1 Reading ¶ Material related to this page, as well as additional exercises, can be found in ALA 10.6.
2 Learning Objectives ¶ By the end of this page, you should know:
how to formulate a first-order system from Newton’s law the physical parameters from the equation of the system how to interpret the system behavior from its solution the different types of system behavior based on the eigenvalues and how it relates to the physical parameters 3 Simple System ¶ We’ll start with the simplest possible mechanical system: a single mass connected to a fixed support by a spring. Assuming no external force, Newton’s law of Force = Mass × Acceleration results in the following homogeneous second order scalar equation:
m p ¨ + k p = 0 ( SM ) , m > 0 mass k > 0 spring constant \begin{align*}
m\ddot{p} + kp = 0 \quad (\text{SM}), \quad & m > 0 \ \text{mass} & \\ \quad & k > 0 \ \text{spring constant} &
\end{align*} m p ¨ + k p = 0 ( SM ) , m > 0 mass k > 0 spring constant where p ( t ) ∈ R p(t) \in \mathbb{R} p ( t ) ∈ R is the mass’ position over time, p ˙ \dot{p} p ˙ its velocity, and p ¨ \ddot{p} p ¨ its acceleration. Our first order of business is to convert (SM ) to a first-order system. To do so, we define the vector x ∈ R 2 \vv x \in \mathbb{R}^2 x ∈ R 2 as
x = [ p p ˙ ] , i.e., we stack position and velocity.
\vv x = \begin{bmatrix} p \\ \dot{p} \end{bmatrix}, \quad \text{i.e., we stack position and velocity.} x = [ p p ˙ ] , i.e., we stack position and velocity. Then
x ˙ = [ p ˙ p ¨ ] = [ p ˙ − k m p ] = [ 0 1 − k m 0 ] [ p p ˙ ] = [ 0 1 − k m 0 ] x = : A x .
\dot{\vv x} = \begin{bmatrix} \dot{p} \\ \ddot{p} \end{bmatrix} = \begin{bmatrix} \dot{p} \\ -\frac{k}{m}p \end{bmatrix} = \begin{bmatrix} 0 & 1 \\ -\frac{k}{m} & 0 \end{bmatrix} \begin{bmatrix} p \\ \dot{p} \end{bmatrix} = \begin{bmatrix} 0 & 1 \\ -\frac{k}{m} & 0 \end{bmatrix}\vv x=: A\vv x. x ˙ = [ p ˙ p ¨ ] = [ p ˙ − m k p ] = [ 0 − m k 1 0 ] [ p p ˙ ] = [ 0 − m k 1 0 ] x =: A x . Thus, the solutions to (SM ) can be obtained by reading off the first component of x ( t ) = [ p ( t ) p ˙ ( t ) ] \vv x(t) = \bm p(t) \\ \dot{p}(t)\em x ( t ) = [ p ( t ) p ˙ ( t ) ] .
The matrix A A A has imaginary eigenvalues λ ± = ± i k m \lambda_{\pm} = \pm i \sqrt{\frac{k}{m}} λ ± = ± i m k (what physical intuition might have suggested this?), with corresponding eigenvectors
v ± = [ 1 0 ] ± i [ 0 m k ] \vv v_{\pm} = \begin{bmatrix} 1 \\ 0 \end{bmatrix} \pm i \begin{bmatrix} 0 \\ \sqrt{\frac{m}{k}} \end{bmatrix} v ± = [ 1 0 ] ± i [ 0 k m ] Therefore, solutions are weighted sums of
