def create_pit_node_entries(cls, net, node_pit, node_name):
"""
Function which creates pit node entries.
:param net: The pandapipes network
:type net: pandapipesNet
:param node_pit:
:type node_pit:
:return: No Output.
"""
circ_pump, press = super().create_pit_node_entries(
net, node_pit, node_name)
junction_idx_lookups = get_lookup(net, "node", "index")[node_name]
juncts_p, press_sum, number = _sum_by_group(
circ_pump.to_junction.values, press - circ_pump.plift_bar.values,
np.ones_like(press, dtype=np.int32))
index_p = junction_idx_lookups[juncts_p]
node_pit[index_p, PINIT] = press_sum / number
node_pit[index_p, NODE_TYPE] = P
node_pit[index_p, EXT_GRID_OCCURENCE] += number
net["_lookups"]["ext_grid"] = \
np.array(list(set(np.concatenate([net["_lookups"]["ext_grid"], index_p]))))
def create_pit_node_entries(cls, net, node_pit, node_name):
"""
Function which creates pit node entries.
:param net: The pandapipes network
:type net: pandapipesNet
:param node_pit:
:type node_pit:
:param node_name:
:type node_name:
:return: No Output.
"""
ext_grids = net[cls.table_name()]
p_mask = np.where(np.isin(ext_grids.type.values, ["p", "pt"]))
press = ext_grids.p_bar.values[p_mask] * ext_grids.in_service.values[
p_mask]
junction_idx_lookups = get_lookup(net, "node", "index")[node_name]
junction = cls.get_connected_junction(net)
juncts_p, press_sum, number = _sum_by_group(
junction.values[p_mask], press, np.ones_like(press,
dtype=np.int32))
index_p = junction_idx_lookups[juncts_p]
node_pit[index_p, PINIT] = press_sum / number
node_pit[index_p, NODE_TYPE] = P
node_pit[index_p, EXT_GRID_OCCURENCE] += number
t_mask = np.where(np.isin(ext_grids.type.values, ["t", "pt"]))
t_k = ext_grids.t_k.values[t_mask] * ext_grids.in_service.values[t_mask]
juncts_t, t_sum, number = _sum_by_group(
junction.values[t_mask], t_k, np.ones_like(t_k, dtype=np.int32))
index = junction_idx_lookups[juncts_t]
node_pit[index, TINIT] = t_sum / number
node_pit[index, NODE_TYPE_T] = T
node_pit[index, EXT_GRID_OCCURENCE_T] += number
net["_lookups"]["ext_grid"] = \
np.array(list(set(np.concatenate([net["_lookups"]["ext_grid"], index_p])))) if \
"ext_grid" in net['_lookups'] else index_p
return ext_grids, press
def prepare_result_tables(cls, net, options, node_name):
res_table = super().extract_results(net, options, node_name)
f, t = get_lookup(net, "branch", "from_to")[cls.table_name()]
fa, ta = get_lookup(net, "branch", "from_to_active")[cls.table_name()]
placement_table = np.argsort(net[cls.table_name()].index.values)
idx_pit = net["_pit"]["branch"][f:t, ELEMENT_IDX]
pipe_considered = get_lookup(net, "branch", "active")[f:t]
_, active_pipes = _sum_by_group(idx_pit, pipe_considered.astype(np.int32))
active_pipes = active_pipes > 0.99
placement_table = placement_table[active_pipes]
branch_pit = net["_active_pit"]["branch"][fa:ta, :]
return placement_table, branch_pit, res_table
def extract_results(cls, net, options, node_name):
"""
Function that extracts certain results.
:param net: The pandapipes network
:type net: pandapipesNet
:param options:
:type options:
:param node_name:
:type node_name:
:return: No Output.
