diff --git a/.gitignore b/.gitignore
index bbdd72c..c4e6357 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,4 @@
 2bignored.txt
-functions/__pycache__/
+*__pycache__/
 .vscode/settings.json
-__pycache__/Ausgleichsbecken_class_file.cpython-38.pyc
-__pycache__/Ausgleichsbecken.cpython-38.pyc
-__pycache__/functions.cpython-38.pyc
+*.pyc
diff --git a/Ausgleichsbecken.py b/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken.py
similarity index 100%
rename from Ausgleichsbecken.py
rename to Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken.py
diff --git a/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken_class_file.py b/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken_class_file.py
new file mode 100644
index 0000000..b1275c8
--- /dev/null
+++ b/Ausgleichsbecken/dynamic_pipeline_pressure/Ausgleichsbecken_class_file.py
@@ -0,0 +1,87 @@
+from Ausgleichsbecken import FODE_function, get_h_halfstep, get_p_halfstep
+from pressure_conversion import pressure_conversion
+class Ausgleichsbecken_class:
+# units
+    area_unit           = r'$\mathrm{m}^2$'
+    area_outflux_unit   = r'$\mathrm{m}^2$'
+    level_unit          = 'm'
+    volume_unit         = r'$\mathrm{m}^3$'
+    flux_unit           = r'$\mathrm{m}^3/\mathrm{s}$'
+    time_unit           = 's'
+    pressure_unit       = 'Pa'
+
+# init
+    def __init__(self,area,outflux_area,level_min,level_max,timestep = 1):
+        self.area           = area              # base area of the rectangular structure
+        self.area_outflux   = outflux_area      # area of the outlet towards the pipeline        
+        self.level_min      = level_min         # lowest  allowed water level    
+        self.level_max      = level_max         # highest allowed water level
+        self.timestep       = timestep          # timestep of the simulation    
+
+# setter
+    def set_volume(self):
+        self.volume = self.level*self.area 
+
+    def set_initial_level(self,initial_level):
+        self.level = initial_level
+        self.set_volume()
+
+    def set_influx(self,influx):
+        self.influx = influx
+
+    def set_outflux(self,outflux):
+        self.outflux = outflux
+
+# getter
+    def get_area(self):
+        print('The base area of the cuboid reservoir is', self.area, self.area_unit)
+
+    def get_outflux_area(self):
+        print('The outflux area from the cuboid reservoir to the pipeline is', \
+            self.area_outflux, self.area_outflux_unit)
+    
+    def get_level(self):
+        print('The current level in the reservoir is', self.level , self.level_unit)
+
+    def get_crit_levels(self):
+        print('The critical water levels in the reservoir are:  \n',\
+            '   Minimum:', self.level_min , self.level_unit , '\n',\
+            '   Maximum:', self.level_max , self.level_unit )
+
+    def get_volume(self):
+        print('The current water volume in the reservoir is', self.volume, self.volume_unit)
+
+    def get_timestep(self):
+        print('The timestep for the simulation is' , self.timestep, self.time_unit)
+
+    def get_influx(self):
+        print('The current influx is', self.influx, self.flux_unit)
+
+    def get_outflux(self):
+        print('The current outflux is', self.outflux, self.flux_unit)
+
+# methods
+    def update_level(self,timestep):
+        net_flux = self.influx-self.outflux
+        delta_V = net_flux*timestep
+        new_level = (self.volume+delta_V)/self.area
+        return new_level
+
+
+    def e_RK_4(self):
+        # Update to deal with non constant pipeline pressure!
