added expression for all pumps in graph.nodes

3cdccb21 · Steinmann · ba6abf31 · 3cdccb21
Commit 3cdccb21 authored 5 months ago by Steinmann
--- a/optimierung_pymoo.ipynb
+++ b/optimierung_pymoo.ipynb
@@ -226,6 +226,7 @@
    "\n",
    "\n",
    "TestData={None:{'G':{None:graph},'LR':{None:LR_H}}}\n",
+    "#[node for node in graph.nodes if 'pump' in node]\n",
    "#Optimierungsgleichung\n",
    "#instance=modell.create_instance(graph,LR_H)\n",
    "#instance.obj = pyo.Objective(expr=sum(PumpPower(modell.Q[i],modell.n[i],LR_P) for i in modell.pumps),sense=min)\n"

 %% Cell type:markdown id: tags:

 Formulieren der Optimierungsgleichung in pymoo

 %% Cell type:markdown id: tags:

 Es gilt die Kontinuitätsgleichung:

 $ \Sigma \dot{V}_k(t) = O$

 und die aus der Topologie resultierende Inzidenzmatrix $A_i$

 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}}] $

 $P=\beta_1 Q^3+\beta_2 Q^2 n+\beta_3 Q n^2+\beta_4n^3+\beta_5$

 und die beziehung für den Druckverlust an den Ventilen:

 $\Delta p_{\mathrm{loss}} = - \frac{1}{2} \varrho \zeta \left(\frac{Q}{A}\right)^2 = -l Q^2 :l\in [l_{\mathrm{min}}:\infty )$


 nun soll für einen Gegebenen Volumenstrom $Q$ eine Optimale Drehzahl bestimmt werden, welche die Pumpenlesitung minimiert.

 $$
 \begin{align*}
 \mathrm{min} \sum_{p \in \mathcal{P}} Po_{p}  \\
 Q_p = Q_{\mathrm{target}, i} \\
 Q_p
 , n\epsilon [n_{min},n_{max}] \\
 \overrightarrow{n} = (1,n,n^2,n^3)^T \\
 min P = A \overrightarrow{n} \\
 -n\leq n_{min} \\
 n\leq n_{max}
 \end{align*}
 $$

 Förderhöhe als constraint continuität fomulieren pro strang




 %% Cell type:code id: tags:

 ``` python
 !pip install pyomo
 ```

 %% Output

    Defaulting to user installation because normal site-packages is not writeable
    Collecting pyomo
      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 -:--:--
       ---------------------------------------- 3.7/3.7 MB 73.6 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

      WARNING: The script pyomo.exe is installed in 'C:\Users\steinmann\AppData\Roaming\Python\Python312\Scripts' which is not on PATH.
      Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
    
    [notice] A new release of pip is available: 24.2 -> 24.3.1
    [notice] To update, run: C:\Program Files\Python312\python.exe -m pip install --upgrade pip

 %% Cell type:code id: tags:

 ``` python
 #Pump-Powercurve and Pump-Hightcurve
 import regression_own
 (LR_H,LR_P)=regression_own.regress_pump()
 print(LR_H,LR_P)
 ```

 %% Output

    R^20.9998289611292903
    R^20.9994449560888792
    LinearRegression(fit_intercept=False) LinearRegression()



 %% Cell type:code id: tags:

 ``` python
 #Graph constroctor
 import multiDiGraph as gr
 nodes =['source','pump1','pump2','valveA','valveB','valveC']
 graph = gr.construct_graph('source',('source','pump1'),('pump1','valveC'),
                           ('pump1','pump2'),('pump2','valveA'),('pump2','valveB'),('valveA','source'),('valveB','source'),('valveC','source'))
 ```

 %% Output



 %% Cell type:code id: tags:

 ``` python
 import networkx as nx
 Mtrx= nx.incidence_matrix(graph,nodes)
 print(Mtrx)
 ```

 %% Output

    <Compressed Sparse Column sparse array of dtype 'float64'
    	with 16 stored elements and shape (6, 8)>
      Coords	Values
      (0, 0)	1.0
      (1, 0)	1.0
      (1, 1)	1.0
      (5, 1)	1.0
      (1, 2)	1.0
      (2, 2)	1.0
      (0, 3)	1.0
      (5, 3)	1.0
      (2, 4)	1.0
      (3, 4)	1.0
      (2, 5)	1.0
      (4, 5)	1.0
      (0, 6)	1.0
      (3, 6)	1.0
      (0, 7)	1.0
      (4, 7)	1.0

 %% Cell type:markdown id: tags:

 Durchfluss aus Incidenzmatrix beerechnen

 Zeilen = knoten
 Spalten = kanten
 Summe pro knoten = 0

 %% Cell type:code id: tags:

 ``` python
 #defining abstract modell for given Network
 import pyomo.environ as pyo
 import numpy as np
 from sklearn.linear_model import LinearRegression

 modell =  pyo.AbstractModel()
 #notwendige Mengen zur Berechnung der Constraints
 modell.nodes = pyo.Set()
 modell.pumps = pyo.Set()
 modell.valves = pyo.Set()

 #Parameter zur berechnung
 modell.Q = pyo.Param(modell.nodes)
 modell.n = pyo.Param(modell.pumps)

 #Optimierungsvariable
 modell.P = pyo.Var(modell.pumps)

 #constraints
 def PumpPower(modell):
    return sum(np.dot(np.array([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_) for i in modell.pumps)
 modell.Power_Constraint = pyo.Objective(rule=PumpPower,sense=min)

 def continuityRule(modell,node):
    return sum(modell.Q[i] for i in graph.predecessors(node))==sum(modell.Q[i] for i in graph.successors(node))
 modell.pump_constraint = pyo.Objective(modell.nodes,rule=continuityRule)


 TestData={None:{'G':{None:graph},'LR':{None:LR_H}}}
+#[node for node in graph.nodes if 'pump' in node]
 #Optimierungsgleichung
 #instance=modell.create_instance(graph,LR_H)
 #instance.obj = pyo.Objective(expr=sum(PumpPower(modell.Q[i],modell.n[i],LR_P) for i in modell.pumps),sense=min)
 ```

 %% Cell type:markdown id: tags:


 Frage:  gibt es nur eine Lösung für Drehzahl?

 Bsp. Optimierung nach Dezentraler Pumpe um modell zu prüfen