In classical mechanics, a double pendulum is a pendulum attached to the end of another pendulum. Its equations of motion are often written using the Lagrangian formulation of mechanics and solved numerically, which is the approach taken here. The dynamics of the double pendulum are chaotic and complex, as illustrated below.

The code:

```
import sys
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# Pendulum rod lengths (m), bob masses (kg).
L1, L2 = 1, 1
m1, m2 = 1, 1
# The gravitational acceleration (m.s-2).
g = 9.81
def deriv(y, t, L1, L2, m1, m2):
"""Return the first derivatives of y = theta1, z1, theta2, z2."""
theta1, z1, theta2, z2 = y
c, s = np.cos(theta1-theta2), np.sin(theta1-theta2)
theta1dot = z1
z1dot = (m2*g*np.sin(theta2)*c - m2*s*(L1*z1**2*c + L2*z2**2) -
(m1+m2)*g*np.sin(theta1)) / L1 / (m1 + m2*s**2)
theta2dot = z2
z2dot = ((m1+m2)*(L1*z1**2*s - g*np.sin(theta2) + g*np.sin(theta1)*c) +
m2*L2*z2**2*s*c) / L2 / (m1 + m2*s**2)
return theta1dot, z1dot, theta2dot, z2dot
def calc_E(y):
"""Return the total energy of the system."""
th1, th1d, th2, th2d = y.T
V = -(m1+m2)*L1*g*np.cos(th1) - m2*L2*g*np.cos(th2)
T = 0.5*m1*(L1*th1d)**2 + 0.5*m2*((L1*th1d)**2 + (L2*th2d)**2 +
2*L1*L2*th1d*th2d*np.cos(th1-th2))
return T + V
# Maximum time, time point spacings and the time grid (all in s).
tmax, dt = 30, 0.01
t = np.arange(0, tmax+dt, dt)
# Initial conditions: theta1, dtheta1/dt, theta2, dtheta2/dt.
y0 = np.array([3*np.pi/7, 0, 3*np.pi/4, 0])
# Do the numerical integration of the equations of motion
y = odeint(deriv, y0, t, args=(L1, L2, m1, m2))
# Check that the calculation conserves total energy to within some tolerance.
EDRIFT = 0.05
# Total energy from the initial conditions
E = calc_E(y0)
if np.max(np.sum(np.abs(calc_E(y) - E))) > EDRIFT:
sys.exit('Maximum energy drift of {} exceeded.'.format(EDRIFT))
# Unpack z and theta as a function of time
theta1, theta2 = y[:,0], y[:,2]
# Convert to Cartesian coordinates of the two bob positions.
x1 = L1 * np.sin(theta1)
y1 = -L1 * np.cos(theta1)
x2 = x1 + L2 * np.sin(theta2)
y2 = y1 - L2 * np.cos(theta2)
# Plotted bob circle radius
r = 0.05
# Plot a trail of the m2 bob's position for the last trail_secs seconds.
trail_secs = 1
# This corresponds to max_trail time points.
max_trail = int(trail_secs / dt)
def make_plot(i):
# Plot and save an image of the double pendulum configuration for time
# point i.
# The pendulum rods.
ax.plot([0, x1[i], x2[i]], [0, y1[i], y2[i]], lw=2, c='k')
# Circles representing the anchor point of rod 1, and bobs 1 and 2.
c0 = Circle((0, 0), r/2, fc='k', zorder=10)
c1 = Circle((x1[i], y1[i]), r, fc='b', ec='b', zorder=10)
c2 = Circle((x2[i], y2[i]), r, fc='r', ec='r', zorder=10)
ax.add_patch(c0)
ax.add_patch(c1)
ax.add_patch(c2)
# The trail will be divided into ns segments and plotted as a fading line.
ns = 20
s = max_trail // ns
for j in range(ns):
imin = i - (ns-j)*s
if imin < 0:
continue
imax = imin + s + 1
# The fading looks better if we square the fractional length along the
# trail.
alpha = (j/ns)**2
ax.plot(x2[imin:imax], y2[imin:imax], c='r', solid_capstyle='butt',
lw=2, alpha=alpha)
# Centre the image on the fixed anchor point, and ensure the axes are equal
ax.set_xlim(-L1-L2-r, L1+L2+r)
ax.set_ylim(-L1-L2-r, L1+L2+r)
ax.set_aspect('equal', adjustable='box')
plt.axis('off')
plt.savefig('frames/_img{:04d}.png'.format(i//di), dpi=72)
plt.cla()
# Make an image every di time points, corresponding to a frame rate of fps
# frames per second.
# Frame rate, s-1
fps = 10
di = int(1/fps/dt)
fig = plt.figure(figsize=(8.3333, 6.25), dpi=72)
ax = fig.add_subplot(111)
for i in range(0, t.size, di):
print(i // di, '/', t.size // di)
make_plot(i)
```

The images are saved to a subdirectory, `frames/`

and can be converted into an animated gif, for example with ImageMagick's `convert`

utility.

