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Commit 0528b858 authored by Steinmann Victor's avatar Steinmann Victor
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tidied up functions used to express constraints

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%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Formulieren der Optimierungsgleichung in pymoo Formulieren der Optimierungsgleichung in pymoo
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Es gilt die Kontinuitätsgleichung: Es gilt die Kontinuitätsgleichung:
$ \Sigma \dot{V}_k(t) = O$ $ \Sigma \dot{V}_k(t) = O$
und die aus der Topologie resultierende Inzidenzmatrix $A_i$ und die aus der Topologie resultierende Inzidenzmatrix $A_i$
sowie die aus dem Pumpenkennfeld folgende Beziehung: sowie die aus dem Pumpenkennfeld folgende Beziehung:
$\Delta p=\alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2 : n \in \{0\} \cup [n_{\mathrm{min}},n_{\mathrm{max}}] $ $\Delta p=\alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2 : n \in \{0\} \cup [n_{\mathrm{min}},n_{\mathrm{max}}] $
$P=\beta_1 Q^3+\beta_2 Q^2 n+\beta_3 Q n^2+\beta_4n^3+\beta_5$ $P=\beta_1 Q^3+\beta_2 Q^2 n+\beta_3 Q n^2+\beta_4n^3+\beta_5$
und die beziehung für den Druckverlust an den Ventilen: und die beziehung für den Druckverlust an den Ventilen:
$\Delta p_{\mathrm{loss}} = - \frac{1}{2} \varrho \zeta \left(\frac{Q}{A}\right)^2 = -l Q^2 :l\in [l_{\mathrm{min}}:\infty )$ $\Delta p_{\mathrm{loss}} = - \frac{1}{2} \varrho \zeta \left(\frac{Q}{A}\right)^2 = -l Q^2 :l\in [l_{\mathrm{min}}:\infty )$
nun soll für einen Gegebenen Volumenstrom $Q$ eine Optimale Drehzahl bestimmt werden, welche die Pumpenlesitung minimiert. nun soll für einen Gegebenen Volumenstrom $Q$ eine Optimale Drehzahl bestimmt werden, welche die Pumpenlesitung minimiert.
$$ $$
\begin{align*} \begin{align*}
\mathrm{min} \sum_{p \in \mathcal{P}} Po_{p} \\ \mathrm{min} \sum_{p \in \mathcal{P}} Po_{p} \\
Q_{p,i} \geq \sum_{strang} Q_v + \sum_{strang} Q_p \\ Q_{p,i} \geq \sum_{strang} Q_v + \sum_{strang} Q_p \\
Q_p , n\epsilon [n_{min},n_{max}] \\ Q_p , n\epsilon [n_{min},n_{max}] \\
\overrightarrow{n} = (1,n,n^2,n^3)^T \\ \overrightarrow{n} = (1,n,n^2,n^3)^T \\
min P = A \overrightarrow{n} \\ min P = A \overrightarrow{n} \\
-n\leq n_{min} \\ -n\leq n_{min} \\
n\leq n_{max} n\leq n_{max}
\end{align*} \end{align*}
$$ $$
Förderhöhe als constraint continuität fomulieren pro strang Förderhöhe als constraint continuität fomulieren pro strang
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
!pip install pyomo !pip install pyomo
``` ```
%% Output %% Output
Defaulting to user installation because normal site-packages is not writeable Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: pyomo in c:\users\steinmann\appdata\roaming\python\python312\site-packages (6.8.2) Collecting pyomo
Requirement already satisfied: ply in c:\users\steinmann\appdata\roaming\python\python312\site-packages (from pyomo) (3.11) Downloading Pyomo-6.8.2-py3-none-any.whl.metadata (8.0 kB)
Collecting ply (from pyomo)
Downloading ply-3.11-py2.py3-none-any.whl.metadata (844 bytes)
Downloading Pyomo-6.8.2-py3-none-any.whl (3.7 MB)
---------------------------------------- 0.0/3.7 MB ? eta -:--:--
---------------------------- ----------- 2.6/3.7 MB 12.6 MB/s eta 0:00:01
---------------------------------------- 3.7/3.7 MB 11.6 MB/s eta 0:00:00
Downloading ply-3.11-py2.py3-none-any.whl (49 kB)
Installing collected packages: ply, pyomo
Successfully installed ply-3.11 pyomo-6.8.2
[notice] A new release of pip is available: 24.2 -> 25.0 [notice] A new release of pip is available: 24.3.1 -> 25.0
[notice] To update, run: C:\Program Files\Python312\python.exe -m pip install --upgrade pip [notice] To update, run: C:\Users\Victor\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\python.exe -m pip install --upgrade pip
