def get_variables(self):
return {
'fuel': dc_simple(), 'fuel_alias': dc_simple(),
'air': dc_simple(), 'air_alias': dc_simple(),
'path': dc_simple(),
'lamb': dc_cp(
min_val=1, deriv=self.lambda_deriv, func=self.lambda_func,
latex=self.lambda_func_doc, num_eq=1),
'ti': dc_cp(
min_val=0, deriv=self.ti_deriv, func=self.ti_func,
latex=self.ti_func_doc, num_eq=1)
}
def get_variables(self):
return {
'P':
dc_cp(max_val=0,
num_eq=1,
deriv=self.energy_balance_deriv,
func=self.energy_balance_func,
latex=self.energy_balance_func_doc),
'eta_s':
dc_cp(min_val=0,
max_val=1,
num_eq=1,
deriv=self.eta_s_deriv,
func=self.eta_s_func,
latex=self.eta_s_func_doc),
'eta_s_char':
dc_cc(param='m',
num_eq=1,
deriv=self.eta_s_char_deriv,
func=self.eta_s_char_func,
latex=self.eta_s_char_func_doc),
'pr':
dc_cp(min_val=0,
max_val=1,
num_eq=1,
deriv=self.pr_deriv,
func=self.pr_func,
func_params={'pr': 'pr'},
latex=self.pr_func_doc),
'cone':
dc_simple(deriv=self.cone_deriv,
num_eq=1,
func=self.cone_func,
latex=self.cone_func_doc)
}
def get_variables(self):
return {
'Q': dc_cp(
deriv=self.energy_balance_deriv,
latex=self.energy_balance_func_doc, num_eq=1,
func=self.energy_balance_func),
'pr': dc_cp(
min_val=1e-4, max_val=1, num_eq=1,
deriv=self.pr_deriv, latex=self.pr_func_doc,
func=self.pr_func, func_params={'pr': 'pr'}),
'zeta': dc_cp(
min_val=0, max_val=1e15, num_eq=1,
deriv=self.zeta_deriv, func=self.zeta_func,
latex=self.zeta_func_doc,
func_params={'zeta': 'zeta'}),
'D': dc_cp(min_val=1e-2, max_val=2, d=1e-4),
'L': dc_cp(min_val=1e-1, d=1e-3),
'ks': dc_cp(val=1e-4, min_val=1e-7, max_val=1e-3, d=1e-8),
'kA': dc_cp(min_val=0, d=1),
'kA_char': dc_cc(param='m'), 'Tamb': dc_cp(),
'dissipative': dc_simple(val=True),
'hydro_group': dc_gcp(
elements=['L', 'ks', 'D'], num_eq=1,
latex=self.hydro_group_func_doc,
func=self.hydro_group_func, deriv=self.hydro_group_deriv),
'kA_group': dc_gcp(
elements=['kA', 'Tamb'], num_eq=1,
latex=self.kA_group_func_doc,
func=self.kA_group_func, deriv=self.kA_group_deriv),
'kA_char_group': dc_gcp(
elements=['kA_char', 'Tamb'], num_eq=1,
latex=self.kA_char_group_func_doc,
func=self.kA_char_group_func, deriv=self.kA_char_group_deriv)
}
def attr():
return {
'pr': dc_cp(min_val=1e-4, max_val=1),
'zeta': dc_cp(min_val=0),
'dp_char': dc_cc(param='m'),
'Sirr': dc_simple()
}
def get_variables(self):
return {
'Q': dc_cp(
deriv=self.energy_balance_deriv,
latex=self.energy_balance_func_doc, num_eq=1,
func=self.energy_balance_func),
'pr': dc_cp(
min_val=1e-4, max_val=1, num_eq=1,
deriv=self.pr_deriv, latex=self.pr_func_doc,
func=self.pr_func, func_params={'pr': 'pr'}),
'zeta': dc_cp(
min_val=0, max_val=1e15, num_eq=1,
deriv=self.zeta_deriv, func=self.zeta_func,
latex=self.zeta_func_doc,
func_params={'zeta': 'zeta'}),
'D': dc_cp(min_val=1e-2, max_val=2, d=1e-4),
'L': dc_cp(min_val=1e-1, d=1e-3),
'ks': dc_cp(val=1e-4, min_val=1e-7, max_val=1e-3, d=1e-8),
'E': dc_cp(min_val=0), 'A': dc_cp(min_val=0),
'eta_opt': dc_cp(min_val=0, max_val=1),
'lkf_lin': dc_cp(min_val=0), 'lkf_quad': dc_cp(min_val=0),
'Tamb': dc_cp(),
'Q_loss': dc_cp(max_val=0, val=0),
'dissipative': dc_simple(val=True),
'hydro_group': dc_gcp(
elements=['L', 'ks', 'D'], num_eq=1,
latex=self.hydro_group_func_doc,
func=self.hydro_group_func, deriv=self.hydro_group_deriv),
'energy_group': dc_gcp(
elements=['E', 'eta_opt', 'lkf_lin', 'lkf_quad', 'A', 'Tamb'],
num_eq=1, latex=self.energy_group_func_doc,
func=self.energy_group_func, deriv=self.energy_group_deriv)
}
def attr(self):
return {'P': dc_cp(min_val=0),
'Q': dc_cp(max_val=0),
'eta': dc_cp(min_val=0, max_val=1),
'e': dc_cp(),
'pr_c': dc_cp(max_val=1),
'zeta': dc_cp(min_val=0),
'eta_char': dc_cc(method='GENERIC'),
'S': dc_simple()}
def construct_connections(c, *args):
r"""
Create TESPy connection from data in the .csv-file and its parameters.