+        yn = self.outflux/self.area_outflux
+        h = self.level
+        dt = self.timestep
+        p,_ = pressure_conversion(self.pressure,self.pressure_unit,'Pa')
+        p_hs,_ = pressure_conversion(self.pressure,self.pressure_unit,'Pa')
+        alpha = (self.area_outflux/self.area-1)
+        h_hs = self.update_level(dt/2)
+        Y1 = yn
+        Y2 = yn + dt/2*FODE_function(Y1, h, alpha, self.pressure)
+        Y3 = yn + dt/2*FODE_function(Y2, h_hs, alpha, p_hs)
+        Y4 = yn + dt*FODE_function(Y3, h_hs, alpha, p_hs)
+        ynp1 = yn + dt/6*(FODE_function(Y1, h, alpha, p)+2*FODE_function(Y2, h_hs, alpha, p_hs)+ \
+            2*FODE_function(Y3, h_hs, alpha, p_hs)+ FODE_function(Y4, h, alpha, p))
+
+        self.outflux = ynp1*self.area_outflux
\ No newline at end of file
diff --git a/Ausgleichsbecken/dynamic_pipeline_pressure/Main_Program.ipynb b/Ausgleichsbecken/dynamic_pipeline_pressure/Main_Program.ipynb
new file mode 100644
index 0000000..51890e2
--- /dev/null
+++ b/Ausgleichsbecken/dynamic_pipeline_pressure/Main_Program.ipynb
@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pressure_conversion import pressure_conversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define constants\n",
+    "initial_level               = 5.        # m\n",
+    "initial_influx              = 0.5       # m³/s\n",
+    "initial_outflux             = 0.        # m³/s\n",
+    "initial_pipeline_pressure   = 1\n",
+    "initial_pressure_unit       = 'bar'\n",
+    "conversion_pressure_unit    = 'mWS'\n",
+    "\n",
+    "area_base                   = 1.        # m²\n",
+    "area_outflux                = 0.5       # m²\n",
+    "critical_level_low          = 0.        # m\n",
+    "critical_level_high         = 10.       # m\n",
+    "simulation_timestep         = 0.001     # s\n",
+    "\n",
+    "# for while loop\n",
+    "total_min_level             = 0.01      # m\n",
+    "total_max_time              = 300       # s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ece5839afa864ae2b836269b18279459",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": "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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img src='data:image/png;base64,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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib widget\n",
+    "\n",
+    "V = Ausgleichsbecken_class(area_base, area_outflux, critical_level_low, critical_level_high,simulation_timestep)\n",
+    "V.set_initial_level(initial_level) \n",
+    "V.set_influx(initial_influx)\n",
+    "V.set_outflux(initial_outflux)\n",
+    "\n",
+    "converted_pressure, V.pressure_unit = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = conversion_pressure_unit)\n",
+    "V.pressure = converted_pressure\n",
+    "\n",
+    "time_vec        = np.arange(0,total_max_time,simulation_timestep)\n",
+    "outflux_vec     = np.empty_like(time_vec)\n",
+    "outflux_vec[0]  = initial_outflux\n",
+    "level_vec       = np.empty_like(time_vec)\n",
+    "level_vec[0]    = initial_level\n",
+    "\n",
+    "pressure_vec    = np.full_like(time_vec,converted_pressure)*((np.sin(time_vec/5)+1)*np.exp(-time_vec/50))\n",
+    "  \n",
+    " \n",
+    "i_max = -1\n",
+    "\n",
+    "for i in range(np.size(time_vec)-1):\n",
+    "    V.pressure = pressure_vec[i]\n",
+    "    V.e_RK_4()\n",
+    "    V.level = V.update_level(V.timestep)\n",
+    "    V.set_volume()\n",
+    "    outflux_vec[i+1] = V.outflux\n",
+    "    level_vec[i+1]   = V.level\n",
+    "    if V.level < total_min_level:\n",
+    "        i_max = i\n",
+    "        break\n",
+    "\n",
+    "\n",
+    "fig1, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1)\n",
+    "fig1.set_figheight(10)\n",
+    "fig1.suptitle('Ausgleichsbecken')\n",
+    "\n",
+    "ax1.plot(time_vec[:i_max],level_vec[:i_max], label='Water level')\n",
+    "ax1.set_ylabel(r'$h$ ['+V.level_unit+']')\n",
+    "ax1.set_xlabel(r'$t$ ['+V.time_unit+']')\n",
+    "ax1.legend()\n",
+    "\n",
+    "ax2.plot(time_vec[:i_max],outflux_vec[:i_max], label='Outflux')\n",
+    "ax2.set_ylabel(r'$Q_{out}$ ['+V.flux_unit+']')\n",
+    "ax2.set_xlabel(r'$t$ ['+V.time_unit+']')\n",
+    "ax2.legend()\n",
+    "\n",
