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In [1]:
```# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *

Suppose you want to set the world record for the highest "bungee dunk", as shown in this video. Since the record is 70 m, let's design a jump for 80 m.

We'll make the following modeling assumptions:

Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.

Until the cord is fully extended, it applies no force to the jumper. It turns out this might not be a good assumption; we will revisit it.

After the cord is fully extended, it obeys Hooke's Law; that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.

The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.

Our objective is to choose the length of the cord, `L`

, and its spring constant, `k`

, so that the jumper falls all the way to the tea cup, but no farther!

First I'll create a `Param`

object to contain the quantities we'll need:

Let's assume that the jumper's mass is 75 kg.

With a terminal velocity of 60 m/s.

The length of the bungee cord is

`L = 40 m`

.The spring constant of the cord is

`k = 20 N / m`

when the cord is stretched, and 0 when it's compressed.

```
In [2]:
```m = UNITS.meter
s = UNITS.second
kg = UNITS.kilogram
N = UNITS.newton

```
In [3]:
```params = Params(y_attach = 80 * m,
v_init = 0 * m / s,
g = 9.8 * m/s**2,
mass = 75 * kg,
area = 1 * m**2,
rho = 1.2 * kg/m**3,
v_term = 60 * m / s,
L = 25 * m,
k = 40 * N / m,
zero_force = 0 * N)

Now here's a version of `make_system`

that takes a `Params`

object as a parameter.

`make_system`

uses the given value of `v_term`

to compute the drag coefficient `C_d`

.

```
In [4]:
```def make_system(params):
"""Makes a System object for the given params.
params: Params object
returns: System object
"""
area, mass = params.area, params.mass
g, rho = params.g, params.rho
v_init, v_term = params.v_init, params.v_term
y_attach = params.y_attach
C_d = 2 * mass * g / (rho * area * v_term**2)
init = State(y=y_attach, v=v_init)
t_end = 20 * s
return System(params, C_d=C_d,
init=init, t_end=t_end)

Let's make a `System`

```
In [5]:
```system = make_system(params)

`spring_force`

computes the force of the cord on the jumper.

If the spring is not extended, it returns `zero_force`

, which is either 0 Newtons or 0, depending on whether the `System`

object has units. I did that so the slope function works correctly with and without units.

```
In [6]:
```def spring_force(y, system):
"""Computes the force of the bungee cord on the jumper:
y: height of the jumper
Uses these variables from system|
y_attach: height of the attachment point
L: resting length of the cord
k: spring constant of the cord
returns: force in N
"""
y_attach, L, k = system.y_attach, system.L, system.k
distance_fallen = y_attach - y
if distance_fallen <= L:
return system.zero_force
extension = distance_fallen - L
f_spring = k * extension
return f_spring

```
In [7]:
```spring_force(80*m, system)

```
In [8]:
```spring_force(55*m, system)

```
In [9]:
```spring_force(54*m, system)

`drag_force`

computes drag as a function of velocity:

```
In [10]:
```def drag_force(v, system):
"""Computes drag force in the opposite direction of `v`.
v: velocity
system: System object
returns: drag force
"""
rho, C_d, area = system.rho, system.C_d, system.area
f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2
return f_drag

Here's the drag force at 60 meters per second.

```
In [11]:
```v = -60 * m/s
f_drag = drag_force(v, system)

```
In [12]:
```a_drag = f_drag / system.mass

Now here's the slope function:

```
In [13]:
```def slope_func(state, t, system):
"""Compute derivatives of the state.
state: position, velocity
t: time
system: System object containing g, rho,
C_d, area, and mass
returns: derivatives of y and v
"""
y, v = state
mass, g = system.mass, system.g
a_drag = drag_force(v, system) / mass
a_spring = spring_force(y, system) / mass
dvdt = -g + a_drag + a_spring
return v, dvdt

As always, let's test the slope function with the initial params.

```
In [14]:
```slope_func(system.init, 0, system)

And then run the simulation.

```
In [15]:
```results, details = run_ode_solver(system, slope_func)
details

Here's the plot of position as a function of time.

```
In [16]:
```def plot_position(results):
plot(results.y)
decorate(xlabel='Time (s)',
ylabel='Position (m)')

```
In [17]:
```plot_position(results)

After reaching the lowest point, the jumper springs back almost to almost 70 m, and oscillates several times. That looks like more osciallation that we expect from an actual jump, which suggests that there some dissipation of energy in the real world that is not captured in our model. To improve the model, that might be a good thing to investigate.

But since we are primarily interested in the initial descent, the model might be good enough for now.

We can use `min`

to find the lowest point:

```
In [18]:
```min(results.y)

At the lowest point, the jumper is still too high, so we'll need to increase `L`

or decrease `k`

.

Here's velocity as a function of time:

```
In [19]:
```def plot_velocity(results):
plot(results.v, color='C1', label='v')
decorate(xlabel='Time (s)',
ylabel='Velocity (m/s)')

```
In [20]:
```plot_velocity(results)

Although we compute acceleration inside the slope function, we don't get acceleration as a result from `run_ode_solver`

.

We can approximate it by computing the numerical derivative of `ys`

:

```
In [21]:
```a = gradient(results.v)
plot(a)
decorate(xlabel='Time (s)',
ylabel='Acceleration (m/$s^2$)')

And we can compute the maximum acceleration the jumper experiences:

```
In [22]:
```max_acceleration = max(a) * m/s**2

Relative to the acceleration of gravity, the jumper "pulls" about "1.7 g's".

```
In [23]:
```max_acceleration / system.g

```
In [24]:
```source_code(gradient)

The metric we are interested in is the lowest point of the first oscillation. For both efficiency and accuracy, it is better to stop the simulation when we reach this point, rather than run past it and the compute the minimum.

Here's an event function that stops the simulation when velocity is 0.

```
In [25]:
```def event_func(state, t, system):
"""Return velocity.
"""
y, v = state
return v

As usual, we should test it with the initial conditions.

```
In [26]:
```event_func(system.init, 0, system)

Now we can test it and confirm that it stops at the bottom of the jump.

```
In [27]:
```results, details = run_ode_solver(system, slope_func, events=event_func)
plot_position(results)

```
In [28]:
```min(results.y)

**Exercise:** Write an error function that takes `L`

and `params`

as arguments, simulates a bungee jump, and returns the lowest point.

Test the error function with a guess of 25 m and confirm that the return value is about 5 meters.

Use `root_scalar`

with your error function to find the value of `L`

that yields a perfect bungee dunk. Hint: before calling `root_scalar`

, make a version of `params`

with no dimensions.

Run a simulation with the result from `root_scalar`

and confirm that it works.

```
In [29]:
``````
# Solution goes here
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