np.where(np.all(abs(a - b) <= err, axis=1))[0]
if ix.size == 1:
# known fluid properties
s = memorise.s_ph[fl][ix, -1][0]
memorise.s_ph_f[fl] += [s]
return s
else:
# unknown fluid properties
if num_fluids(flow[3]) > 1:
# calculate the fluid properties for fluid mixtures
val = s_mix_pT(flow, T_mix_ph(flow, T0=T0))
new = np.array([[flow[1], flow[2]] + list(flow[3].values()) +
[val]])
# memorise the newly calculated value
memorise.s_ph[fl] = np.append(memorise.s_ph[fl], new, axis=0)
return val
else:
# calculate fluid property for pure fluids
for fluid, x in flow[3].items():
if x > err:
val = s_ph(flow[1], flow[2], fluid)
new = np.array([[flow[1], flow[2]] +
list(flow[3].values()) + [val]])
# memorise the newly calculated value
memorise.s_ph[fl] = np.append(memorise.s_ph[fl],
new,
axis=0)
return val
def T_mix_ps(flow, s, T0=300):
r"""
Calculates the temperature from pressure and entropy.
Parameters
----------
flow : list
Fluid property vector containing mass flow, pressure, enthalpy and
fluid composition.
s : float
Entropy of flow in J / (kgK).
Returns
-------
T : float
Temperature T / K.
Note
----
First, check if fluid property has been memorised already.
If this is the case, return stored value, otherwise calculate value and
store it in the memorisation class.
Uses CoolProp interface for pure fluids, newton algorithm for mixtures:
.. math::
T_{mix}\left(p,s\right) = T_{i}\left(p,s_{i}\right)\;
\forall i \in \text{fluid components}\\
s_{i} = s \left(pp_{i}, T_{mix} \right)\\
pp: \text{partial pressure}
"""
# check if fluid properties have been calculated before
fl = tuple(flow[3].keys())
a = memorise.T_ps[fl][:, 0:-1]
b = np.array([flow[1], flow[2]] + list(flow[3].values()) + [s])
ix = np.where(np.all(abs(a - b) <= err, axis=1))[0]
if ix.size == 1:
# known fluid properties
T = memorise.T_ps[fl][ix, -1][0]
memorise.T_ps_f[fl] += [T]
return T
else:
# unknown fluid properties
if num_fluids(flow[3]) > 1:
# calculate the fluid properties for fluid mixtures
if T0 < 70:
T0 = 300
val = newton(s_mix_pT,
ds_mix_pdT,
flow,
s,
val0=T0,
valmin=70,
valmax=3000,
imax=10)
new = np.array([[flow[1], flow[2]] + list(flow[3].values()) +
[s, val]])
# memorise the newly calculated value
memorise.T_ps[fl] = np.append(memorise.T_ps[fl], new, axis=0)
return val
else:
# calculate fluid property for pure fluids
msg = ('The calculation of temperature from pressure and entropy '
'for pure fluids should not be required, as the '
'calculation is always possible from pressure and '
'enthalpy. If there is a case, where you need to calculate '
'temperature from these properties, please inform us: '
'https://github.com/oemof/tespy.')
logging.error(msg)
raise ValueError(msg)
def T_mix_ph(flow, T0=300):
r"""
Calculates the temperature from pressure and enthalpy.
Parameters
----------
flow : list
Fluid property vector containing mass flow, pressure, enthalpy and
fluid composition.
Returns
-------
T : float
Temperature T / K.
Note
----
First, check if fluid property has been memorised already.
If this is the case, return stored value, otherwise calculate value and
store it in the memorisation class.
Uses CoolProp interface for pure fluids, newton algorithm for mixtures:
.. math::
T_{mix}\left(p,h\right) = T_{i}\left(p,h_{i}\right)\;
\forall i \in \text{fluid components}\\
h_{i} = h \left(pp_{i}, T_{mix} \right)\\
pp: \text{partial pressure}
"""
# check if fluid properties have been calculated before
fl = tuple(flow[3].keys())
a = memorise.T_ph[fl][:, 0:-1]
b = np.array([flow[1], flow[2]] + list(flow[3].values()))
ix = np.where(np.all(abs(a - b) <= err, axis=1))[0]
if ix.size == 1:
# known fluid properties
T = memorise.T_ph[fl][ix, -1][0]
memorise.T_ph_f[fl] += [T]
return T
else:
# unknown fluid properties
if num_fluids(flow[3]) > 1:
# calculate the fluid properties for fluid mixtures
if T0 < 70:
T0 = 300
val = newton(h_mix_pT,
dh_mix_pdT,
flow,
flow[2],
val0=T0,
valmin=70,
valmax=3000,
imax=10)
new = np.array([[flow[1], flow[2]] + list(flow[3].values()) +
[val]])
# memorise the newly calculated value
memorise.T_ph[fl] = np.append(memorise.T_ph[fl], new, axis=0)
return val
else:
# calculate fluid property for pure fluids
for fluid, x in flow[3].items():
if x > err:
val = T_ph(flow[1], flow[2], fluid)
new = np.array([[flow[1], flow[2]] +
list(flow[3].values()) + [val]])
# memorise the newly calculated value
memorise.T_ph[fl] = np.append(memorise.T_ph[fl],
new,
axis=0)
return val