Merge branch 'dev' into main

docs/analyses/exergy.rst

@@ -37,6 +37,7 @@ processes yet, chemical exergy is not considered. Changes in kinetic and
 potential exergy are neglected and therefore not considered as well.
 
 .. list-table:: Terminology
+
     :widths: 20 20 10 50
     :header-rows: 1
     :class: tight-table
@@ -103,33 +104,35 @@ potential exergy are neglected and therefore not considered as well.
 
 Tutorial
 --------
-In this short tutorial, an exergy analysis is carried out for the Solar Energy
-Generating System (SEGS). The full python script is available on GitHub in
-an individual repository: https://github.com/fwitte/SEGS_exergy.
+In this short tutorial, an exergy analysis is carried out for the so called
+"Solar Energy Generating System" (SEGS). The full python script is available on
+GitHub in an individual repository: https://github.com/fwitte/SEGS_exergy.
 
 Two other full code examples are to be found at:
 
 - Supercritical CO\ :sub:`2` power cycle: https://github.com/fwitte/sCO2_exergy
 - Refrigeration machine: https://github.com/fwitte/refrigeration_cycle_exergy
 
-The SEGS consists of three main systems, the solar field, the steam cycle and
-the cooling water system. In the solar field Therminol VP1 is used as heat
-transfer liquid. In the steam generator and reheater the TVP1 is cooled down to
-evaporate and overheat/reheat the water of the steam cycle. The turbine is
-divided in a high pressure turbine and a low pressure turbine, which are
-further subdivided in 2 stages (high pressure turbine) and 5 stages. In between
-the stages steam is exctracted for preheating. Finally, the main condenser of
-the steam cycle is connected to an air cooling tower. The figure below shows
-the topology of the model.
+SEGS consists of three main systems, the solar field, the steam cycle and the
+cooling water system. In the solar field Therminol VP1 is used as heat transfer
+fluid. In the steam generator and reheater the TVP1 is cooled down to evaporate
+and overheat/reheat the water of the steam cycle. The turbine is divided in a
+high pressure turbine and a low pressure turbine, which are further subdivided
+in 2 parts (high pressure turbine) and 5 parts. In between the stages steam is
+exctracted for preheating. Finally, the main condenser of the steam cycle is
+connected to an air cooling tower. The figure below shows the topology of the
+model.
 
 .. figure:: api/_images/SEGS_flowsheet.svg
+
     :align: center
     :alt: Topology of the Solar Energy Generating System (SEGS)
 
 The input data are based on literature :cite:`Kearney1988`, which provides
 measured data. Some parameters are however taken from a follow-up publication,
 as the original data show some inconsistencies, e.g. higher enthalpy at the low
-pressure turbine's last stage outlet than at its inlet :cite:`Lippke1995`.
+pressure turbine's last stage outlet than at its inlet :cite:`Lippke1995`. As
+mentioned, you can find all data in the respective GitHub repository.
 
 TESPy model
 ^^^^^^^^^^^
@@ -142,9 +145,11 @@ is implemented, calculating the surface area required for the provision of the
 heat input at optimal conditions.
 
 All components are flagged with the :code:`fkt_group` parameter, which will
-automatically create component groups for the exergy analysis sankey diagram.
-The specification of this parameter is not required for the exergy analysis
-itself, but helps to simplify the automatically generated sankey diagram.
+automatically create functional groups (component groups) for the exergy
+analysis Grassmann diagram. The specification of this parameter is not required
+for the exergy analysis itself, but helps to simplify the automatically
+generated diagram. Components not assigned to any functional group will form
+their respective group.
 
 Regarding parameter specification, the following parameters are specified:
 
@@ -297,13 +302,14 @@ rounding errors).
 
