by Jeffrey Kantor (jeff at nd.edu). The latest version of this notebook is available at https://github.com/jckantor/CBE30338.

This notebook demonstrates the modeling and interactive simulation of a u-tube manometer. This device demonstrates a variety of behaviors exhibited by a linear second order system. An interesting aspect of the problem is the opportunity for passive design of dynamics for a measurement device.

- Model 1. Steady State Response to a Pressure Differential
- Model 2. Dynamic Response with Negligible Viscosity
- Model 3. Dynamic Response with Viscous Dissipation
- Model 4. Second Order System in Standard Form
- Model 5. Dynamic Response to Sinusoidal Input
- Model 6. State Space Representation

- Develop linear differential equations models for mechanical systems from momentum/force balances.
- Describe role of position and velocity as state variables in a dynamic model.
- Describe undamped, underdamped, overdamped, and critically damped responses.
- Represent a second order system in standard form with natural frequency and damping factor.
- Describe second order response to sinusoidal input, and resonance.
- Construct a state space representation of a second order linear differential equation.

In [1]:

```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy import linalg as la
from ipywidgets import interact,interactive
from control.matlab import *
# scales for all subsequent plots
tmax = 20
ymin = -0.02
ymax = +0.02
axis = [0.0,tmax,ymin,ymax]
t = np.linspace(0.0,tmax,1000)
# physical properties
g = 9.8 # m/s
rho = 1000.0 # density of water kg/m^3
nu = 1.0e-6 # kinematic viscosity of water m/s^2
# system dimensions
L = 7 # meters
d = 0.08 # meters
```

For this first model we will that the ends of the u-tube are exposed to a pressure differential $\Delta P$. How does the level in the tubes change?

The u-tube manometer of cross-sectional area $A$, filled with a liquid of density $\rho$, the total length of the liquid column $L$. When the ends are open and exposed to the same environmental pressure $P$ the liquid levels in the two the legs of the device will reach the same level. We'll measure the levels in the tubes as a deviation $y$ from this equilibrium position.

At steady state the difference in the levels of the tubes will be $h$. The static pressure difference

$$\Delta P = \rho g h$$or

$$y = \frac{\Delta P}{\rho g}$$This is simple statics. Notice that neither the cross-sectional area or the length of the liquid column matter. This is the rationale behind the common water level.

By Bd at the German language Wikipedia, CC BY-SA 3.0, Link

In [2]:

```
def model1(deltaP = 100.0):
h = deltaP/(rho*g)
plt.axis(axis)
plt.plot(plt.xlim(),[h,h])
plt.grid()
plt.xlabel('Time [sec]')
plt.ylabel('h [m]')
plt.title('dP = {0:5.1f} Pascals'.format(deltaP))
interact(model1,deltaP=(-200,200,20.0));
```

The second model for the manometer includes the dynamics associated with moving a mass $m$ of the liquid column held within the manometer. For this model we will chose a different measure of displacem

The net force on the liquid column is due to the applied pressure differential, $A\Delta P$, and the gravitational force due to the difference in liquid levels between the two arms of the manometer, $2 A \rho g$. $A$ is the cross-sectional area. From Newton's law

$$m \frac{d^2y}{dt^2} = A \Delta P - 2 A \rho g y$$The mass of liquid is $m = \rho L A$ where $L$ is the total length of the liquid column. After canceling a common factor $A$, the result is an inhomogeneous linear second order differential equation

$$ \frac{d^2y}{dt^2} + \frac{2 g}{L} y = \frac{1}{\rho L} \Delta P$$At steady state this model reduces to the static case outlined in model 1 above. The dynamic case corresponds to an undamped harmonic oscillator with an angular frequency

$$\omega = \sqrt{\frac{2 g}{L}}$$For numerical solution using the scipy libraries, it is necessary to convert the second order differential equation to a system of first order differential equations.

$$\begin{align*} \frac{dy}{dt} & = v \\ \frac{dv}{dt} & = -\frac{2g}{L} y + \frac{1}{\rho L} \Delta P \end{align*}$$In [3]:

```
def model2(deltaP=100, L = 7.0):
def deriv(X,t):
x,v = X
xdot = v
vdot = -(2*g/L)*x + deltaP/rho/L
return [xdot,vdot]
IC = [0,0]
w = np.sqrt(2*g/L)
print(" natural frequency = {0:0.1f} rad/sec".format(w))
print("period of oscillation = {0:0.1f} seconds".format(2*np.pi/w))
sol = odeint(deriv,IC,t)
plt.axis(axis)
plt.plot(t,sol)
plt.grid()
plt.xlabel('Time [sec]')
plt.ylabel('y [m], v[m/s]')
plt.title('dP = {0:5.1f} Pascals, L = {1:4.2f} meters'.format(deltaP,L))
plt.legend(['Position','Velocity'])
interact(model2, deltaP = (-200,200,1), L = (0.2,10,0.1));
```