"""
ext_grids = net[cls.table_name()]
if len(ext_grids) == 0:
return
res_table = super().extract_results(net, options, node_name)
branch_pit = net['_pit']['branch']
node_pit = net["_pit"]["node"]
p_grids = np.isin(ext_grids.type.values, ["p", "pt"])
junction = cls.get_connected_junction(net)
eg_nodes = get_lookup(net, "node", "index")[node_name][np.array(
junction.values[p_grids])]
node_uni, inverse_nodes, counts = np.unique(eg_nodes,
return_counts=True,
return_inverse=True)
eg_from_branches = np.isin(branch_pit[:, FROM_NODE], node_uni)
eg_to_branches = np.isin(branch_pit[:, TO_NODE], node_uni)
from_nodes = branch_pit[eg_from_branches, FROM_NODE]
to_nodes = branch_pit[eg_to_branches, TO_NODE]
mass_flow_from = branch_pit[eg_from_branches, LOAD_VEC_NODES]
mass_flow_to = branch_pit[eg_to_branches, LOAD_VEC_NODES]
loads = node_pit[node_uni, LOAD]
all_index_nodes = np.concatenate([from_nodes, to_nodes, node_uni])
all_mass_flows = np.concatenate(
[-mass_flow_from, mass_flow_to, -loads])
nodes, sum_mass_flows = _sum_by_group(all_index_nodes, all_mass_flows)
# positive results mean that the ext_grid feeds in, negative means that the ext grid
# extracts (like a load)
res_table["mdot_kg_per_s"].values[p_grids] = \
cls.sign() * (sum_mass_flows / counts)[inverse_nodes]
return res_table, ext_grids, node_uni, node_pit, branch_pit
def create_pit_node_entries(cls, net, node_pit, node_name):
"""
Function which creates pit node entries.
:param net: The pandapipes network
:type net: pandapipesNet
:param node_pit:
:type node_pit:
:return: No Output.
"""
loads = net[cls.table_name()]
helper = loads.in_service.values * loads.scaling.values * cls.sign()
mf = np.nan_to_num(loads.mdot_kg_per_s.values)
mass_flow_loads = mf * helper
juncts, loads_sum = _sum_by_group(loads.junction.values, mass_flow_loads)
junction_idx_lookups = get_lookup(net, "node", "index")[node_name]
index = junction_idx_lookups[juncts]
node_pit[index, LOAD] += loads_sum
def create_pit_node_entries(cls, net, node_pit, node_name):
"""
Function which creates pit node entries.
:param net: The pandapipes network
:type net: pandapipesNet
:param node_pit:
:type node_pit:
:param node_name:
:type node_name:
:return: No Output.
"""
circ_pump, _ = super().create_pit_node_entries(net, node_pit, node_name)
mf = np.nan_to_num(circ_pump.mdot_kg_per_s.values)
mass_flow_loads = mf * circ_pump.in_service.values
juncts, loads_sum = _sum_by_group(circ_pump.to_junction.values, mass_flow_loads)
junction_idx_lookups = get_lookup(net, "node", "index")[node_name]
index = junction_idx_lookups[juncts]
node_pit[index, LOAD] += loads_sum
def extract_results(cls, net, options, node_name):
placement_table, branch_pit, res_table = cls.prepare_result_tables(
net, options, node_name)
node_pit = net["_active_pit"]["node"]
node_active_idx_lookup = get_lookup(net, "node",
"index_active")[node_name]
junction_idx_lookup = get_lookup(net, "node", "index")[node_name]
from_junction_nodes = node_active_idx_lookup[junction_idx_lookup[net[
cls.table_name()]["from_junction"].values[placement_table]]]
to_junction_nodes = node_active_idx_lookup[junction_idx_lookup[net[
cls.table_name()]["to_junction"].values[placement_table]]]
from_nodes = branch_pit[:, FROM_NODE].astype(np.int32)