Parameters
----------
c : pandas.core.series.Series
Connection information from .csv-file.
args[0] : pandas.core.frame.DataFrame
DataFrame containing all created components.
Returns
-------
conn : tespy.connections.connection.Connection
TESPy connection object.
"""
# create connection
conn = Connection(
args[0].instance[c.source], c.source_id,
args[0].instance[c.target], c.target_id, label=str(c.label)
)
# read basic properties
for key in ['design', 'offdesign', 'design_path', 'local_design',
'local_offdesign']:
if key in c:
if isinstance(c[key], float):
setattr(conn, key, None)
else:
setattr(conn, key, c[key])
# read fluid properties
for key in ['m', 'p', 'h', 'T', 'x', 'v', 'Td_bp']:
if key in c:
setattr(conn, key, dc_prop(
val=c[key], val0=c[key + '0'], val_set=c[key + '_set'],
unit=c[key + '_unit'], ref=None, ref_set=c[key + '_ref_set']))
if 'state' in c:
conn.state = dc_simple(val=c[key], is_set=c[key + '_set'])
# read fluid vector
val = {}
val0 = {}
val_set = {}
for key in args[1].fluids:
if key in c:
val[key] = c[key]
val0[key] = c[key + '0']
val_set[key] = c[key + '_set']
conn.fluid = dc_flu(
val=val, val0=val0, val_set=val_set, balance=c['balance'])
# write properties to connection and return connection object
return conn
def attr():
return {'P': dc_cp(min_val=0),
'Q': dc_cp(max_val=0),
'eta': dc_cp(min_val=0, max_val=1),
'e': dc_cp(),
'pr_c': dc_cp(max_val=1),
'zeta': dc_cp(min_val=0),
'eta_char': dc_cc(),
'S': dc_simple()}
def attr():
r"""
Return available attributes of a connection.
Returns
-------
out : list
List of available attributes of a connection.
"""
return {'m': dc_prop(), 'p': dc_prop(), 'h': dc_prop(), 'T': dc_prop(),
'x': dc_prop(), 'v': dc_prop(),
'fluid': dc_flu(), 'Td_bp': dc_prop(), 'state': dc_simple()}
def __init__(self, label, **kwargs):
self.comps = pd.DataFrame(
columns=['param', 'P_ref', 'char', 'efficiency', 'base'])
self.label = label
self.P = dc_simple(val=np.nan, is_set=False)
self.char = CharLine(x=np.array([0, 3]), y=np.array([1, 1]))
self.printout = True
self.set_attr(**kwargs)
msg = 'Created bus ' + self.label + '.'