This third model for manometer incorporates the energy loss due to viscous dissipation in fluid motion. The pressure drop due to the laminar flow of incompressible Newtonian fluid in a long pipe with circular cross-section is given by the Hagen-Poiseuille equation

$$\Delta P_{drag} = \frac{32 \mu L v}{d^2}$$where $\mu$ is the dynamic viscosity and $d$ is pipe diameter. Doing a balance of forces acting on the fluid column

$$\rho AL\frac{d^2y}{dt^2} + \frac{32\mu L A}{d^2}v + 2 A \rho g y = A \Delta P$$Denoting $\nu = \frac{\mu}{\rho}$ as the kinematic viscosity, substituting for velocity $\frac{dy}{dt} = v$ leaves

$$\frac{d^2y}{dt^2} + \frac{32 \nu }{d^2}\frac{dy}{dt} + \frac{2g}{L} y = \frac{1}{\rho L} \Delta P$$This can be recast as a pair of first-order linear differential equations

$$\begin{align*} \frac{dy}{dt} & = v \\ \frac{dv}{dt} & = -\frac{2g}{L} y - \frac{32 \nu }{d^2}v + \frac{1}{\rho L} \Delta P \end{align*}$$In [4]:

```
def model3(dP = 100.0, L = 7.0, d = 0.008):
def deriv(X,t):
y,v = X
ydot = v
vdot = -(2*g/L)*y - (32*nu/d**2)*v + dP/rho/L
return [ydot,vdot]
IC = [0,0]
sol = odeint(deriv,IC,t)
plt.axis(axis)
plt.plot(t,sol)
plt.grid()
plt.xlabel('Time [sec]')
plt.ylabel('y [m], v[m/s]')
plt.title('dP = {0:5.1f} bars, L = {1:4.2f} meters, d = {2:5.3f} meters'.format(dP,L,d))
plt.legend(['Position','Velocity'])
w = interactive(model3, dP=(-200,200,20), L = (0.2,30,0.1), d=(0.001,0.020,0.001));
w.children[2].readout_format = '.3f'
w
```

Standard form of a damped second order system is

$$\tau^2\frac{d^2y}{dt^2} + 2\zeta\tau\frac{dy}{dt} + y = K u(t)$$Examples include buildings, car suspensions, other structures. Starting with the model equation

$$\frac{d^2y}{dt^2} + \frac{32 \nu }{d^2}\frac{dy}{dt} + \frac{2g}{L} y = \frac{1}{\rho L} \Delta P$$The first step is to normalize the zeroth order term in $y$ and compare to the second-order model in standard form

$$\underbrace{\frac{L}{2g}}_{\tau^2}\frac{d^2y}{dt^2} + \underbrace{\frac{16 \nu L}{g d^2}}_{2\zeta\tau}\frac{dy}{dt} + y = \underbrace{\frac{1}{2\rho g}}_K \underbrace{\Delta P}_{u(t)}$$Solving for the coefficients in standard form

$$\begin{align*} K & = \frac{1}{2\rho g}\\ \tau & = \sqrt{\frac{L}{2g}} \\ \zeta & = \frac{8\nu}{d^2}\sqrt{\frac{2L}{g}} \end{align*}$$In [5]:

```
K = 1/2/rho/g
tau = np.sqrt(L/2/g)
zeta = (8*nu/d**2)*np.sqrt(2*L/g)
print(K,tau,zeta)
dcritical = (128*nu*nu*L/g)**0.25
print(dcritical)
```

In [6]:

```
def model4(dP=100.0, L=1.0, d=0.10, freq=0.5):
def deriv(X,t):
x,v = X
xdot = v
vdot = -(2*g/L)*x - (32*nu/d**2)*v + dP*np.sin(2.0*np.pi*freq*t)/rho/L
return [xdot,vdot]
IC = [0,0]
sol = odeint(deriv,IC,t)
plt.axis(axis)
plt.plot(t,sol[:,1])
plt.plot(t,dP*np.sin(2.0*np.pi*freq*t)/10000)
plt.grid()
plt.xlabel('Time [sec]')
plt.ylabel('y [m], P[bars/10000]')
plt.title('dP = {0:5.1f} bars, L = {1:4.2f} meters, d = {2:5.3f} meters'.format(dP,L,d))
plt.legend(['Position','Pressure/10000'])
interact(model4, dP=(-200,200,20), L = (0.2,5,0.1), d=(0.01,0.20,0.002), freq=(0,4,0.01));
```

State space models are widely used in textbooks, software, and the research literature to represent linear systems. It's a generic model that represents a system with inputs and outputs. Here's how to recast out manometer model is time-varying pressure as a state model where the liquid level is the measured output.