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#Pump-Powercurve and Pump-Hightcurve #Pump-Powercurve and Pump-Hightcurve
import regression_own import regression_own
(LR_H,LR_P)=regression_own.regress_pump() (LR_H,LR_P)=regression_own.regress_pump()
``` ```
%% Output %% Output
R^20.9998289611292903 R^20.9998289611292903
R^20.9994449560888792 R^20.9994449560888792
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#Graph constroctor #Graph constroctor
#Alle Ventile sind direkt mit der Quelle/Senke Verbunden #Alle Ventile sind direkt mit der Quelle/Senke Verbunden
import multiDiGraph as gr import multiDiGraph as gr
nodes =['source','pump1','pump2','valveA','valveB','valveC'] nodes =['source','pump1','pump2','valveA','valveB','valveC']
graph = gr.construct_graph('source',('source','pump1',0.),('pump1','pump2',0.),('pump2','valveA',0.),('pump2','valveB',0.), graph = gr.construct_graph('source',('source','pump1',0.),('pump1','pump2',0.),('pump2','valveA',0.),('pump2','valveB',0.),
('pump1','valveC',0.),('valveA','source',4.),('valveB','source',4.),('valveC','source',4.)) ('pump1','valveC',0.),('valveA','source',4.),('valveB','source',4.),('valveC','source',4.))
#ist das notwendig?!? #ist das notwendig?!?
for node in graph.nodes: for node in graph.nodes:
#definieren der Drehzahl für jede Pumpe im graphen #definieren der Drehzahl für jede Pumpe im graphen
#inizieren des Durchflusses für jedes Ventil im Graphen #inizieren des Durchflusses für jedes Ventil im Graphen
if 'pump' in node: if 'pump' in node:
graph.nodes[node]['n']=750/3600 graph.nodes[node]['n']=750/3600
else: else:
graph.nodes[node]['n']=None graph.nodes[node]['n']=None
graph.nodes[node]['flow']=0. graph.nodes[node]['flow']=0.
if 'valve' in node: if 'valve' in node:
graph.nodes[node]['flow']= graph[node]['source'][0]['weight'] graph.nodes[node]['flow']= graph[node]['source'][0]['weight']
for node in graph.nodes: for node in graph.nodes:
#Berechnen des Durchflusses im Knoten #Berechnen des Durchflusses im Knoten
if 'valve' in node: if 'valve' in node:
continue continue
for inF in graph.predecessors(node): for inF in graph.predecessors(node):
graph.nodes[node]['flow'] += graph[inF][node][0]['weight'] graph.nodes[node]['flow'] += graph[inF][node][0]['weight']
#Berechnen des Durchflusses der abgehenden Kanten #Berechnen des Durchflusses der abgehenden Kanten
tempF=graph.nodes[node]['flow'] tempF=graph.nodes[node]['flow']
SC=0 SC=0
for outF in graph.successors(node): for outF in graph.successors(node):
if 'valve' in outF: if 'valve' in outF:
graph[node][outF][0]['weight']=graph.nodes[outF]['flow'] graph[node][outF][0]['weight']=graph.nodes[outF]['flow']
tempF=tempF - graph.nodes[outF]['flow'] tempF=tempF - graph.nodes[outF]['flow']
else: else:
SC+=1 SC+=1
for outF in graph.successors(node): for outF in graph.successors(node):
if SC!=0. and not'valve' in outF: if SC!=0. and not'valve' in outF:
graph[node][outF][0]['weight']=tempF/SC graph[node][outF][0]['weight']=tempF/SC
else:continue else:continue
``` ```
%% Output %% Output
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import networkx as nx import networkx as nx
Mtrx= nx.incidence_matrix(graph,nodes,oriented=True) Mtrx= nx.incidence_matrix(graph,nodes,oriented=True)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
import networkx as nx import networkx as nx
def create_dict(GR:nx.multidigraph): def create_dict(GR:nx.multidigraph):
data={None:{'nodes':{}, data={None:{'nodes':{},
'pumps':{}, 'pumps':{},
'valves':{}, 'valves':{},
} }
} }
for node in GR.nodes: for node in GR.nodes:
data[None]['nodes'][node]=None data[None]['nodes'][node]=None
data[None]['Q'][node]=GR.nodes[node]['flow'] data[None]['Q'][node]=GR.nodes[node]['flow']
if 'pump' in node: if 'pump' in node:
data[None]['pumps'][node]=None data[None]['pumps'][node]=None
data[None]['n'][node]=0. data[None]['n'][node]=0.
