# Lorenz system

(Redirected from Lorenz Attractor)
A sample solution in the Lorenz attractor when ρ = 28, σ = 10, and β = 8/3

The Lorenz system is a system of ordinary differential equations first studied by Edward Lorenz. It is notable for having chaotic solutions for certain parameter values and initial conditions. In particular, the Lorenz attractor is a set of chaotic solutions of the Lorenz system which, when plotted, resemble a butterfly or figure eight.

## Overview

In 1963, Edward Lorenz developed a simplified mathematical model for atmospheric convection.[1] The model is a system of three ordinary differential equations now known as the Lorenz equations:

{\displaystyle {\begin{aligned}{\frac {\mathrm {d} x}{\mathrm {d} t}}&=\sigma (y-x),\\{\frac {\mathrm {d} y}{\mathrm {d} t}}&=x(\rho -z)-y,\\{\frac {\mathrm {d} z}{\mathrm {d} t}}&=xy-\beta z.\end{aligned}}}

The equations relate the properties of a two-dimensional fluid layer uniformly warmed from below and cooled from above. In particular, the equations describe the rate of change of three quantities with respect to time: ${\displaystyle x}$ is proportional to the rate of convection, ${\displaystyle y}$ to the horizontal temperature variation, and ${\displaystyle z}$ to the vertical temperature variation.[2] The constants ${\displaystyle \sigma }$, ${\displaystyle \rho }$, and ${\displaystyle \beta }$ are system parameters proportional to the Prandtl number, Rayleigh number, and certain physical dimensions of the layer itself.[3]

The Lorenz equations also arise in simplified models for lasers,[4] dynamos,[5] thermosyphons,[6] brushless DC motors,[7] electric circuits,[8] chemical reactions[9] and forward osmosis.[10]

From a technical standpoint, the Lorenz system is nonlinear, non-periodic, three-dimensional and deterministic. The Lorenz equations have been the subject of hundreds of research articles, and at least one book-length study.[11]

## Analysis

One normally assumes that the parameters ${\displaystyle \sigma }$, ${\displaystyle \rho }$, and ${\displaystyle \beta }$ are positive. Lorenz used the values ${\displaystyle \sigma =10}$, ${\displaystyle \beta =8/3}$ and ${\displaystyle \rho =28}$. The system exhibits chaotic behavior for these (and nearby) values.[12]

If ${\displaystyle \rho <1}$ then there is only one equilibrium point, which is at the origin. This point corresponds to no convection. All orbits converge to the origin, which is a global attractor, when ${\displaystyle \rho <1}$.[13]

A pitchfork bifurcation occurs at ${\displaystyle \rho =1}$, and for ${\displaystyle \rho >1}$ two additional critical points appear at: ${\displaystyle \left({\sqrt {\beta (\rho -1)}},{\sqrt {\beta (\rho -1)}},\rho -1\right)}$ and ${\displaystyle \left(-{\sqrt {\beta (\rho -1)}},-{\sqrt {\beta (\rho -1)}},\rho -1\right).}$ These correspond to steady convection. This pair of equilibrium points is stable only if

${\displaystyle \rho <\sigma {\frac {\sigma +\beta +3}{\sigma -\beta -1}},}$

which can hold only for positive ${\displaystyle \rho }$ if ${\displaystyle \sigma >\beta +1}$. At the critical value, both equilibrium points lose stability through a Hopf bifurcation.[14]

When ${\displaystyle \rho =28}$, ${\displaystyle \sigma =10}$, and ${\displaystyle \beta =8/3}$, the Lorenz system has chaotic solutions (but not all solutions are chaotic). Almost all initial points will tend to an invariant set – the Lorenz attractor – a strange attractor and a fractal. Its Hausdorff dimension is estimated to be 2.06 ± 0.01, and the correlation dimension is estimated to be 2.05 ± 0.01.[15] The exact Lyapunov dimension (Kaplan-Yorke dimension) formula of the global attractor can be found analytically under classical restrictions on the parameters

[16]
${\displaystyle 3-{\frac {2(\sigma +\beta +1)}{\sigma +1+{\sqrt {(\sigma -1)^{2}+4\sigma \rho }}}}}$.

The Lorenz attractor is difficult to analyze, but the action of the differential equation on the attractor is described by a fairly simple geometric model.[17] Proving that this is indeed the case is the fourteenth problem on the list of Smale's problems. This problem was the first one to be resolved, by Warwick Tucker in 2002.[18]

For other values of ${\displaystyle \rho }$, the system displays knotted periodic orbits. For example, with ${\displaystyle \rho =99.96}$ it becomes a T(3,2) torus knot.