+    "ax3.plot(time_vec[:i_max],pressure_vec[:i_max], label='Pipeline pressure at reservoir')\n",
+    "ax3.set_ylabel(r'$p_{pipeline}$ ['+V.pressure_unit+']')\n",
+    "ax3.set_xlabel(r'$t$ ['+V.time_unit+']')\n",
+    "ax3.legend()\n",
+    "\n",
+    "# plt.subplots_adjust(left=0.2, bottom=0.2)\n",
+    "ax4.set_axis_off()\n",
+    "cell_text = np.array([[initial_level, V.level_unit], \\\n",
+    "            [initial_influx, V.flux_unit], \\\n",
+    "            [initial_outflux, V.flux_unit], \\\n",
+    "            [simulation_timestep, V.time_unit], \\\n",
+    "            [area_base, V.area_unit], \\\n",
+    "            [area_outflux, V.area_unit]])\n",
+    "\n",
+    "row_labels =['initial_level', \\\n",
+    "            'initial_influx', \\\n",
+    "            'initial_outflux', \\\n",
+    "            'simulation_timestep', \\\n",
+    "            'area_base', \\\n",
+    "            'area_outflux']\n",
+    "\n",
+    "plt.table(cellText=cell_text, \\\n",
+    "            cellLoc='center', \\\n",
+    "            colWidths=[0.3,0.1,0.3], \\\n",
+    "            rowLabels=row_labels, \\\n",
+    "            loc = 1, \\\n",
+    "            rowLoc='left', \\\n",
+    "            fontsize = 15.)\n",
+    "\n",
+    "fig1.tight_layout()            "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ausgleichsbecken/dynamic_pipeline_pressure/pressure_conversion.py b/Ausgleichsbecken/dynamic_pipeline_pressure/pressure_conversion.py
new file mode 100644
index 0000000..96744c4
--- /dev/null
+++ b/Ausgleichsbecken/dynamic_pipeline_pressure/pressure_conversion.py
@@ -0,0 +1,77 @@
+# convert to Pa
+def bar_to_pa(p):
+    return p*1e5
+
+def mWS_to_pa(p):
+    return p*9.80665*1e3
+
+def torr_to_pa(p):
+    return p*133.322
+
+def atm_to_pa(p):
+    return p*101.325*1e3
+
+def psi_to_pa(p):
+    return p*6894.8
+
+# convert from Pa
+def pa_to_bar(p):
+    return p*1e-5
+
+def pa_to_mWS(p):
+    return p*1/(9.80665*1e3)
+
+def pa_to_torr(p):
+    return p/133.322
+
+def pa_to_atm(p):
+    return p*1/(101.325*1e3)
+
+ # converstion function
+
+def pa_to_psi(p):
+    return p/6894.8
+
+def pressure_conversion(pressure, input_unit = 'bar', target_unit = 'Pa'):
+    p = pressure
+    if input_unit.lower()   == 'bar':
+        p_pa = bar_to_pa(p)
+    elif input_unit.lower() == 'mws':
+        p_pa = mWS_to_pa(p)
+    elif input_unit.lower() == 'torr':
+        p_pa = torr_to_pa(p)
+    elif input_unit.lower() == 'atm':
+        p_pa = atm_to_pa(p)
+    elif input_unit.lower() == 'psi':
+        p_pa = psi_to_pa(p)
+    elif input_unit.lower() == 'pa':
+        p_pa = p
+    else:
+        raise Exception('Given input unit not recognised. \n Known units are: Pa, bar, mWs, Torr, atm, psi')
+    
+    if target_unit.lower()      == 'bar':
+        return pa_to_bar(p_pa), target_unit
+    elif target_unit.lower()    == 'mws':
+        return pa_to_mWS(p_pa), target_unit
+    elif target_unit.lower()    == 'torr':
+        return pa_to_torr(p_pa), target_unit
+    elif target_unit.lower()    == 'atm':
+        return pa_to_atm(p_pa), target_unit
+    elif target_unit.lower()    =='psi':
+        return pa_to_psi(p_pa), target_unit
+    elif target_unit.lower()    == 'pa':
+        return p_pa, target_unit
+    else:
+        raise Exception('Given target unit not recognised. \n Known units are: Pa, bar, mWs, Torr, atm, psi')
+
+# testing_pressure_conversion
+if __name__ == '__main__':
+    p = 1
+
+    unit_dict = ['Pa','Bar','Torr','Atm','MWS','psi']
+
+    for input_unit in unit_dict:
+        for target_unit in unit_dict:
+            converted_p = pressure_conversion(p,input_unit,target_unit)
+            print(input_unit,target_unit)
+            print(converted_p)
\ No newline at end of file
diff --git a/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken.py b/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken.py
new file mode 100644
index 0000000..e887617
--- /dev/null
+++ b/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken.py
@@ -0,0 +1,67 @@
+import numpy as np 
+
+def Volume_trend(influx, outflux, timestep=1, V_0=0):
+    '''
+    Returns the trend and the volume and the final volume,  defined
+    by influx and outflux patterns. The optional parameter timestep
+    defines the time increment over which the fluxes are changing.