to_nodes = branch_pit[:, TO_NODE].astype(np.int32)
p_scale = get_net_option(net, "p_scale")
fluid = get_fluid(net)
v_mps = branch_pit[:, VINIT]
t0 = node_pit[from_nodes, TINIT_NODE]
t1 = node_pit[to_nodes, TINIT_NODE]
mf = branch_pit[:, LOAD_VEC_NODES]
vf = branch_pit[:, LOAD_VEC_NODES] / get_fluid(net).get_density(
(t0 + t1) / 2)
idx_active = branch_pit[:, ELEMENT_IDX]
_, v_sum, mf_sum, vf_sum, internal_pipes = \
_sum_by_group(idx_active, v_mps, mf, vf, np.ones_like(idx_active))
if fluid.is_gas:
# derived from the ideal gas law
p_from = node_pit[from_nodes,
PAMB] + node_pit[from_nodes, PINIT] * p_scale
p_to = node_pit[to_nodes,
PAMB] + node_pit[to_nodes, PINIT] * p_scale
numerator = NORMAL_PRESSURE * branch_pit[:, TINIT]
normfactor_from = numerator * fluid.get_property("compressibility", p_from) \
/ (p_from * NORMAL_TEMPERATURE)
normfactor_to = numerator * fluid.get_property("compressibility", p_to) \
/ (p_to * NORMAL_TEMPERATURE)
v_gas_from = v_mps * normfactor_from
v_gas_to = v_mps * normfactor_to
mask = ~np.isclose(p_from, p_to)
p_mean = np.empty_like(p_to)
p_mean[~mask] = p_from[~mask]
p_mean[mask] = 2 / 3 * (p_from[mask] ** 3 - p_to[mask] ** 3) \
/ (p_from[mask] ** 2 - p_to[mask] ** 2)
normfactor_mean = numerator * fluid.get_property("compressibility", p_mean) \
/ (p_mean * NORMAL_TEMPERATURE)
v_gas_mean = v_mps * normfactor_mean
_, _, _, v_gas_mean_sum, nf_from_sum, nf_to_sum, \
internal_pipes = _sum_by_group(idx_active, v_gas_from, v_gas_to, v_gas_mean,
normfactor_from, normfactor_to,
np.ones_like(idx_active))
v_gas_from_ordered = select_from_pit(from_nodes,
from_junction_nodes,
v_gas_from)
v_gas_to_ordered = select_from_pit(to_nodes, to_junction_nodes,
v_gas_to)
res_table["v_from_m_per_s"].values[
placement_table] = v_gas_from_ordered
res_table["v_to_m_per_s"].values[
placement_table] = v_gas_to_ordered
res_table["v_mean_m_per_s"].values[
placement_table] = v_gas_mean_sum / internal_pipes
res_table["normfactor_from"].values[
placement_table] = nf_from_sum / internal_pipes
res_table["normfactor_to"].values[
placement_table] = nf_to_sum / internal_pipes
else:
res_table["v_mean_m_per_s"].values[
placement_table] = v_sum / internal_pipes
res_table["p_from_bar"].values[placement_table] = node_pit[
from_junction_nodes, PINIT]
res_table["p_to_bar"].values[placement_table] = node_pit[
to_junction_nodes, PINIT]
res_table["t_from_k"].values[placement_table] = node_pit[
from_junction_nodes, TINIT_NODE]
res_table["t_to_k"].values[placement_table] = node_pit[
to_junction_nodes, TINIT_NODE]
res_table["mdot_to_kg_per_s"].values[
placement_table] = -mf_sum / internal_pipes
res_table["mdot_from_kg_per_s"].values[
placement_table] = mf_sum / internal_pipes
res_table["vdot_norm_m3_per_s"].values[
placement_table] = vf_sum / internal_pipes
idx_pit = branch_pit[:, ELEMENT_IDX]
_, lambda_sum, reynolds_sum, = \
_sum_by_group(idx_pit, branch_pit[:, LAMBDA], branch_pit[:, RE])
res_table["lambda"].values[
placement_table] = lambda_sum / internal_pipes
res_table["reynolds"].values[
placement_table] = reynolds_sum / internal_pipes
def extract_results(cls, net, options, node_name):
"""
Function that extracts certain results.
:param net: The pandapipes network
:type net: pandapipesNet
:param options:
:type options:
:return: No Output.