if 'valve' in node: if 'valve' in node:
data[None]['valves'][node]=None data[None]['valves'][node]=None
return data return data
``` ```
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Durchfluss aus Incidenzmatrix beerechnen Durchfluss aus Incidenzmatrix beerechnen
$-l Q^2 = \alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2$ $-l Q^2 = \alpha_1 Q^2+\alpha_2 Q n+\alpha_3 n^2$
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
#defining abstract modell for given Network #defining abstract modell for given Network
import pyomo.environ as pyo import pyomo.environ as pyo
from pyomo.dataportal import DataPortal from pyomo.dataportal import DataPortal
import numpy as np import numpy as np
from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression
modell = pyo.AbstractModel() modell = pyo.AbstractModel()
#notwendige Mengen zur Berechnung der Constraints #notwendige Mengen zur Berechnung der Constraints
modell.nodes = pyo.Set() modell.nodes = pyo.Set()
modell.pumps = pyo.Set() modell.pumps = pyo.Set()
modell.valves = pyo.Set() modell.valves = pyo.Set()
modell.Q_valve=pyo.Param(modell.valves) modell.Q_valve=pyo.Param(modell.valves)
#Optimierungsvariable #Optimierungsvariable
modell.n = pyo.Var(modell.pumps,bounds=(750/3600,1)) modell.n = pyo.Var(modell.pumps,bounds=(750/3600,1))
modell.Q = pyo.Var(modell.nodes) modell.Q = pyo.Var(modell.nodes,bounds=(0.,10.))
#expressions for constraints: #expressions for constraints:
def PumpFlow(modell,pump): def PumpFlow(modell,pump):
return np.dot(np.array([modell.Q[pump]**2,modell.n[pump]*modell.Q[pump],modell.n[pump]**2]),LR_H.coef_) return pyo.summation(np.array([modell.Q[pump]**2, modell.n[pump]*modell.Q[pump], modell.n[pump]**2]),LR_H.coef_,index=[0,1,2])
def Pump_delivery_req(modell,pump): def Pump_delivery_req(modell,pump):
return PumpFlow(modell,pump) ==pyo.summation(modell.Q,index=graph.successors(pump)) return PumpFlow(modell,pump) ==pyo.summation(modell.Q,index=graph.successors(pump))
def valve_req_rule(modell,valve): def valve_req_rule(modell,valve):
return modell.Q[valve]>=modell.Q_valve[valve] return modell.Q[valve]>=modell.Q_valve[valve]
#modell.Flow_Objective = pyo.Objective(modell.pumps,rule=Flow_req,sense=pyo.minimize) def continuity_inflow(modell,node):
return modell.Q[node]==pyo.summation(modell.Q, index=graph.successors(node))
#Constaints def continuity_outflow(modell,node):
def continuityRule(modell,node): return modell.Q[node]==pyo.summation(modell.Q,index=graph.predecessors(node))
return pyo.summation(modell.Q, index=graph.predecessors(node))==pyo.summation(modell.Q, index=graph.successors(node))
#Objective #Objective
def PumpPower(modell): def PumpPower(modell):
return sum(np.dot( return sum(np.dot(
np.array( np.array(
[modell.Q[i]**3,(modell.Q[i]**2)*modell.n[i],modell.Q[i]*modell.n[i]**2,modell.n[i]**3] [modell.Q[i]**3,(modell.Q[i]**2)*modell.n[i],modell.Q[i]*modell.n[i]**2,modell.n[i]**3]
),LR_P.coef_ ),LR_P.coef_
) for i in modell.pumps) ) for i in modell.pumps)
modell.Power_Objective = pyo.Objective(rule=PumpPower,sense=pyo.minimize) modell.Power_Objective = pyo.Objective(rule=PumpPower,sense=pyo.minimize)
TestData={
None:{
'nodes':[key for key in graph.nodes.keys()],
'pumps':[key for key in graph.nodes.keys() if 'pump' in key],
'valves':[key for key in graph.nodes.keys() if 'valve' in key],
'Q_valve':{'valveA':1.,'valveB':1.,'valveC':2.},
}
}
print(TestData)
``` ```
%% Output
{None: {'nodes': ['source', 'pump1', 'pump2', 'valveA', 'valveB', 'valveC'], 'pumps': ['pump1', 'pump2'], 'valves': ['valveA', 'valveB', 'valveC'], 'Q_valve': {'valveA': 1.0, 'valveB': 1.0, 'valveC': 2.0}}}
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
Frage: gibt es nur eine Lösung für Drehzahl? Frage: gibt es nur eine Lösung für Drehzahl?