+    '''
+    net_flux = influx-outflux
+    delta_V = net_flux*timestep
+    V_trend = V_0+np.cumsum(delta_V)
+    V_end = V_trend[-1]
+    return V_end,  V_trend
+
+def Height_trend(V_trend, area=1, h_crit_low=-np.inf, h_crit_high=np.inf):
+    '''
+    Returns the trend and the height and the final height,  defined
+    by influx and outflux patterns as well as the crosssection area.
+    The optional parameters h_crit_low/high indicate limits that the height
+    should never exceed. If this occures,  TRUE is returned in the corresponding
+    h_crit_flag.
+    '''
+    h_trend = V_trend/area
+    h_crit_flag_low = np.any(h_trend <= h_crit_low)
+    h_crit_flag_high = np.any(h_trend >= h_crit_high)
+    h_end = h_trend[-1]
+    return h_trend, h_end, h_crit_flag_low, h_crit_flag_high
+
+def get_h_halfstep(initial_height, influx, outflux, timestep, area):
+    h0      = initial_height
+    Q_in    = influx
+    Q_out   = outflux
+    dt      = timestep
+    A       = area
+
+    h_halfstep = h0+1/A*(Q_in-Q_out)*dt/2
+
+def get_p_halfstep(p0, p1):
+    p_halfstep = (p0+p1)/2
+
+def FODE_function(x, h, alpha, p, rho=1000., g=9.81):
+    f = x*abs(x)/h*alpha+g-p/(rho*h)
+    return f
+
+
+def e_RK_4(yn, h, dt, Q0, Q1, A0, A1, p0, p1):
+    alpha = (A1/A0-1)
+
+    h_hs = get_h_halfstep(h, Q0, Q1, dt, A0)
+    p_hs = get_p_halfstep(p0, p1)
+    Y1 = yn
+    Y2 = yn + dt/2*FODE_function(Y1, h, alpha, p0)
+    Y3 = yn + dt/2*FODE_function(Y2, h_hs, alpha, p_hs)
+    Y4 = yn + dt*FODE_function(Y3, h_hs, alpha, p_hs)
+    ynp1 = yn + dt/6*(FODE_function(Y1, h, alpha, p)+2*FODE_function(Y2, h_hs, alpha, p_hs)+ \
+        2*FODE_function(Y3, h_hs, alpha, p_hs)+ FODE_function(Y4, h, alpha, p))
+
+
+
+
+## testing
+# if __name__ == "__main__":
+#     influx = np.full([1, 100],  6)
+#     outflux = np.full_like(influx,  4)
+#     V_end,  V_trend = Volume_trend(influx,  outflux, timestep=0.5, V_0 = 100)
+#     print(V_end)
+#     print(V_trend)
diff --git a/Ausgleichsbecken_class_file.py b/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken_class_file.py
similarity index 98%
rename from Ausgleichsbecken_class_file.py
rename to Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken_class_file.py
index a6e2500..969dc04 100644
--- a/Ausgleichsbecken_class_file.py
+++ b/Ausgleichsbecken/static_pipeline_pressure/Ausgleichsbecken_class_file.py
@@ -1,5 +1,5 @@
 from Ausgleichsbecken import FODE_function, get_h_halfstep, get_p_halfstep
-from functions.pressure_conversion import pressure_conversion
+from pressure_conversion import pressure_conversion
 class Ausgleichsbecken_class:
 # units
     area_unit           = r'$\mathrm{m}^2$'
diff --git a/Main_Program.ipynb b/Ausgleichsbecken/static_pipeline_pressure/Main_Program.ipynb
similarity index 99%
rename from Main_Program.ipynb
rename to Ausgleichsbecken/static_pipeline_pressure/Main_Program.ipynb
index b266228..c9fd66c 100644
--- a/Main_Program.ipynb
+++ b/Ausgleichsbecken/static_pipeline_pressure/Main_Program.ipynb
@@ -2,19 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n",
     "import matplotlib.pyplot as plt\n",
-    "from functions.pressure_conversion import pressure_conversion"
+    "from pressure_conversion import pressure_conversion"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,13 +39,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c7ff9cd7c3ae4d21a32d833650fa73a3",
+       "model_id": "6a4020b97d834285b3b362c8b1f27e47",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/e-RK4-Test.ipynb b/Ausgleichsbecken/static_pipeline_pressure/e-RK4-Test.ipynb