Start with the model written as a differential equation

$$\frac{d^2y}{dt^2} + \frac{32\nu}{d^2}\frac{dy}{dt} + \frac{2g}{L} y = \frac{1}{\rho L} \Delta P$$Assemble the dependent variables in a vector, and rewrite using matrix/vector operations.

$$\begin{align*} \frac{d}{dt} \left[\begin{array}{c}y \\ v\end{array}\right] & = \left[\begin{array}{cc}0 & 1 \\ - \frac{2g}{L} & -\frac{32\nu}{d^2} \end{array}\right] \left[\begin{array}{c}y \\ v\end{array}\right] + \left[\begin{array}{c}0 \\ \frac{1}{\rho L}\end{array}\right] \left[\Delta P\right] \\ \left[y\right] & = \left[\begin{array}{c} 1 & 0\end{array}\right] \left[\begin{array}{c}y \\ v\end{array}\right] + \left[0\right] \left[\Delta P\right] \end{align*} $$Use standard symbols to label the vectors and matrices.

$$\begin{align*} \frac{d}{dt} \underbrace{\left[\begin{array}{c}y \\ v\end{array}\right]}_{x} & = \underbrace{\left[\begin{array}{cc}0 & 1 \\ - \frac{2g}{L} & -\frac{32\nu}{d^2} \end{array}\right]}_{A} \underbrace{\left[\begin{array}{c}y \\ v\end{array}\right]}_{x} + \underbrace{\left[\begin{array}{c}0 \\ \frac{1}{\rho L}\end{array}\right]}_{B} \underbrace{\left[\Delta P\right]}_{u} \\ \underbrace{\left[y\right]}_{y} & = \underbrace{\left[\begin{array}{c} 1 & 0\end{array}\right]}_{C} \underbrace{\left[\begin{array}{c}y \\ v\end{array}\right]}_{x} + \underbrace{\left[0\right]}_{D} \underbrace{\left[\Delta P\right]}_{u} \end{align*} $$The result is a model of a linear system in a standard state space representation.

$$\begin{align*} \frac{dx}{dt} & = Ax + Bu \\ y & = Cx + Du \end{align*}$$In [7]:

```
def model6(dP=100, L=1.0, d=0.10):
A = [[0,1],[-2*g/L, -32*nu/(d**2)]]
B = [[0],[1/rho/L]]
C = [[1,0]]
D = [[0]]
sys = ss(A,B,C,D)
y,tout = step(sys,t);
plt.axis(axis)
plt.plot(t,dP*y)
plt.grid()
plt.xlabel('Time [sec]')
plt.ylabel('y [m]')
plt.title('dP = {0:5.1f} bars, L = {1:4.2f} meters, d = {2:5.3f} meters'.format(dP,L,d))
plt.legend(['Position'])
interact(model6, dP=(-200,200,1), L = (0.2,5,0.1), d=(0.01,0.20,0.002));
```

In [11]:

```
w = np.logspace(0,1,200)
def model6(L=1.0, d=0.10):
A = [[0,1],[-2*g/L, -32*nu/(d**2)]]
B = [[0],[1/rho/L]]
C = [[1,0]]
D = [[0]]
mano = ss(A,B,C,D)
bode(mano,w);
interact(model6, L = (0.2,5,0.1), d=(0.01,0.20,0.002));
```

In [9]:

```
w = np.logspace(0,1,200)
def model6(L=1.0, d=0.10):
A = [[0,1],[-2*g/L, -128*nu/(np.pi*d**4)]]
B = [[0],[1/rho/L]]
C = [[1,0]]
D = [[0]]
e_vals,e_vecs = la.eig(A)
plt.axis([-5,5,-5,5])
plt.axis('equal')
plt.plot(e_vals.real,e_vals.imag,'o')
interact(model6, L = (0.2,5,0.1), d=(0.01,0.20,0.002));
```

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