# Chapter 20¶

Click here to run this chapter on Colab

So far the differential equations we’ve worked with have been **first
order**, which means they involve only first derivatives. In this
chapter, we turn our attention to second order differential equations, which can involve both first and second derivatives.

We’ll revisit the falling penny example from Chapter 1, and use `run_solve_ivp`

to find the position and velocity of the penny as it falls, with and without air resistance.

## Newton’s Second Law of Motion¶

First order differential equations (DEs) can be written

where \(G\) is some function of \(x\) and \(y\) (see http://modsimpy.com/ode). Second order DEs can be written

where \(H\) is a function of \(x\), \(y\), and \(dy/dx\).

In this chapter, we will work with one of the most famous and useful second order DEs, Newton’s second law of motion:

where \(F\) is a force or the total of a set of forces, \(m\) is the mass of a moving object, and \(a\) is its acceleration.

Newton’s law might not look like a differential equation, until we realize that acceleration, \(a\), is the second derivative of position, \(y\), with respect to time, \(t\). With the substitution

Newton’s law can be written

And that’s definitely a second order DE. In general, \(F\) can be a function of time, position, and velocity.

Of course, this “law” is really a model in the sense that it is a simplification of the real world. Although it is often approximately true:

It only applies if \(m\) is constant. If mass depends on time, position, or velocity, we have to use a more general form of Newton’s law (see http://modsimpy.com/varmass).

It is not a good model for very small things, which are better described by another model, quantum mechanics.

And it is not a good model for things moving very fast, which are better described by yet another model, relativistic mechanics.

However, for medium-sized things with constant mass, moving at medium-sized speeds, Newton’s model is extremely useful. If we can quantify the forces that act on such an object, we can predict how it will move.

## Dropping Pennies¶

As a first example, let’s get back to the penny falling from the Empire State Building, which we considered in Chapter 1. We will implement two models of this system: first without air resistance, then with.

Given that the Empire State Building is 381 m high, and assuming that the penny is dropped from a standstill, the initial conditions are:

```
init = State(y=381, v=0)
```

where `y`

is height above the sidewalk and `v`

is velocity.

I’ll put the initial conditions in a `System`

object, along with the magnitude of acceleration due to gravity, `g`

, and the duration of the simulations, `t_end`

.

```
system = System(init=init,
g=9.8,
t_end=10)
```

Now we need a slope function, and here’s where things get tricky. As we have seen, `run_solve_ivp`

can solve systems of first order DEs, but Newton’s law is a second order DE. However, if we recognize that

Velocity, \(v\), is the derivative of position, \(dy/dt\), and

Acceleration, \(a\), is the derivative of velocity, \(dv/dt\),

we can rewrite Newton’s law as a system of first order ODEs:

And we can translate those equations into a slope function:

```
def slope_func(t, state, system):
y, v = state
dydt = v
dvdt = -system.g
return dydt, dvdt
```

As usual, the parameters are a time stamp, a `State`

object, and a `System`

object.

The first line unpacks the state variables, `y`

and `v`

.

The next two lines compute the derivatives of the state variables, `dydt`

and `dvdt`

.
The derivative of position is velocity, and the derivative of velocity is acceleration.
In this case, \(a = -g\), which indicates that acceleration due to gravity is in the direction of decreasing \(y\).

`slope_func`

returns a sequence containing the two derivatives.

Before calling `run_solve_ivp`

, it is a good idea to test the slope
function with the initial conditions:

```
dydt, dvdt = slope_func(0, system.init, system)
dydt, dvdt
```

```
(0, -9.8)
```

The result is 0 m/s for velocity and -9.8 m/s\(^2\) for acceleration.

Now we call `run_solve_ivp`

like this:

```
results, details = run_solve_ivp(system, slope_func)
details.message
```

```
'The solver successfully reached the end of the integration interval.'
```

`results`

is a `TimeFrame`

with two columns: `y`

contains the height of the penny; `v`

contains its velocity.
Here are the first few rows.

```
results.head()
```

y | v | |
---|---|---|

0.0 | 381.000 | 0.00 |

0.1 | 380.951 | -0.98 |

0.2 | 380.804 | -1.96 |

0.3 | 380.559 | -2.94 |

0.4 | 380.216 | -3.92 |

We can plot the results like this:

```
results.y.plot()
decorate(xlabel='Time (s)',
ylabel='Position (m)')
```

Since acceleration is constant, velocity increases linearly and position decreases quadratically; as a result, the height curve is a parabola.

The last value of `results.y`

is negative, which means we ran the simulation too long.

```
results.iloc[-1].y
```

```
-108.99999999999983
```

One way to solve this problem is to use the results to estimate the time when the penny hits the sidewalk.