Example solutions of the Lorenz system for different values of ρ
ρ=14, σ=10, β=8/3 (Enlarge) ρ=13, σ=10, β=8/3 (Enlarge)
ρ=15, σ=10, β=8/3 (Enlarge) ρ=28, σ=10, β=8/3 (Enlarge)
For small values of ρ, the system is stable and evolves to one of two fixed point attractors. When ρ is larger than 24.74, the fixed points become repulsors and the trajectory is repelled by them in a very complex way.
Java animation showing evolution for different values of ρ
Sensitive dependence on the initial condition
Time t=1 (Enlarge) Time t=2 (Enlarge) Time t=3 (Enlarge)
These figures — made using ρ=28, σ = 10 and β = 8/3 — show three time segments of the 3-D evolution of 2 trajectories (one in blue, the other in yellow) in the Lorenz attractor starting at two initial points that differ only by 10−5 in the x-coordinate. Initially, the two trajectories seem coincident (only the yellow one can be seen, as it is drawn over the blue one) but, after some time, the divergence is obvious.
Java animation of the Lorenz attractor shows the continuous evolution.

### MATLAB simulation

% Solve over time interval [0,100] with initial conditions [1,1,1]
% 'f' is set of differential equations
% 'a' is array containing x, y, and z variables
% 't' is time variable

sigma = 10;
beta = 8/3;
rho = 28;
f = @(t,a) [-sigma*a(1) + sigma*a(2); rho*a(1) - a(2) - a(1)*a(3); -beta*a(3) + a(1)*a(2)];
[t,a] = ode45(f,[0 100],[1 1 1]);     % Runge-Kutta 4th/5th order ODE solver
plot3(a(:,1),a(:,2),a(:,3))


### Mathematica simulation

a = 10; b = 8/3; r = 28;
x = 1; y = 1; z = 1;points = {{1,1,1}};
i := AppendTo[points, {x = N[x + (a*y - a*x)/100], y = N[y + (-x*z + r*x - y)/100], z = N[z + (x*y - b*z)/100]}]

Do[i, {3000}]
ListPointPlot3D[points, PlotStyle -> {Red, PointSize[Tiny]}]


An alternative with more Mathematica style:

Clear[LorenzSystemPoints, p, s, pts]

LorenzSystemPoints[parameters_List, steps_Integer] :=
Module[{σ, ρ, β, updates, δ = 1.*^-3, pt0 = {1., 1., 1.}},
{σ, ρ, β} = parameters;
updates = x \[Function] {{1 - δ σ, δ σ, 0},
{δ ρ, 1 - δ, -δ #},
{0, δ #, 1 - δ β}}.{##} & @@ x;
];

p = {10., 28., 8/3.};
s = 30000;
pts = LorenzSystemPoints[p, s];

ListPointPlot3D[pts, PlotRange -> All, PlotTheme -> "Scientific", ImageSize -> Large]


Standard way:

tend = 50;
eq = {x'[t] == σ (y[t] - x[t]),
y'[t] == x[t] (ρ - z[t]) - y[t],
z'[t] == x[t] y[t] - β z[t]};
init = {x[0] == 10, y[0] == 10, z[0] == 10};
pars = {σ->10, ρ->28, β->8/3};
{xs, ys, zs} =
NDSolveValue[{eq /. pars, init}, {x, y, z}, {t, 0, tend}];
ParametricPlot3D[{xs[t], ys[t], zs[t]}, {t, 0, tend}]


### Python simulation

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from mpl_toolkits.mplot3d import Axes3D

rho = 28.0
sigma = 10.0
beta = 8.0 / 3.0

def f(state, t):
x, y, z = state  # unpack the state vector
return sigma * (y - x), x * (rho - z) - y, x * y - beta * z  # derivatives

state0 = [1.0, 1.0, 1.0]
t = np.arange(0.0, 40.0, 0.01)

states = odeint(f, state0, t)

fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(states[:,0], states[:,1], states[:,2])
plt.show()


### Modelica simulation

model LorenzSystem

parameter Real sigma = 10;
parameter Real rho = 28;
parameter Real beta = 8/3;

parameter Real x_start = 1 "Initial x-coordinate";
parameter Real y_start = 1 "Initial y-coordinate";
parameter Real z_start = 1 "Initial z-coordinate";

Real x "x-coordinate";
Real y "y-coordinate";
Real z "z-coordinate";

initial equation
x = x_start;
y = y_start;
z = z_start;

equation

der(x) = sigma*(y-x);
der(y) = rho*x - y - x*z;
der(z) = x*y - beta*z;

end LorenzSystem;


## Derivation of the Lorenz equations as a model of atmospheric convection

The Lorenz equations are derived from the Oberbeck-Boussinesq approximation to the equations describing fluid circulation in a shallow layer of fluid, heated uniformly from below and cooled uniformly from above.[1] This fluid circulation is known as Rayleigh-Bénard convection. The fluid is assumed to circulate in two dimensions (vertical and horizontal) with periodic rectangular boundary conditions.