logging.debug(msg)
def get_variables(self):
return {
'Q': dc_cp(
max_val=0, num_eq=1, latex=self.energy_balance_cold_func_doc,
func=self.energy_balance_cold_func,
deriv=self.energy_balance_cold_deriv),
'pr1': dc_cp(
min_val=1e-4, max_val=1, num_eq=1, deriv=self.pr_deriv,
latex=self.pr_func_doc,
func=self.pr_func, func_params={'pr': 'pr1'}),
'pr2': dc_cp(
min_val=1e-4, max_val=1, num_eq=1, latex=self.pr_func_doc,
deriv=self.pr_deriv, func=self.pr_func,
func_params={'pr': 'pr2', 'inconn': 1, 'outconn': 1}),
'pr3': dc_cp(
min_val=1e-4, max_val=1, num_eq=1, latex=self.pr_func_doc,
deriv=self.pr_deriv, func=self.pr_func,
func_params={'pr': 'pr3', 'inconn': 2, 'outconn': 2}),
'zeta1': dc_cp(
min_val=0, max_val=1e15, num_eq=1, latex=self.zeta_func_doc,
deriv=self.zeta_deriv, func=self.zeta_func,
func_params={'zeta': 'zeta1'}),
'zeta2': dc_cp(
min_val=0, max_val=1e15, num_eq=1, latex=self.zeta_func_doc,
deriv=self.zeta_deriv, func=self.zeta_func,
func_params={'zeta': 'zeta2', 'inconn': 1, 'outconn': 1}),
'zeta3': dc_cp(
min_val=0, max_val=1e15, num_eq=1, latex=self.zeta_func_doc,
deriv=self.zeta_deriv, func=self.zeta_func,
func_params={'zeta': 'zeta3', 'inconn': 2, 'outconn': 2}),
'subcooling': dc_simple(
val=False, num_eq=1, latex=self.subcooling_func_doc,
deriv=self.subcooling_deriv, func=self.subcooling_func),
'overheating': dc_simple(
val=False, num_eq=1, latex=self.overheating_func_doc,
deriv=self.overheating_deriv, func=self.overheating_func)
}
def attr():
return {'Q': dc_cp(max_val=0),
'pr1': dc_cp(max_val=1), 'pr2': dc_cp(max_val=1),
'pr3': dc_cp(max_val=1),
'zeta1': dc_cp(min_val=0), 'zeta2': dc_cp(min_val=0),
'zeta3': dc_cp(min_val=0),
'subcooling': dc_simple(val=False),
'overheating': dc_simple(val=False),
'SQ1': dc_simple(), 'SQ2': dc_simple(), 'SQ3': dc_simple(),
'Sirr': dc_simple()}
def get_variables(self):
return {
'Q': dc_cp(
max_val=0, func=self.energy_balance_hot_func, num_eq=1,
deriv=self.energy_balance_hot_deriv,
latex=self.energy_balance_hot_func_doc),
'kA': dc_cp(
min_val=0, num_eq=1, func=self.kA_func, latex=self.kA_func_doc,
deriv=self.kA_deriv),
'td_log': dc_cp(min_val=0),
'ttd_u': dc_cp(
min_val=0, num_eq=1, func=self.ttd_u_func,
deriv=self.ttd_u_deriv, latex=self.ttd_u_func_doc),
'ttd_l': dc_cp(
min_val=0, num_eq=1, func=self.ttd_l_func,
deriv=self.ttd_l_deriv, latex=self.ttd_l_func_doc),
'pr1': dc_cp(
min_val=1e-4, max_val=1, num_eq=1, deriv=self.pr_deriv,
latex=self.pr_func_doc,
func=self.pr_func, func_params={'pr': 'pr1'}),
'pr2': dc_cp(
min_val=1e-4, max_val=1, num_eq=1, latex=self.pr_func_doc,
deriv=self.pr_deriv, func=self.pr_func,
func_params={'pr': 'pr2', 'inconn': 1, 'outconn': 1}),
'zeta1': dc_cp(
min_val=0, max_val=1e15, num_eq=1, latex=self.zeta_func_doc,
deriv=self.zeta_deriv, func=self.zeta_func,
func_params={'zeta': 'zeta1'}),
'zeta2': dc_cp(
min_val=0, max_val=1e15, num_eq=1, latex=self.zeta_func_doc,
deriv=self.zeta_deriv, func=self.zeta_func,
func_params={'zeta': 'zeta2', 'inconn': 1, 'outconn': 1}),
'kA_char': dc_gcc(
elements=['kA_char1', 'kA_char2'],
num_eq=1, latex=self.kA_char_func_doc, func=self.kA_char_func,
deriv=self.kA_char_deriv),
'kA_char1': dc_cc(param='m'),
'kA_char2': dc_cc(
param='m', char_params={
'type': 'rel', 'inconn': 1, 'outconn': 1}),
'subcooling': dc_simple(
val=False, num_eq=1, latex=self.subcooling_func_doc,
deriv=self.subcooling_deriv, func=self.subcooling_func)
}
def construct_comps(c, *args):
r"""
Create TESPy component from class name and set parameters.