The ModSim library provides `crossings`

, which takes a `TimeSeries`

and a value, and returns a sequence of times when the series passes through the value. We can find the time when the height of the penny is `0`

like this:

```
t_crossings = crossings(results.y, 0)
t_crossings
```

```
array([8.81788535])
```

The result is an array with a single value, 8.818 s. Now, we could run
the simulation again with `t_end = 8.818`

, but there’s a better way.

## Events¶

As an option, `run_solve_ivp`

can take an *event function*, which
detects an “event”, like the penny hitting the sidewalk, and ends the
simulation.

Event functions take the same parameters as slope functions, `t`

, `state`

, and `system`

. They should return a value that passes through `0`

when the event occurs. Here’s an event function that detects the penny hitting the sidewalk:

```
def event_func(t, state, system):
y, v = state
return y
```

The return value is the height of the penny, `y`

, which passes through
`0`

when the penny hits the sidewalk.

We pass the event function to `run_solve_ivp`

like this:

```
results, details = run_solve_ivp(system, slope_func,
events=event_func)
details.message
```

```
'A termination event occurred.'
```

Then we can get the flight time and final velocity like this:

```
t_end = results.index[-1]
t_end
```

```
8.8
```

```
y, v = results.iloc[-1]
y, v
```

```
(0.0, -86.41527642726145)
```

If there were no air resistance, the penny would hit the sidewalk (or someone’s head) at about 86 m/s. So it’s a good thing there is air resistance.

## Summary¶

In this chapter, we wrote Newton’s second law, which is a second order DE, as a system of first order DEs.
Then we used `run_solve_ivp`

to simulate a penny dropping from the Empire State Building in the absence of air resistance.
And we used an event function to stop the simulation when the penny reaches the sidewalk.

In the next chapter we’ll add air resistance to the model. But first you might want to work on this exercise.

### Exercises¶

### Exercise¶

Here’s a question from the web site Ask an Astronomer:

“If the Earth suddenly stopped orbiting the Sun, I know eventually it would be pulled in by the Sun’s gravity and hit it. How long would it take the Earth to hit the Sun? I imagine it would go slowly at first and then pick up speed.”

Use `run_solve_ivp`

to answer this question.

Here are some suggestions about how to proceed:

Look up the Law of Universal Gravitation and any constants you need. I suggest you work entirely in SI units: meters, kilograms, and Newtons.

When the distance between the Earth and the Sun gets small, this system behaves badly, so you should use an event function to stop when the surface of Earth reaches the surface of the Sun.

Express your answer in days, and plot the results as millions of kilometers versus days.

If you read the reply by Dave Rothstein, you will see other ways to solve the problem, and a good discussion of the modeling decisions behind them.

You might also be interested to know that it’s actually not that easy to get to the Sun.

```
# Solution
r_0 = 150e9 # 150 million km in m
v_0 = 0
init = State(r=r_0,
v=v_0)
```

```
# Solution
radius_earth = 6.37e6 # meters
radius_sun = 696e6 # meters
r_final = radius_sun + radius_earth
r_final
```

```
702370000.0
```

```
# Solution
t_end = 1e7 # seconds
system = System(init=init,
G=6.674e-11, # N m^2 / kg^2
m1=1.989e30, # kg
m2=5.972e24, # kg
r_final=radius_sun + radius_earth,
t_end=t_end)
```

```
# Solution
def universal_gravitation(state, system):
"""Computes gravitational force.
state: State object with distance r
system: System object with m1, m2, and G
"""
r, v = state
G, m1, m2 = system.G, system.m1, system.m2
force = G * m1 * m2 / r**2
return force
```

```
# Solution
universal_gravitation(init, system)
```

```
3.5233701151999997e+22
```

```
# Solution
def slope_func(t, state, system):
y, v = state
m2 = system.m2
force = universal_gravitation(state, system)
dydt = v
dvdt = -force / m2
return dydt, dvdt
```

```
# Solution
slope_func(0, system.init, system)
```

```
(0.0, -0.005899815999999999)
```

```
# Solution
def event_func(t, state, system):
r, v = state
return r - system.r_final
```

```
# Solution
event_func(0, init, system)
```

```
149297630000.0
```

```
# Solution
results, details = run_solve_ivp(system, slope_func,
events=event_func)
details.message
```

```
message: 'A termination event occurred.'
nfev: 188
njev: 0
nlu: 0
sol: None
status: 1
success: True
t_events: [array([5599828.17941854])]
y_events: [array([[ 7.02370000e+08, -6.13362867e+05]])]
```

```
# Solution
t_event = results.index[-1]
t_event
```

```
5400000.0
```

```
# Solution
seconds = t_event * units.second
days = seconds.to(units.day)
```

```
# Solution
results.index /= 60 * 60 * 24
```

```
# Solution
results.r /= 1e9
```

```
# Solution
results.r.plot(label='r')
decorate(xlabel='Time (day)',
ylabel='Distance from sun (million km)')
```