Bsp. Optimierung nach Dezentraler Pumpe um modell zu prüfen Bsp. Optimierung nach Dezentraler Pumpe um modell zu prüfen
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` python ``` python
from pyomo.opt import SolverFactory from pyomo.opt import SolverFactory
opt = pyo.SolverFactory('scipampl', executable=r'C:\Program Files\SCIPOptSuite 9.2.0\bin\scip.exe') TestData={
None:{
'nodes':[key for key in graph.nodes.keys()],
'pumps':[key for key in graph.nodes.keys() if 'pump' in key],
'valves':[key for key in graph.nodes.keys() if 'valve' in key],
'Q_valve':{'valveA':1.,'valveB':1.,'valveC':1.},
}
}
opt = pyo.SolverFactory('scipampl', executable=r'C:\Program Files\SCIPOptSuite 9.2.1\bin\scip.exe')
instance = modell.create_instance(TestData) instance = modell.create_instance(TestData)
instance.Continuity_constaint=pyo.Constraint(instance.nodes, rule=continuityRule)
instance.Flow_constraint=pyo.Constraint(instance.valves,rule=valve_req_rule) instance.Flow_constraint=pyo.Constraint(instance.valves,rule=valve_req_rule)
instance.pump_Flow_constraint=pyo.Constraint(instance.pumps,rule=Pump_delivery_req) instance.pump_Flow_constraint=pyo.Constraint(instance.pumps,rule=Pump_delivery_req)
#instance.flow_constraint=pyo.Constraint(instance.nodes,rule=continuity_inflow)
#instance.flow_constraint=pyo.Constraint(instance.nodes,rule=continuity_outflow)
result=opt.solve(instance, tee=True) result=opt.solve(instance, tee=True)
print(result)
instance.n.pprint() instance.n.pprint()
instance.Q.pprint() instance.Q.pprint()
``` ```
%% Output %% Output
SCIP version 9.2.0 [precision: 8 byte] [memory: block] [mode: optimized] [LP solver: Soplex 7.1.2] [GitHash: 74cea9222e] SCIP version 9.2.1 [precision: 8 byte] [memory: block] [mode: optimized] [LP solver: Soplex 7.1.3] [GitHash: 0d2d3c7c2d]
Copyright (c) 2002-2024 Zuse Institute Berlin (ZIB) Copyright (c) 2002-2025 Zuse Institute Berlin (ZIB)
External libraries: External libraries:
Soplex 7.1.2 Linear Programming Solver developed at Zuse Institute Berlin (soplex.zib.de) [GitHash: b040369c] Soplex 7.1.3 Linear Programming Solver developed at Zuse Institute Berlin (soplex.zib.de) [GitHash: 60fd96f2]
CppAD 20180000.0 Algorithmic Differentiation of C++ algorithms developed by B. Bell (github.com/coin-or/CppAD) CppAD 20180000.0 Algorithmic Differentiation of C++ algorithms developed by B. Bell (github.com/coin-or/CppAD)
TinyCThread 1.2 small portable implementation of the C11 threads API (tinycthread.github.io) TinyCThread 1.2 small portable implementation of the C11 threads API (tinycthread.github.io)
MPIR 3.0.0 Multiple Precision Integers and Rationals Library developed by W. Hart (mpir.org) MPIR 3.0.0 Multiple Precision Integers and Rationals Library developed by W. Hart (mpir.org)
ZIMPL 3.6.2 Zuse Institute Mathematical Programming Language developed by T. Koch (zimpl.zib.de) ZIMPL 3.6.2 Zuse Institute Mathematical Programming Language developed by T. Koch (zimpl.zib.de)
AMPL/MP 690e9e7 AMPL .nl file reader library (github.com/ampl/mp) AMPL/MP 690e9e7 AMPL .nl file reader library (github.com/ampl/mp)
PaPILO 2.4.0 parallel presolve for integer and linear optimization (github.com/scipopt/papilo) (built with TBB) [GitHash: 2d9fe29f] PaPILO 2.4.1 parallel presolve for integer and linear optimization (github.com/scipopt/papilo) (built with TBB) [GitHash: 11974394]
Nauty 2.8.8 Computing Graph Automorphism Groups by Brendan D. McKay (users.cecs.anu.edu.au/~bdm/nauty) Nauty 2.8.8 Computing Graph Automorphism Groups by Brendan D. McKay (users.cecs.anu.edu.au/~bdm/nauty)
sassy 1.1 Symmetry preprocessor by Markus Anders (github.com/markusa4/sassy) sassy 1.1 Symmetry preprocessor by Markus Anders (github.com/markusa4/sassy)
Ipopt 3.14.16 Interior Point Optimizer developed by A. Waechter et.al. (github.com/coin-or/Ipopt) Ipopt 3.14.16 Interior Point Optimizer developed by A. Waechter et.al. (github.com/coin-or/Ipopt)