The derivation of the double pendulum equations of motion using the Lagrangian formulation has become a standard exercise in introductory classical mechanics, but an outline is given below. There are many, many similar derivations on the internet. We use the following coordinate system:

The two degrees of freedom are taken to be $\theta_1$ and $\theta_2$, the angle of each pendulum rod from the vertical. The components of the bob positions and velocities are

\begin{align*}
x_1 & = l_1\sin\theta_1 & \dot{x}_1 &= l_1\dot{\theta}_1\cos\theta_1\\
y_1 & = -l_1\cos\theta_1 & \dot{y}_1 &= l_1\dot{\theta}_1\sin\theta_1\\
x_2 & = l_1\sin\theta_1 + l_2\sin\theta_2 & \dot{x}_2 &= l_1\dot{\theta}_1\cos\theta_1 + l_2\dot{\theta}_2\cos\theta_2\\
y_2 & = -l_1\cos\theta_1 - l_2\cos\theta_2 & \dot{y}_2 &= l_1\dot{\theta}_1\sin\theta_1 + l_2\dot{\theta}_2\sin\theta_2
\end{align*}

The potential and kinetic energies are then

\begin{align*}
V &= m_1gy_1 + m_2gy_2 = -(m_1 + m_2)l_1g\cos\theta_1 - m_2l_2g\cos\theta_2\\
T &= \tfrac{1}{2}m_1v_1^2 + \tfrac{1}{2}m_2v_2^2 = \tfrac{1}{2}m_1(\dot{x}_1^2 + \dot{y}_1^2) + \tfrac{1}{2}m_2(\dot{x}_2^2 + \dot{y}_2^2)\\
&= \tfrac{1}{2}m_1l_1^2\dot{\theta}_1^2 + \tfrac{1}{2}m_2[l_1^2\dot{\theta}_1^2 + l_2^2\dot{\theta}_2^2 + 2l_1l_2\dot{\theta}_1\dot{\theta}_2\cos(\theta_1 - \theta_2)]
\end{align*}

The Lagrangian, $\mathcal{L} = T - V$ is therefore:

\begin{align*}
\mathcal{L} = \tfrac{1}{2}(m_1+m_2)l_1^2\dot{\theta}_1^2 + \tfrac{1}{2}m_2l_2^2\dot{\theta}_2^2 + m_1l_1l_2\dot{\theta}_1\dot{\theta}_2\cos(\theta_1 - \theta_2) + (m_1+m_2)l_1g\cos\theta_1 + m_2gl_2\cos\theta_2.
\end{align*}

The Euler-Lagrange equations are:

\begin{align*}
\frac{\mathrm{d}}{\mathrm{d}t}\left(\frac{\partial\mathcal{L}}{\partial \dot{q}_i}\right) - \frac{\partial \mathcal{L}}{\partial q_i} = 0 \quad \mathrm{for}\;q_i = \theta_1, \theta_2.
\end{align*}

For these coordinates, after some calculus and algebra, we get:

\begin{align*}
(m_1 + m_2)l_1 \ddot{\theta}_1 + m_2l_2\ddot{\theta}_2\cos(\theta_1 - \theta_2) + m_2l_2\dot{\theta}_2^2\sin(\theta_1 - \theta_2) + (m_1+m_2)g\sin\theta_1 &= 0,\\
m_2l_2\ddot{\theta}_2 + m_2l_1\ddot{\theta}_1\cos(\theta_1 - \theta_2) - m_2l_1\dot{\theta}_1^2\sin(\theta_1 - \theta_2) + m_2g\sin\theta_2 = 0.
\end{align*}

`scipy`

's ordinary differential equation solver, `integrate.odeint`

needs to work with systems of first-order differential equations, so let $z_1 \equiv \dot{\theta_1} \Rightarrow \ddot{\theta}_1 = \dot{z}_1$ and $z_2 \equiv \dot{\theta_2} \Rightarrow \ddot{\theta}_2 = \dot{z}_2$. After some rearranging, the following expressions for $\dot{z}_1$ and $\dot{z}_2$ are obtained:

\begin{align*}
\dot{z}_1 = \frac{m_2g\sin\theta_2\cos(\theta_1-\theta_2) - m_2\sin(\theta_1 - \theta_2)[l_1z_1^2\cos(\theta_1 - \theta_2) + l_2z_2^2] - (m_1+m_2)g\sin\theta_1}{l_1[m_1 + m_2\sin^2(\theta_1-\theta_2)]},\\
\dot{z}_2 = \frac{(m_1+m_2)[l_1z_1^2\sin(\theta_1-\theta_2) - g\sin\theta_2 + g\sin\theta_1\cos(\theta_1-\theta_2)]+m_2l_2z_2^2\sin(\theta_1-\theta_2)\cos(\theta_1-\theta_2)}{l_2[m_1 + m_2\sin^2(\theta_1-\theta_2)]}
\end{align*}

It is these equations which appear in the function `deriv`

in the code above.