 Printing the results is possible with the
 :py:meth:`tespy.tools.analyses.ExergyAnalysis.print_results` method. The
-results are printed in five individual tables:
+results are printed in six individual tables:
 
 - connections
 - components
 - busses
-- groups (component groups)
+- aggregation (aggregation of components and the respective busses)
 - network
+- groups (functional groups)
 
 By default, all of these tables are printed to the prompt. It is possible to
 deselect the tables, e.g. by passing :code:`groups=False` to the method call.
@@ -312,9 +318,28 @@ deselect the tables, e.g. by passing :code:`groups=False` to the method call.
 
     ean.print_results(groups=False, connections=False)
 
-For the component related tables, i.e. busses, components and groups, the data
-are sorted descending regarding the exergy destruction value of the individual
-component.
+For the component related tables, i.e. busses, components, aggregation and
+groups, the data are sorted descending regarding the exergy destruction value
+of the individual entry. The component data contain fuel exergy, product exergy
+and exergy destruction values related to the component itself ignoring losses
+that might occur on the busses, for example, mechanical or electrical
+conversion losses in motors and generators. The bus data contain the respective
+information related to the conversion losses on the busses only. The
+aggregation data contain both, the component and the bus data. For instance,
+a turbine driving a generator will have the electrical energy delivered by the
+generator as product exergy value. The same component's exergy product without
+considering the mechanical or electrical conversion losses is the shaft power
+delivered by the turbine. From the generator's perspective, this is the fuel
+exergy, while the product is the electrical energy.
+
+.. note::
+
+  Please note, that in contrast to the component and bus data, group data do
+  not contain fuel and product exergy as well as exergy efficiency. Instead all
+  exergy streams entering the system borders of the component group and all
+  exergy streams leaving the system borders are calculated. On this basis, a
+  graphical representation of the exergy flows in the network can be generated
+  in the form of a Grassmann diagram.
 
 Accessing the data
 ^^^^^^^^^^^^^^^^^^
@@ -328,6 +353,7 @@ the following code snippet.
     connection_data = ean.connection_data
     bus_data = ean.bus_data
     component_data = ean.component_data
+    aggregation_data = ean.aggregation_data
     network_data = ean.network_data
     group_data = ean.group_data
 
@@ -356,6 +382,7 @@ diagram is then easily done:
     fig.show()
 
 .. figure:: api/_images/SEGS_sankey.png
+
     :align: center
     :alt: Sankey diagram of the Soler Energy Generating System (SEGS)
 
@@ -383,11 +410,12 @@ colors can be assigned to these types of streams.
 
 .. note::
 
-    - The :code:`node_order` must contain all exergy streams, thus including
+    - The :code:`node_order` must contain all exergy streams, thus
 
-      - ALL component group labels
-      - lables of the busses used in the definitions of the analysis
-      - :code:`'E_F'`, :code:`'E_P'`, :code:`'E_D'`, :code:`'E_L'`
+      - ALL component group labels (you can find the labels in the group data
+        results printout),
+      - lables of the busses used in the definitions of the analysis and
+      - :code:`'E_F'`, :code:`'E_P'`, :code:`'E_D'` as well as :code:`'E_L'`
 
     - The colors dictionary works with the following keys:
 

docs/whats_new.rst

@@ -8,6 +8,7 @@ Discover noteable new features and improvements in each release
     :local:
     :backlinks: top
 
+.. include::  whats_new/v0-5-1.rst
 .. include::  whats_new/v0-5-0.rst
 .. include::  whats_new/v0-4-4.rst
 .. include::  whats_new/v0-4-3-003.rst

docs/whats_new/v0-5-1.rst

@@ -0,0 +1,25 @@
+v0.5.1 - Exciting Exergy (January, 14, 2022)
+++++++++++++++++++++++++++++++++++++++++++++
+
+Documentation
+#############
+- Improvements on the description of the exergy analysis results data.
+  Additional dataframe added, that contains the aggregated component exergy
+  analysis results (component and respective bus exergy data)
+  (`PR #293 <https://github.com/oemof/tespy/pull/293>`_).
+
+Bug Fixes
+#########
+- In the first offdesign calculation the connection parameter specifications
+  in the LaTeX report were still reported in design mode
+  (`PR #290 <https://github.com/oemof/tespy/pull/290>`_).
+- Labels of string representations of numeric labels in components, connections
+  and busses have been misinterpreted as numeric values by the network_reader
+  (`PR #298 <https://github.com/oemof/tespy/pull/298>`_).
+
+Other Changes
+#############
+
+Contributors
+############
+- Francesco Witte (`@fwitte <https://github.com/fwitte>`_)