The partial differential equations modeling the system's stream function and temperature are subjected to a spectral Galerkin approximation: the hydrodynamic fields are expanded in Fourier series, which are then severely truncated to a single term for the stream function and two terms for the temperature. This reduces the model equations to a set of three coupled, nonlinear ordinary differential equations. A detailed derivation may be found, for example, in nonlinear dynamics texts.[19] The Lorenz system is a reduced version of a larger system studied earlier by Barry Saltzman.[20]

## Resolution of Smale's 14th problem

Smale's 14th problem says 'Do the properties of the Lorenz attractor exhibit that of a strange attractor?', it was answered affirmatively by Warwick Tucker in 2002[21]. To prove this result, Tucker used rigorous numerics methods like Interval arithmetic and Normal Forms. First, Tucker defined a cross section ${\displaystyle \Sigma \subset \{x_{3}=r-1\}}$ that is cut transversely by the flow trajectories. From this, one can define the first-return map ${\displaystyle P}$, which assigns to each ${\displaystyle x\in \Sigma }$ the point ${\displaystyle P(x)}$ where the trajectory of ${\displaystyle x}$ first intersects ${\displaystyle \Sigma }$.

Then the proof is split in three main points that are proved and imply the existence of a strange attractor[22]. The three points are:

• There exists a region ${\displaystyle N\subset \Sigma }$ invariant under the first-return map, meaning ${\displaystyle P(N)\subset N}$
• The return map admits a forward invariant cone field
• Vectors inside this invariant cone field are uniformly expanded by the derivative ${\displaystyle DP}$ of the return map.

To prove the first point, we notice that the cross section ${\displaystyle \Sigma }$ is cut by two arcs formed by ${\displaystyle P(\Sigma )}$ (see [23]). Tucker covers the location of these two arcs by small rectangles ${\displaystyle R_{i}}$, the union of these rectangles gives ${\displaystyle N}$. Now, the goal is to prove that for all points in ${\displaystyle N}$, the flow will bring back the points in ${\displaystyle \Sigma }$, in ${\displaystyle N}$. To do that, we take a plan ${\displaystyle \Sigma '}$ below ${\displaystyle \Sigma }$ at a distance ${\displaystyle h}$ small, then by taking the center ${\displaystyle c_{i}}$ of ${\displaystyle R_{i}}$ and using Euler integration method, one can estimate where the flow will bring ${\displaystyle c_{i}}$ in ${\displaystyle \Sigma '}$ which gives us a new point ${\displaystyle c_{i}'}$. Then, one can estimate where the points in ${\displaystyle \Sigma }$ will be mapped in ${\displaystyle \Sigma '}$ using Taylor expansion, this gives us a new rectangle ${\displaystyle R_{i}'}$ centered on ${\displaystyle c_{i}}$. Thus we know that all points in ${\displaystyle R_{i}}$ will be mapped in ${\displaystyle R_{i}'}$. The goal is to do this method recursively until the flow comes back to ${\displaystyle \Sigma }$ and we obtain a rectangle ${\displaystyle Rf_{i}}$ in ${\displaystyle \Sigma }$ such that we know that ${\displaystyle P(R_{i})\subset Rf_{i}}$. The problem is that our estimation may become imprecise after several iterations, thus what Tucker does is to split ${\displaystyle R_{i}'}$ into smaller rectangles ${\displaystyle R_{i,j}}$ and then apply the process recursively. Another problem is that as we are applying this algorithm, the flow becomes more 'horizontal' (see [24]), leading to a dramatic increase in imprecision. To prevent this, the algorithm changes the orientation of the cross sections, becoming either horizontal or vertical.

## Notes

1. ^ a b Lorenz (1963)
2. ^ Sparrow (1982)
3. ^ Sparrow (1982)
4. ^ Haken (1975)
5. ^ Knobloch (1981)
6. ^ Gorman, Widmann & Robbins (1986)
7. ^ Hemati (1994)
8. ^ Cuomo & Oppenheim (1993)
9. ^ Poland (1993)
10. ^
11. ^ Sparrow (1982)
12. ^ Hirsch, Smale & Devaney (2003), pp. 303–305
13. ^ Hirsch, Smale & Devaney (2003), pp. 306+307
14. ^ Hirsch, Smale & Devaney (2003), pp. 307+308
15. ^ Grassberger & Procaccia (1983)
16. ^ Leonov et al. (2016)
17. ^ Guckenheimer, John; Williams, R. F. (1979-12-01). "Structural stability of Lorenz attractors". Publications Mathématiques de l'Institut des Hautes Études Scientifiques. 50 (1): 59–72. doi:10.1007/BF02684769. ISSN 0073-8301.
18. ^ Tucker (2002)
19. ^ Hilborn (2000), Appendix C; Bergé, Pomeau & Vidal (1984), Appendix D
20. ^ Saltzman (1962)
21. ^ Tucker (2002)
22. ^ Viana (2000)
23. ^ Viana (2000)
24. ^ Viana (2000)