Parameters
----------
c : pandas.core.series.Series
Component information from .csv-file.
args[0] : pandas.core.frame.DataFrame
DataFrame containing the data of characteristic lines.
args[1] : pandas.core.frame.DataFrame
DataFrame containing the data of characteristic maps.
Returns
-------
instance : tespy.components.components.component
TESPy component object.
"""
target_class = comp_target_classes[c.cp]
instance = target_class(c.label)
kwargs = {}
# basic properties
for key in [
'design', 'offdesign', 'design_path', 'local_design',
'local_offdesign'
]:
if key in c:
kwargs[key] = c[key]
for key, value in instance.variables.items():
if key in c:
# component parameters
if isinstance(value, dc_cp):
kwargs[key] = dc_cp(val=c[key],
is_set=c[key + '_set'],
is_var=c[key + '_var'])
# component parameters
elif isinstance(value, dc_simple):
kwargs[key] = dc_simple(val=c[key], is_set=c[key + '_set'])
# component characteristics
elif isinstance(value, dc_cc):
# finding x and y values of the characteristic function
values = args[0]['id'] == c[key]
try:
x = args[0][values].x.values[0]
y = args[0][values].y.values[0]
extrapolate = False
if 'extrapolate' in args[0].columns:
extrapolate = args[0][values].extrapolate.values[0]
char = char_line(x=x, y=y, extrapolate=extrapolate)
except IndexError:
char = None
msg = ('Could not find x and y values for characteristic '
'line, using defaults instead for function ' + key +
' at component ' + c.label + '.')
logging.warning(msg)
kwargs[key] = dc_cc(is_set=c[key + '_set'],
param=c[key + '_param'],
func=char)
# component characteristics
elif isinstance(value, dc_cm):
# finding x and y values of the characteristic function
values = args[1]['id'] == c[key]
try:
x = list(args[1][values].x.values[0])
y = list(args[1][values].y.values[0])
z1 = list(args[1][values].z1.values[0])
z2 = list(args[1][values].z2.values[0])
target_class = map_target_classes[args[1]
[values].type.values[0]]
char = target_class(x=x, y=y, z1=z1, z2=z2)
except IndexError:
char = None
msg = ('Could not find x, y, z1 and z2 values for '
'characteristic map of component ' + c.label + '!')
logging.warning(msg)
kwargs[key] = dc_cm(is_set=c[key + '_set'],
param=c[key + '_param'],
func=char)
# grouped component parameters
elif isinstance(value, dc_gcp):
kwargs[key] = dc_gcp(method=c[key])
instance.set_attr(**kwargs)
return instance
def get_variables():
return {'num_in': dc_simple()}
def attr(self):
return {'num_out': dc_simple()}
def construct_conns(c, *args):
r"""
Create TESPy connection from data in the .csv-file and its parameters.
Parameters
----------
c : pandas.core.series.Series
Connection information from .csv-file.
args[0] : pandas.core.frame.DataFrame
DataFrame containing all created components.
Returns
-------
conn : tespy.connections.connection
TESPy connection object.
"""
# create connection
conn = connection(args[0].instance[c.s], c.s_id, args[0].instance[c.t],
c.t_id)
kwargs = {}
# read basic properties
for key in [
'design', 'offdesign', 'design_path', 'local_design',
'local_offdesign'
]:
if key in c:
kwargs[key] = c[key]
# read fluid properties
for key in ['m', 'p', 'h', 'T', 'x', 'v', 'Td_bp']:
if key in c:
if key in c:
kwargs[key] = dc_prop(val=c[key],
val0=c[key + '0'],
val_set=c[key + '_set'],
unit=c[key + '_unit'],
unit_set=c[key + '_unit_set'],
ref=None,
ref_set=c[key + '_ref_set'])
key = 'state'
if key in c:
kwargs[key] = dc_simple(val=c[key], is_set=c[key + '_set'])
# read fluid vector
val = {}
val0 = {}
val_set = {}
for key in args[1].fluids:
if key in c:
val[key] = c[key]
val0[key] = c[key + '0']
val_set[key] = c[key + '_set']
kwargs['fluid'] = dc_flu(val=val,
val0=val0,
val_set=val_set,
balance=c['balance'])
# write properties to connection and return connection object
conn.set_attr(**kwargs)
return conn
def attr():
return {'num_in': dc_simple(), 'num_out': dc_simple()}
def attr():
return {'num_inter': dc_simple()}
def attr():
return {'num_in': dc_simple(), 'zero_flag': dc_simple()}