## Comments

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## Matias Koskimies 3 years, 2 months ago

I would love to see how a chaos pendulum with the middle point (knee) fixed would look like.

Link | Reply## christian 3 years, 2 months ago

I'm not sure I follow you – if you fix both the first and second joints, then you have one pendulum. If you fix only the second ("knee"), you have two single pendulums? Or are you interested in the relative motion between the two bobs (theta2 - theta1)?

Link | Reply## Dave F 3 years ago

The double pendulum animation at the top of the page starts off slowly then builds up its speed until it is moving very quickly before the animation resets. (On my browser, at least.) I assume it's written in Javascript.

Link | ReplyMy guess is that in discretizing the changes to the angles, a tiny change to the system's total energy in introduced with each discrete step, and this accumulates with time.

## christian 3 years ago

Many thanks for noticing this: the code is in Python but there was a bug – a missing factor of cos(theta1-theta2) – which caused the energy to drift. I've corrected it now and added a check for energy conservation.

Link | Reply## Joel 3 years ago

Nice.

Link | Reply## Andrey 1 year, 11 months ago

Can we slide the joint connecting the second pendulum (or "knee", as one commenter puts it) up and down the first pendulum? E.g., one corner case is that you align the joint connecting the second pendulum to the first with the joint of the first pendulum (the two pendulums are, in effect, independent pendulums).

Link | Reply## christian 1 year, 11 months ago

Hi Andrey,

Link | Replyyou can alter the values of L1 and L2 to effectively move the joint connecting the two pendulums: but this keeps the "knee" at the bob of the first pendulum. I think you're describing the situation where the knee is along the rod L1 between the top pivot and the first bob.

No doubt this could be investigated, but it requires a new analysis of the dynamics and an additional variable (the position of the knee).

## John 1 year, 8 months ago

Pretty cool simulation.

Link | ReplyHow would you account for friction in the joints?

I'm building a 3d printed double pendulum and wonder if I could use this simulation to model different bearing and their friction, rod lengths, and different rod masses.

Could you use your energy conservation fix to insert a small lose each time?

## christian 1 year, 8 months ago

I think you could account for friction by adding a dissipative term to the Euler-Lagrange equations... this would add some complexity to the solution, however.

Link | ReplyMy energy conservation fix was a fix to the implementation of the friction-free equations: I had a bug that I should have detected by checking for energy conservation. I don't think the bug itself was a good way of introducing a dissipation effect: in my case the energy increased...(!)

Don't forget that this is a chaotic system, so modelling in this way may not tell you very much about the precise long-term behaviour of an actual, physical double-pendulum.

## Naomi 1 year, 4 months ago

i am looking for code that makes a trail following a circle on python canvas, can you point out the code for that specific part?

Link | Reply## Mike 1 year, 4 months ago

Hey! Im really looking for a simulation of a double physical pendulum, have you any?

Link | Reply## christian 1 year, 4 months ago

I'm a bit confused: this is a simulation of a double pendulum... what do you mean by "double physical pendulum"?

Link | Reply## Mike 1 year, 3 months ago

The physical pendulum is when the mass isnt entirely at the end like a point, the string itself has mass, ideal pendulum is when you neglect the mass of the string. So you get that the mass is at lenght L/2 for example.

Link | Reply## christian 1 year, 3 months ago

Oh, I see: well, the Langrangian is given in Wikipedia, so it would be fairly straightforward to edit the deriv function to change it there. I'll see if I can get round to it and post again.

Link | Reply## Shawn 4 months, 3 weeks ago

I'm trying to simulate a double pendulum which the middle ball can move freely along the string ,do you have any idea?

Link | Reply## christian 4 months, 3 weeks ago

In principle, you just write down the Lagrangian and rearrange it for the dynamic variables (in your case, theta and L1, I guess, if the rod is rigid); the problem (apart from the algebra) is in ensuring that the middle bob "knows" not to pass the end bob (L1 must not be longer than the total length of the rod): the equations written here do not account for that.

Link | ReplyYou might be better off just numerically integrating Newton's laws given the forces that apply at a given time in the simulation. Alternatively, have a look at Kane's method, e.g. https://docs.sympy.org/latest/modules/physics/mechanics/kane.html

## Naz Öztürk 3 months, 2 weeks ago

Hello, i am trying to run your code and not sure what to do after installing numpy and scipy. I get the output 'Maximum energy drift of 0.05 exceeded.' What else am i supposed to download to see the final animation. Thank you in advence.

Link | Reply## New Comment