Variational Equations (iPython)¶
For this tutorial, we work with a two planet system. We vary the initial semi-major axis \(a\) of the outer planet. Because the planets interact with each other, the final \(x\)-position of the inner planet at the end of the simulation will depend on the initial semi-major axis of the outer planet. We run the simulation once for a fixed \(a_0\) and then use first and second order variational equations to predict the final position of the outer planet for different \(a\)s in a neighbourhood of \(a_0\).
To do that, let us first import REBOUND, numpy and matplotlib.
import rebound import numpy as np %matplotlib inline import matplotlib; import matplotlib.pyplot as plt
Before using variational equations, let us define a function that calculates the final position of the inner planet as a function of \(a\) in the brute-force way:
def run_sim(a): sim = rebound.Simulation() sim.add(m=1.) sim.add(primary=sim.particles,m=1e-3, a=1) sim.add(primary=sim.particles,m=1e-3, a=a) sim.integrate(2.*np.pi*10.) return sim.particles.x
We’ll use this function to create a list of true final positions to which we later compare our results.
N=400 x_exact = np.zeros((N)) a_grid = np.linspace(1.4,1.7,N) for i,a in enumerate(a_grid): x_exact[i] = run_sim(a)
Running a simulation with variational equations is very easy. We start
by creating a simulation and add the three particles (the star and two
planets) just as before. Note that the
vary convenience function we
use below only accepts heliocentric coordinates, so we explicitly tell
REBOUND that the star is the primary when adding particles to the
We then add variational particles to the simulation. We vary one paramter (\(a\)) and thus need only one set of first order variational equaitions. The second order variational equations depend on the first order ones. Thus, when initializing them, one has to pass the set of first order variational equations using the ‘first_order’ parameter.
After adding a variation, one must always initialize it. We do this
below with REBOUND’s
vary() convenience function, which makes
varying orbital parameters particularly easy. Alternatively, one can
also initialize the variational particles directly, e.g. using
var_da.particles.x = 1. Note that variations are implemented as
particles, but you they really represent derivatives of a particle’s
corrdinates with respect to some initial parameter. For more details,
see Rein and Tamayo (2016).
The function below does all that and returns the final position of the inner planet, as well as the first and second derivatives of the position with respect to \(a\).
def run_sim_var(a): sim = rebound.Simulation() sim.add(m=1.) sim.add(primary=sim.particles,m=1e-3, a=1) sim.add(primary=sim.particles,m=1e-3, a=a) var_da = sim.add_variation() var_dda = sim.add_variation(order=2, first_order=var_da) var_da.vary(2, "a") var_dda.vary(2, "a") sim.integrate(2.*np.pi*10.) return sim.particles.x, var_da.particles.x, var_dda.particles.x
We can now use the variational equations to predict the final position of the inner particle. Note that we only run one simulation, at \(a_0=1.56\).
a_0 = 1.56 x, dxda, ddxdda = run_sim_var(a_0) x_1st_order = np.zeros(N) x_2nd_order = np.zeros(N) for i,a in enumerate(a_grid): x_1st_order[i] = x + (a-a_0)*dxda x_2nd_order[i] = x + (a-a_0)*dxda + 0.5*(a-a_0)*(a-a_0)*ddxdda
In the figure below, we plot the final position as a function of the initial semi-major axis. The black line corresponds to the true final position as calculated by the brute-force approach. The dashed and dotted lines correspond to the approximations using first and second order variational equations. As one can see, the second order approximation is very accurate within a neighbourhood of \(a_0\).
fig = plt.figure(figsize=(6,4)) ax = plt.subplot(111) ax.set_xlim(a_grid,a_grid[-1]) ax.set_ylim(np.min(x_exact),np.max(x_exact)*1.01) ax.set_xlabel("initial semi-major axis of the outer planet") ax.set_ylabel("$x$ position of inner planet after 10 orbits") ax.plot(a_grid, x_exact, "-", color="black", lw=2) ax.plot(a_grid, x_1st_order, "--", color="green") ax.plot(a_grid, x_2nd_order, ":", color="blue") ax.plot(a_0, x, "ro",ms=10);