setup.py

@@ -25,7 +25,7 @@ def read(*names, **kwargs):
 
 setup(
     name='TESPy',
-    version='0.5.0',
+    version='0.5.1',
     license='MIT',
     description='Thermal Engineering Systems in Python (TESPy)',
     long_description='%s' % (
@@ -34,7 +34,7 @@ def read(*names, **kwargs):
         )
     ),
     author='Francesco Witte',
-    author_email='francesco.witte@hs-flensburg.de',
+    author_email='francesco.witte@dlr.de',
     url='https://github.com/oemof/tespy',
     packages=find_packages('src'),
     package_dir={'': 'src'},

src/tespy/__init__.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8
 
-__version__ = '0.5.0 - Davis\' Domain'
+__version__ = '0.5.1 - Exciting Exergy'
 
 # tespy data and connections import
 from . import connections  # noqa: F401

src/tespy/networks/network.py

@@ -245,11 +245,13 @@ def set_fluid_back_ends(self, memorise_fluid_properties):
 
         # set up results dataframe for connections
         cols = (
-            ['m', 'p', 'h', 'T', 'v', 'vol', 's', 'x', 'Td_bp'] + self.fluids)
+            ['m', 'p', 'h', 'T', 'v', 'vol', 's', 'x', 'Td_bp']
+            + self.fluids)
         self.results['Connection'] = pd.DataFrame(
             columns=cols, dtype='float64')
+        # include column for fluid balance in specs dataframe
         self.specifications['Connection'] = pd.DataFrame(
-            columns=cols, dtype='bool')
+            columns=cols + ['balance'], dtype='bool')
         self.specifications['Ref'] = pd.DataFrame(
             columns=cols, dtype='bool')
         self.specifications['lookup'] = {
@@ -816,6 +818,7 @@ def init_set_properties(self):
         """Specification of SI values for user set values."""
         # fluid property values
         for c in self.conns['object']:
+            # set all specifications to False
             self.specifications['Connection'].loc[c.label] = False
             if not self.init_previous:
                 c.good_starting_values = False
@@ -830,11 +833,9 @@ def init_set_properties(self):
                     c.get_attr(key).unit = self.get_attr(key + '_unit')
                 # set SI value
                 if c.get_attr(key).val_set:
-                    self.specifications['Connection'].loc[c.label, key] = True
                     c.get_attr(key).val_SI = hlp.convert_to_SI(
                         key, c.get_attr(key).val, c.get_attr(key).unit)
                 if c.get_attr(key).ref_set:
-                    self.specifications['Ref'].loc[c.label, key] = True
                     if key == 'T':
                         c.get_attr(key).ref.delta_SI = hlp.convert_to_SI(
                             'Td_bp', c.get_attr(key).ref.delta,
@@ -854,16 +855,6 @@ def init_set_properties(self):
                     raise hlp.TESPyNetworkError(msg)
             tmp0 = c.fluid.val0
             tmp_set = c.fluid.val_set
-            self.specifications['Connection'].loc[
-                c.label, 'balance'] = c.fluid.balance
-
-            # enter fluid specifications into specification DataFrame
-            for fluid in self.fluids:
-                try:
-                    self.specifications['Connection'].loc[c.label, fluid] = (
-                        c.fluid.val_set[fluid])
-                except KeyError:
-                    pass
 