user parameter file <scip.set> not found - using default parameters user parameter file <scip.set> not found - using default parameters
read problem <C:\Users\STEINM~1\AppData\Local\Temp\tmprv0ikbwh.pyomo.nl> read problem <C:\Users\Victor\AppData\Local\Temp\tmpfcunk5gi.pyomo.nl>
============ ============
original problem has 9 variables (0 bin, 0 int, 0 impl, 9 cont) and 12 constraints original problem has 8 variables (0 bin, 0 int, 0 impl, 8 cont) and 6 constraints
solve problem solve problem
============= =============
presolving: presolving:
(round 1, fast) 2 del vars, 6 del conss, 0 add conss, 12 chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs (round 1, fast) 0 del vars, 3 del conss, 0 add conss, 11 chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs
presolving (2 rounds: 2 fast, 0 medium, 0 exhaustive): (0.0s) symmetry computation started: requiring (bin +, int +, cont +), (fixed: bin -, int -, cont -)
2 deleted vars, 6 deleted constraints, 0 added constraints, 16 tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients (0.0s) symmetry computation finished: 1 generators found (max: 1500, log10 of symmetry group size: 0.0) (symcode time: 0.00)
dynamic symmetry handling statistics:
orbitopal reduction: no components
orbital reduction: no components
lexicographic reduction: no permutations
handled 1 out of 1 symmetry components
(round 2, exhaustive) 0 del vars, 3 del conss, 1 add conss, 11 chg bounds, 0 chg sides, 0 chg coeffs, 0 upgd conss, 0 impls, 0 clqs
presolving (3 rounds: 3 fast, 2 medium, 2 exhaustive):
0 deleted vars, 3 deleted constraints, 0 added constraints, 11 tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients
0 implications, 0 cliques 0 implications, 0 cliques
presolving detected infeasibility presolved problem has 8 variables (0 bin, 0 int, 0 impl, 8 cont) and 4 constraints
1 constraints of type <linear>
3 constraints of type <nonlinear>
Presolving Time: 0.00 Presolving Time: 0.00
SCIP Status : problem is solved [infeasible] time | node | left |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr| dualbound | primalbound | gap | compl.
0.0s| 1 | 0 | 22 | - | 914k | 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | -- | Inf | unknown
L 0.0s| 1 | 0 | 22 | - | subnlp| 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 22 | - | 914k | 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 22 | - | 914k | 0 | 25 | 4 | 36 | 0 | 0 | 0 | 0 |-3.474607e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 26 | - | 920k | 0 | 25 | 4 | 39 | 3 | 1 | 0 | 0 |-1.963132e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 26 | - | 920k | 0 | 25 | 4 | 39 | 3 | 1 | 0 | 0 |-1.963132e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 30 | - | 935k | 0 | 25 | 4 | 42 | 6 | 2 | 0 | 0 |-1.885311e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 33 | - | 939k | 0 | 25 | 4 | 45 | 9 | 3 | 0 | 0 |-1.766937e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 33 | - | 939k | 0 | 25 | 4 | 45 | 9 | 3 | 0 | 0 |-1.766937e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 36 | - | 939k | 0 | 25 | 4 | 47 | 11 | 4 | 0 | 0 |-1.728948e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 37 | - | 941k | 0 | 25 | 4 | 48 | 12 | 5 | 0 | 0 |-1.712829e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 38 | - | 941k | 0 | 25 | 4 | 49 | 13 | 6 | 0 | 0 |-1.701866e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 39 | - | 943k | 0 | 25 | 4 | 50 | 14 | 7 | 0 | 0 |-1.701746e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 41 | - | 951k | 0 | 25 | 4 | 52 | 16 | 8 | 0 | 0 |-1.695801e+01 | 1.151406e+01 | Inf | unknown
0.0s| 1 | 0 | 332 | - | 957k | 0 | 25 | 4 | 52 | 16 | 9 | 0 | 0 | 1.150982e+01 | 1.151406e+01 | 0.04%| unknown
time | node | left |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr| dualbound | primalbound | gap | compl.