similarity index 100%
rename from e-RK4-Test.ipynb
rename to Ausgleichsbecken/static_pipeline_pressure/e-RK4-Test.ipynb
diff --git a/Ausgleichsbecken/static_pipeline_pressure/pressure_conversion.py b/Ausgleichsbecken/static_pipeline_pressure/pressure_conversion.py
new file mode 100644
index 0000000..96744c4
--- /dev/null
+++ b/Ausgleichsbecken/static_pipeline_pressure/pressure_conversion.py
@@ -0,0 +1,77 @@
+# convert to Pa
+def bar_to_pa(p):
+    return p*1e5
+
+def mWS_to_pa(p):
+    return p*9.80665*1e3
+
+def torr_to_pa(p):
+    return p*133.322
+
+def atm_to_pa(p):
+    return p*101.325*1e3
+
+def psi_to_pa(p):
+    return p*6894.8
+
+# convert from Pa
+def pa_to_bar(p):
+    return p*1e-5
+
+def pa_to_mWS(p):
+    return p*1/(9.80665*1e3)
+
+def pa_to_torr(p):
+    return p/133.322
+
+def pa_to_atm(p):
+    return p*1/(101.325*1e3)
+
+ # converstion function
+
+def pa_to_psi(p):
+    return p/6894.8
+
+def pressure_conversion(pressure, input_unit = 'bar', target_unit = 'Pa'):
+    p = pressure
+    if input_unit.lower()   == 'bar':
+        p_pa = bar_to_pa(p)
+    elif input_unit.lower() == 'mws':
+        p_pa = mWS_to_pa(p)
+    elif input_unit.lower() == 'torr':
+        p_pa = torr_to_pa(p)
+    elif input_unit.lower() == 'atm':
+        p_pa = atm_to_pa(p)
+    elif input_unit.lower() == 'psi':
+        p_pa = psi_to_pa(p)
+    elif input_unit.lower() == 'pa':
+        p_pa = p
+    else:
+        raise Exception('Given input unit not recognised. \n Known units are: Pa, bar, mWs, Torr, atm, psi')
+    
+    if target_unit.lower()      == 'bar':
+        return pa_to_bar(p_pa), target_unit
+    elif target_unit.lower()    == 'mws':
+        return pa_to_mWS(p_pa), target_unit
+    elif target_unit.lower()    == 'torr':
+        return pa_to_torr(p_pa), target_unit
+    elif target_unit.lower()    == 'atm':
+        return pa_to_atm(p_pa), target_unit
+    elif target_unit.lower()    =='psi':
+        return pa_to_psi(p_pa), target_unit
+    elif target_unit.lower()    == 'pa':
+        return p_pa, target_unit
+    else:
+        raise Exception('Given target unit not recognised. \n Known units are: Pa, bar, mWs, Torr, atm, psi')
+
+# testing_pressure_conversion
+if __name__ == '__main__':
+    p = 1
+
+    unit_dict = ['Pa','Bar','Torr','Atm','MWS','psi']
+
+    for input_unit in unit_dict:
+        for target_unit in unit_dict:
+            converted_p = pressure_conversion(p,input_unit,target_unit)
+            print(input_unit,target_unit)
+            print(converted_p)
\ No newline at end of file
diff --git a/Druckrohrleitung.py b/Druckrohrleitung/Druckrohrleitung.py
similarity index 100%
rename from Druckrohrleitung.py
rename to Druckrohrleitung/Druckrohrleitung.py
diff --git a/Druckrohrleitung/Druckstoß.ipynb b/Druckrohrleitung/Druckstoß.ipynb
new file mode 100644
index 0000000..9fabf34
--- /dev/null
+++ b/Druckrohrleitung/Druckstoß.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#imports\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define constants\n",
+    "\n",
+    "g       = 9.81              # gravitational acceleration [m/s²]\n",
+    "\n",
+    "L       = 100              # length of pipeline [m]\n",
+    "rho     = 1000              # density of water [kg/m³]\n",
+    "D       = 1                 # pipe diameter \n",
+    "Q0      = 2                 # initial flow in whole pipe [m³/s]\n",
+    "h       = 20                # water level in upstream reservoir [m]\n",