             c.fluid.val = OrderedDict()
             c.fluid.val0 = OrderedDict()
@@ -1409,13 +1400,28 @@ def init_count_connections_parameters(self, c):
         c : tespy.connections.connection.Connection
             Connection count parameters of.
         """
-        self.num_conn_eq += [
-            c.m.val_set, c.p.val_set, c.h.val_set, c.T.val_set,
-            c.x.val_set, c.v.val_set, c.Td_bp.val_set].count(True)
-        self.num_conn_eq += [
-            c.m.ref_set, c.p.ref_set, c.h.ref_set, c.T.ref_set].count(True)
-        self.num_conn_eq += list(c.fluid.val_set.values()).count(True)
+        # variables 0 to 9: fluid properties
+        vars = self.specifications['Connection'].columns[:9]
+        row = [c.get_attr(var).val_set for var in vars]
+        self.num_conn_eq += row.count(True)
+        # write information to specifaction dataframe
+        self.specifications['Connection'].loc[c.label, vars] = row
+
+        row = [c.get_attr(var).ref_set for var in vars]
+        self.num_conn_eq += row.count(True)
+        # write refrenced value information to specifaction dataframe
+        self.specifications['Ref'].loc[c.label, vars] = row
+
+        # variables 9 to last but one: fluid mass fractions
+        fluids = self.specifications['Connection'].columns[9:-1]
+        row = [c.fluid.val_set[fluid] for fluid in fluids]
+        self.num_conn_eq += row.count(True)
+        self.specifications['Connection'].loc[c.label, fluids] = row
+
+        # last one: fluid balance specification
         self.num_conn_eq += c.fluid.balance * 1
+        self.specifications['Connection'].loc[
+            c.label, 'balance'] = c.fluid.balance
 
     def init_precalc_properties(self, c):
         """

src/tespy/networks/network_reader.py

@@ -307,7 +307,7 @@ def load_network(path):
 
             # create components
             df['instance'] = df.apply(
-                construct_comps, axis=1,  args=(char_lines, char_maps))
+                construct_components, axis=1,  args=(char_lines, char_maps))
 
             cols = [
                 'instance', 'label', 'busses', 'bus_param', 'bus_P_ref',
@@ -335,7 +335,8 @@ def load_network(path):
     logging.debug(msg)
 
     # create connections
-    conns['instance'] = conns.apply(construct_conns, axis=1, args=(comps, nw,))
+    conns['instance'] = conns.apply(
+        construct_connections, axis=1, args=(comps, nw,))
     conns.apply(conns_set_ref, axis=1, args=(conns,))
     conns = conns.set_index('id')
 
@@ -383,7 +384,7 @@ def load_network(path):
 # %% create components
 
 
-def construct_comps(c, *args):
+def construct_components(c, *args):
     r"""
     Create TESPy component from class name and set parameters.
 
@@ -404,7 +405,7 @@ def construct_comps(c, *args):
         TESPy component object.
     """
     target_class = comp_target_classes[c['comp_type']]
-    instance = target_class(c['label'])
+    instance = target_class(str(c['label']))
     kwargs = {}
 
     # basic properties
@@ -521,7 +522,7 @@ def construct_network(path):
 # %% create connections
 
 
-def construct_conns(c, *args):
+def construct_connections(c, *args):
     r"""
     Create TESPy connection from data in the .csv-file and its parameters.
 
@@ -539,12 +540,14 @@ def construct_conns(c, *args):
         TESPy connection object.
     """
     # create connection
-    conn = Connection(args[0].instance[c.source], c.source_id,
-                      args[0].instance[c.target], c.target_id)
+    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', 'label']:
+                'local_offdesign']:
         if key in c:
             if isinstance(c[key], float):
                 setattr(conn, key, None)
@@ -625,7 +628,7 @@ def construct_busses(c, *args):
         TESPy bus object.
     """
     # set up bus with label and specify value for power
-    b = Bus(c.label, P=c.P)
+    b = Bus(str(c.label), P=c.P)
     b.P.is_set = c.P_set
     return b