0.0s| 1 | 0 | 334 | - | 957k | 0 | 25 | 4 | 54 | 18 | 10 | 0 | 0 | 1.150982e+01 | 1.151406e+01 | 0.04%| unknown
0.0s| 1 | 0 | 346 | - | 957k | 0 | 25 | 4 | 37 | 20 | 11 | 0 | 0 | 1.151405e+01 | 1.151406e+01 | 0.00%| unknown
0.0s| 1 | 0 | 346 | - | 957k | 0 | 25 | 4 | 20 | 20 | 11 | 0 | 0 | 1.151405e+01 | 1.151406e+01 | 0.00%| unknown
0.0s| 1 | 0 | 347 | - | 957k | 0 | 25 | 4 | 21 | 21 | 12 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown
L 0.0s| 1 | 0 | 347 | - | subnlp| 0 | 25 | 4 | 21 | 21 | 13 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown
0.0s| 1 | 0 | 347 | - | 958k | 0 | 25 | 4 | 21 | 21 | 13 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown
* 0.0s| 1 | 0 | 347 | - | LP | 0 | 25 | 4 | 21 | 21 | 14 | 0 | 0 | 1.151406e+01 | 1.151406e+01 | 0.00%| unknown
SCIP Status : problem is solved [optimal solution found]
Solving Time (sec) : 0.00 Solving Time (sec) : 0.00
Solving Nodes : 0 Solving Nodes : 1
Primal Bound : +1.00000000000000e+20 (0 solutions) Primal Bound : +1.15140564709997e+01 (3 solutions)
Dual Bound : +1.00000000000000e+20 Dual Bound : +1.15140564709997e+01
Gap : 0.00 % Gap : 0.00 %
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown"; Problem:
- termination condition: infeasible - Lower bound: -inf
- message from solver: infeasible Upper bound: inf
Number of objectives: 1
Number of constraints: 0
Number of variables: 7
Sense: unknown
Solver:
- Status: ok
Message: optimal solution found
Termination condition: optimal
Id: 0
Error rc: 0
Time: 0.3383963108062744
Solution:
- number of solutions: 0
number of solutions displayed: 0
n : Size=2, Index=pumps n : Size=2, Index=pumps
Key : Lower : Value : Upper : Fixed : Stale : Domain Key : Lower : Value : Upper : Fixed : Stale : Domain
pump1 : 0.20833333333333334 : None : 1 : False : True : Reals pump1 : 0.20833333333333334 : 0.34519801810114614 : 1 : False : False : Reals
pump2 : 0.20833333333333334 : None : 1 : False : True : Reals pump2 : 0.20833333333333334 : 0.4881837415314729 : 1 : False : False : Reals
Q : Size=6, Index=nodes Q : Size=6, Index=nodes
Key : Lower : Value : Upper : Fixed : Stale : Domain Key : Lower : Value : Upper : Fixed : Stale : Domain
pump1 : None : None : None : False : True : Reals pump1 : 0.0 : 0.0 : 10.0 : False : False : Reals
pump2 : None : None : None : False : True : Reals pump2 : 0.0 : 0.0 : 10.0 : False : False : Reals
source : None : None : None : False : True : Reals source : 0.0 : None : 10.0 : False : True : Reals
valveA : None : None : None : False : True : Reals valveA : 0.0 : 1.0 : 10.0 : False : False : Reals
valveB : None : None : None : False : True : Reals valveB : 0.0 : 1.0 : 10.0 : False : False : Reals
valveC : None : None : None : False : True : Reals valveC : 0.0 : 1.0 : 10.0 : False : False : Reals
......
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