+    "n       = 10                # number of pipe segments in discretization\n",
+    "nt      = 150              # number of time steps\n",
+    "f_coeff = 0.1              # lambda = 0.01 Friction loss coefficient [m]\n",
+    "c       = 400               # propagation velocity of the pressure wave\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preparing the discretization and initial conditions\n",
+    "\n",
+    "dx      = L/n                       # length of each pipe segment\n",
+    "dt      = dx/c                      # timestep according to method of characterisitics\n",
+    "nn      = n+1                       # number of nodes\n",
+    "pl_vec  = np.arange(0,nn*dx,dx)     # pl = pipe-length. position of the nodes on the pipeline\n",
+    "t_vec   = np.arange(0,(nt)*dt,dt) # time vector\n",
+    "\n",
+    "v0 = Q0/(D**2/4*np.pi)\n",
+    "p0 = (rho*g*h-v0**2*rho/2)\n",
+    "\n",
+    "# storage vectors for old parameters\n",
+    "v_old = np.full(nn,v0)\n",
+    "p_old = p0-(f_coeff*pl_vec/D*rho/2*v0**2)   # ref Wikipedia: Rohrreibungszahls\n",
+    "\n",
+    "# storage vectors for new parameters\n",
+    "v_new = np.zeros_like(v_old)\n",
+    "p_new = np.zeros_like(p_old)\n",
+    "\n",
+    "# storage vector for time evolution of parameters at node nn (at reservoir)\n",
+    "p_nn = np.zeros_like(t_vec)\n",
+    "v_nn = np.zeros_like(t_vec)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt\n",
+    "# time loop\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax1 = fig.add_subplot(111)\n",
+    "lo1, = ax1.plot(pl_vec,np.full_like(pl_vec,p0),marker='.')\n",
+    "ax1.set_ylim([-20*p0,20*p0])\n",
+    "\n",
+    "for it in range(nt):\n",
+    "    # set boundary conditions\n",
+    "    v_new[-1]   = 0       # in front of the instantaneously closing valve, the velocity is 0\n",
+    "    p_new[0]    = p0      # hydrostatic pressure from the reservoir\n",
+    "\n",
+    "    # calculate the new parameters at first and last node\n",
+    "    v_new[0]    = v_old[1]+1/(rho*c)*(p0-p_old[1])-f_coeff*dt/(2*D)*abs(v_old[1])*v_old[1]\n",
+    "    p_new[-1]   = p_old[-2]+rho*c*v_old[-2]-rho*c*f_coeff*dt/(2*D) *abs(v_old[-2])*v_old[-2]\n",
+    "\n",
+    "    # calculate parameters at second to second-to-last nodes \n",
+    "    #equation 2-30 plus 2-31 (and refactor for v_i^j+1) in block 2\n",
+    "\n",
+    "    for i in range(1,nn-1):\n",
+    "        v_new[i] = 0.5*(v_old[i-1]+v_old[i+1])+0.5/(rho*c)*(p_old[i-1]-p_old[i+1]) \\\n",
+    "            -f_coeff*dt/(4*D)*(abs(v_old[i-1])*v_old[i-1]+abs(v_old[i+1])*v_old[i+1])\n",
+    "\n",
+    "        p_new[i] = 0.5*rho*c*(v_old[i-1]-v_old[i+1])+0.5*(p_old[i-1]+p_old[i+1]) \\\n",
+    "            -rho*c*f_coeff*dt/(4*D)*(abs(v_old[i-1])*v_old[i-1]-abs(v_old[i+1])*v_old[i+1])\n",
+    "    \n",
+    "    lo1.set_xdata(pl_vec)\n",
+    "    lo1.set_ydata(p_new)\n",
+    "    ax1.set_title(str(t_vec[it]))\n",
+    "    fig.canvas.draw()\n",
+    "    plt.pause(0.05)\n",
+    "\n",
+    "    # store parameters of node 0 (at reservoir)\n",
+    "    p_nn[it] = p_old[-1]\n",
+    "    v_nn[it] = v_old[-1]\n",
+    "\n",
+    "    # prepare for next loop\n",
+    "    p_old = p_new\n",
+    "    v_old = v_new\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14c45f0bfd0>]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.plot(v_nn)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Durchflussraten.py b/Messing Around/Durchflussraten.py
similarity index 100%
rename from Durchflussraten.py
rename to Messing Around/Durchflussraten.py