"""
placement_table, heat_exchanger_pit, res_table = \
super().extract_results(net, options, node_name)
node_pit = net["_active_pit"]["node"]
node_active_idx_lookup = get_lookup(net, "node", "index_active")[node_name]
junction_idx_lookup = get_lookup(net, "node", "index")[node_name]
from_junction_nodes = node_active_idx_lookup[junction_idx_lookup[
net[cls.table_name()]["from_junction"].values[placement_table]]]
to_junction_nodes = node_active_idx_lookup[junction_idx_lookup[
net[cls.table_name()]["to_junction"].values[placement_table]]]
p_scale = get_net_option(net, "p_scale")
from_nodes = heat_exchanger_pit[:, FROM_NODE].astype(np.int32)
to_nodes = heat_exchanger_pit[:, TO_NODE].astype(np.int32)
fluid = get_fluid(net)
v_mps = heat_exchanger_pit[:, VINIT]
t0 = node_pit[from_nodes, TINIT_NODE]
t1 = node_pit[to_nodes, TINIT_NODE]
mf = heat_exchanger_pit[:, LOAD_VEC_NODES]
vf = heat_exchanger_pit[:, LOAD_VEC_NODES] / get_fluid(net).get_density((t0 + t1) / 2)
idx_active = heat_exchanger_pit[:, ELEMENT_IDX]
idx_sort, v_sum, mf_sum, vf_sum = \
_sum_by_group(idx_active, v_mps, mf, vf)
if fluid.is_gas:
# derived from the ideal gas law
p_from = node_pit[from_nodes, PAMB] + node_pit[from_nodes, PINIT] * p_scale
p_to = node_pit[to_nodes, PAMB] + node_pit[to_nodes, PINIT] * p_scale
numerator = NORMAL_PRESSURE * heat_exchanger_pit[:, TINIT]
normfactor_from = numerator * fluid.get_property("compressibility", p_from) \
/ (p_from * NORMAL_TEMPERATURE)
normfactor_to = numerator * fluid.get_property("compressibility", p_to) \
/ (p_to * NORMAL_TEMPERATURE)
v_gas_from = v_mps * normfactor_from
v_gas_to = v_mps * normfactor_to
mask = p_from != p_to
p_mean = np.empty_like(p_to)
p_mean[~mask] = p_from[~mask]
p_mean[mask] = 2 / 3 * (p_from[mask] ** 3 - p_to[mask] ** 3) \
/ (p_from[mask] ** 2 - p_to[mask] ** 2)
normfactor_mean = numerator * fluid.get_property("compressibility", p_mean) \
/ (p_mean * NORMAL_TEMPERATURE)
v_gas_mean = v_mps * normfactor_mean
idx_sort, v_gas_from_sum, v_gas_to_sum, v_gas_mean_sum, nf_from_sum, nf_to_sum, \
internal_pipes = _sum_by_group(
idx_active, v_gas_from, v_gas_to, v_gas_mean, normfactor_from, normfactor_to,
np.ones_like(idx_active))
res_table["v_from_m_per_s"].values[placement_table] = v_gas_from_sum / internal_pipes
res_table["v_to_m_per_s"].values[placement_table] = v_gas_to_sum / internal_pipes
res_table["v_mean_m_per_s"].values[placement_table] = v_gas_mean_sum / internal_pipes
res_table["normfactor_from"].values[placement_table] = nf_from_sum / internal_pipes
res_table["normfactor_to"].values[placement_table] = nf_to_sum / internal_pipes
else:
res_table["v_mean_m_per_s"].values[placement_table] = v_sum
res_table["p_from_bar"].values[placement_table] = node_pit[from_junction_nodes, PINIT]
res_table["p_to_bar"].values[placement_table] = node_pit[to_junction_nodes, PINIT]
res_table["t_from_k"].values[placement_table] = node_pit[from_junction_nodes, TINIT_NODE]
res_table["t_to_k"].values[placement_table] = node_pit[to_junction_nodes, TINIT_NODE]
res_table["mdot_to_kg_per_s"].values[placement_table] = -mf_sum
res_table["mdot_from_kg_per_s"].values[placement_table] = mf_sum
res_table["vdot_norm_m3_per_s"].values[placement_table] = vf_sum
idx_pit = heat_exchanger_pit[:, ELEMENT_IDX]
idx_sort, lambda_sum, reynolds_sum, = \
_sum_by_group(idx_pit, heat_exchanger_pit[:, LAMBDA], heat_exchanger_pit[:, RE])
res_table["lambda"].values[placement_table] = lambda_sum
res_table["reynolds"].values[placement_table] = reynolds_sum
def build_system_matrix(net, branch_pit, node_pit, heat_mode):
"""
:param net: The pandapipes network
:type net: pandapipesNet
:param branch_pit: pandapipes internal table for branching components such as pipes or valves
:type branch_pit: numpy.ndarray
:param node_pit: pandapipes internal table for node components
:type node_pit: numpy.ndarray
:param heat_mode: Is it a heat network calculation: True or False
:type heat_mode: bool
:return: system_matrix, load_vector
:rtype: system_matrix - scipy.sparse.csr.csr_matrix, load_vector - numpy.ndarray
"""
update_option = get_net_option(net, "only_update_hydraulic_matrix")
update_only = update_option and "hydraulic_data_sorting" in net["_internal_data"] \
and "hydraulic_matrix" in net["_internal_data"]
len_b = len(branch_pit)
len_n = len(node_pit)
branch_matrix_indices = np.arange(len_b) + len_n
fn_col, tn_col, ntyp_col, slack_type, num_der = (FROM_NODE, TO_NODE, NODE_TYPE, P, 3) \
if not heat_mode else (FROM_NODE_T, TO_NODE_T, NODE_TYPE_T, T, 2)
fn = branch_pit[:, fn_col].astype(np.int32)
tn = branch_pit[:, tn_col].astype(np.int32)
not_slack_fn_branch_mask = node_pit[fn, ntyp_col] != slack_type
not_slack_tn_branch_mask = node_pit[tn, ntyp_col] != slack_type
slack_nodes = np.where(node_pit[:, ntyp_col] == slack_type)[0]
if not heat_mode:
len_fn_not_slack = np.sum(not_slack_fn_branch_mask)
len_tn_not_slack = np.sum(not_slack_tn_branch_mask)
len_fn1 = num_der * len_b + len_fn_not_slack
len_tn1 = len_fn1 + len_tn_not_slack
full_len = len_tn1 + slack_nodes.shape[0]
else:
inc_flow_sum = np.zeros(len(node_pit[:, LOAD]))
tn_unique_der, tn_sums_der = _sum_by_group(
tn, branch_pit[:, JAC_DERIV_DT_NODE])
inc_flow_sum[tn_unique_der] += tn_sums_der
len_fn1 = num_der * len_b + len(tn_unique_der)
len_tn1 = len_fn1 + len_b
full_len = len_tn1 + slack_nodes.shape[0]
system_data = np.zeros(full_len, dtype=np.float64)
if not heat_mode:
# pdF_dv
system_data[:len_b] = branch_pit[:, JAC_DERIV_DV]
# pdF_dpi
system_data[len_b:2 * len_b] = branch_pit[:, JAC_DERIV_DP]
# pdF_dpi1
system_data[2 * len_b:3 * len_b] = branch_pit[:, JAC_DERIV_DP1]
# jdF_dv_from_nodes
system_data[3 * len_b:len_fn1] = branch_pit[not_slack_fn_branch_mask,
JAC_DERIV_DV_NODE]
# jdF_dv_to_nodes
system_data[len_fn1:len_tn1] = branch_pit[not_slack_tn_branch_mask,
JAC_DERIV_DV_NODE] * (-1)
# p_nodes
system_data[len_tn1:] = 1
else:
system_data[:len_b] = branch_pit[:, JAC_DERIV_DT]
# pdF_dpi1
system_data[len_b:2 * len_b] = branch_pit[:, JAC_DERIV_DT1]
# jdF_dv_from_nodes
system_data[2 * len_b:len_fn1] = inc_flow_sum[tn_unique_der]
# jdF_dv_to_nodes
data = branch_pit[:, JAC_DERIV_DT_NODE] * (-1)
rows = tn
index = np.argsort(rows)
data = data[index]
system_data[len_fn1:len_fn1 + len_b] = data
system_data[len_fn1 + len_b:] = 1
if not update_only:
system_cols = np.zeros(full_len, dtype=np.int32)
system_rows = np.zeros(full_len, dtype=np.int32)
if not heat_mode:
# pdF_dv
system_cols[:len_b] = branch_matrix_indices
system_rows[:len_b] = branch_matrix_indices
# pdF_dpi
system_cols[len_b:2 * len_b] = fn
system_rows[len_b:2 * len_b] = branch_matrix_indices
# pdF_dpi1
system_cols[2 * len_b:3 * len_b] = tn
system_rows[2 * len_b:3 * len_b] = branch_matrix_indices
# jdF_dv_from_nodes
system_cols[3 * len_b:len_fn1] = branch_matrix_indices[
not_slack_fn_branch_mask]
system_rows[3 * len_b:len_fn1] = fn[not_slack_fn_branch_mask]
# jdF_dv_to_nodes
system_cols[len_fn1:len_tn1] = branch_matrix_indices[
not_slack_tn_branch_mask]
system_rows[len_fn1:len_tn1] = tn[not_slack_tn_branch_mask]
# p_nodes
system_cols[len_tn1:] = slack_nodes
system_rows[len_tn1:] = slack_nodes
else:
# pdF_dTfromnode
system_cols[:len_b] = fn
system_rows[:len_b] = branch_matrix_indices
# pdF_dTout
system_cols[len_b:2 * len_b] = branch_matrix_indices
system_rows[len_b:2 * len_b] = branch_matrix_indices
# t_nodes
system_cols[len_fn1 + len_b:] = slack_nodes
system_rows[len_fn1 + len_b:] = np.arange(0, len(slack_nodes))
# jdF_dTnode_
tn_unique_idx = np.unique(tn, return_index=True)
system_cols[2 * len_b:len_fn1] = tn_unique_idx[0]
system_rows[2 * len_b:len_fn1] = len(slack_nodes) + np.arange(
0, len(tn_unique_der))
# jdF_dTout
branch_order = np.argsort(tn)
tn_uni, tn_uni_counts = np.unique(tn[branch_order],
return_counts=True)
row_index = np.repeat(np.arange(len(tn_uni)), tn_uni_counts)
system_cols[len_fn1:len_fn1 +
len_b] = branch_matrix_indices[branch_order]
system_rows[len_fn1:len_fn1 + len_b] = len(slack_nodes) + row_index
if not update_option:
system_matrix = csr_matrix(
(system_data, (system_rows, system_cols)),
shape=(len_n + len_b, len_n + len_b))
else:
data_order = np.lexsort([system_cols, system_rows])
system_data = system_data[data_order]
system_cols = system_cols[data_order]
system_rows = system_rows[data_order]
row_counter = np.zeros(len_b + len_n + 1, dtype=np.int32)
unique_rows, row_counts = _sum_by_group_sorted(
system_rows, np.ones_like(system_rows))
row_counter[unique_rows + 1] += row_counts
ptr = row_counter.cumsum()
system_matrix = csr_matrix((system_data, system_cols, ptr),
shape=(len_n + len_b, len_n + len_b))
net["_internal_data"]["hydraulic_data_sorting"] = data_order
net["_internal_data"]["hydraulic_matrix"] = system_matrix
else:
data_order = net["_internal_data"]["hydraulic_data_sorting"]
system_data = system_data[data_order]
system_matrix = net["_internal_data"]["hydraulic_matrix"]
system_matrix.data = system_data
if not heat_mode:
load_vector = np.empty(len_n + len_b)
load_vector[len_n:] = branch_pit[:, LOAD_VEC_BRANCHES]
load_vector[:len_n] = node_pit[:, LOAD] * (-1)
fn_unique, fn_sums = _sum_by_group(fn, branch_pit[:, LOAD_VEC_NODES])
tn_unique, tn_sums = _sum_by_group(tn, branch_pit[:, LOAD_VEC_NODES])
load_vector[fn_unique] -= fn_sums
load_vector[tn_unique] += tn_sums
load_vector[slack_nodes] = 0
else:
tn_unique, tn_sums = _sum_by_group(tn, branch_pit[:, LOAD_VEC_NODES_T])
load_vector = np.zeros(len_n + len_b)
load_vector[len(slack_nodes) +
np.arange(0, len(tn_unique_der))] += tn_sums
load_vector[len(slack_nodes) + np.arange(0, len(
tn_unique_der))] -= tn_sums_der * node_pit[tn_unique_der, TINIT]
load_vector[0:len(slack_nodes)] = 0.
load_vector[len_n:] = branch_pit[:, LOAD_VEC_BRANCHES_T]
return system_matrix, load_vector