x ± ( t ) = e i k m t ( [ 1 0 ] ± i [ 0 k m ] ) = ( cos ( k m t ) + i sin ( k m t ) ) ( [ 1 0 ] ± i [ 0 k m ] ) = [ cos ( k m t ) ∓ sin ( k m t ) ] ± i [ sin ( k m t ) cos ( k m t ) ] , \begin{align*}
\vv x_{\pm}(t) &= e^{ i\sqrt{\frac{k}{m}}t} \left(\begin{bmatrix} 1 \\ 0 \end{bmatrix} \pm i \begin{bmatrix} 0 \\ \sqrt{\frac{k}{m}} \end{bmatrix}\right) \\
&= \left(\cos\left(\sqrt{\frac{k}{m}}t\right) + i \sin\left(\sqrt{\frac{k}{m}}t\right)\right) \left(\bm 1 \\ 0 \em \pm i \bm 0 \\ \sqrt{\frac{k}{m}}\em\right) \\
&= \begin{bmatrix} \cos\left(\sqrt{\frac{k}{m}}t\right) \\ \mp \sin\left(\sqrt{\frac{k}{m}}t\right) \end{bmatrix}
\pm i\begin{bmatrix} \sin\left(\sqrt{\frac{k}{m}}t\right) \\ \cos\left(\sqrt{\frac{k}{m}}t\right) \end{bmatrix},
\end{align*} x ± ( t ) = e i m k t ( [ 1 0 ] ± i [ 0 m k ] ) = ( cos ( m k t ) + i sin ( m k t ) ) ( [ 1 0 ] ± i [ 0 m k ] ) = ⎣ ⎡ cos ( m k t ) ∓ sin ( m k t ) ⎦ ⎤ ± i ⎣ ⎡ sin ( m k t ) cos ( m k t ) ⎦ ⎤ , but, recalling we want real solutions, we write the general solution as
x ( t ) = c 1 Re { x + ( t ) } + c 2 Im { x + ( t ) } = c 1 [ cos ( k m t ) − sin ( k m t ) ] + c 2 [ sin ( k m t ) cos ( k m t ) ] = [ cos ( k m t ) sin ( k m t ) − sin ( k m t ) cos ( k m t ) ] [ c 1 c 2 ] = [ cos ( k m t ) sin ( k m t ) − sin ( k m t ) cos ( k m t ) ] [ p ( 0 ) p ˙ ( 0 ) ] \begin{align*}
\vv x(t) &= c_1 \text{Re}\{\vv x_{+}(t)\} + c_2 \text{Im}\{x_{+}(t)\} \\
&= c_1 \begin{bmatrix} \cos\left(\sqrt{\frac{k}{m}}t\right) \\ -\sin\left(\sqrt{\frac{k}{m}}t\right) \end{bmatrix} + c_2 \begin{bmatrix} \sin\left(\sqrt{\frac{k}{m}}t\right) \\ \cos\left(\sqrt{\frac{k}{m}}t\right) \end{bmatrix} \\
&= \begin{bmatrix} \cos\left(\sqrt{\frac{k}{m}}t\right) & \sin\left(\sqrt{\frac{k}{m}}t\right) \\ -\sin\left(\sqrt{\frac{k}{m}}t\right) & \cos\left(\sqrt{\frac{k}{m}}t\right) \end{bmatrix} \begin{bmatrix} c_1 \\ c_2 \end{bmatrix} = \begin{bmatrix} \cos\left(\sqrt{\frac{k}{m}}t\right) & \sin\left(\sqrt{\frac{k}{m}}t\right) \\ -\sin\left(\sqrt{\frac{k}{m}}t\right) & \cos\left(\sqrt{\frac{k}{m}}t\right) \end{bmatrix} \begin{bmatrix} p(0) \\ \dot{p}(0) \end{bmatrix}
\end{align*} x ( t ) = c 1 Re { x + ( t )} + c 2 Im { x + ( t )} = c 1 ⎣ ⎡ cos ( m k t ) − sin ( m k t ) ⎦ ⎤ + c 2 ⎣ ⎡ sin ( m k t ) cos ( m k t ) ⎦ ⎤ = ⎣ ⎡ cos ( m k t ) − sin ( m k t ) sin ( m k t ) cos ( m k t ) ⎦ ⎤ [ c 1 c 2 ] = ⎣ ⎡ cos ( m k t ) − sin ( m k t ) sin ( m k t ) cos ( m k t ) ⎦ ⎤ [ p ( 0 ) p ˙ ( 0 ) ] where x ( 0 ) = [ c 1 c 2 ] = [ p ( 0 ) p ˙ ( 0 ) ] \vv x(0) = \begin{bmatrix} c_1 \\ c_2 \end{bmatrix} = \begin{bmatrix} p(0) \\ \dot{p}(0) \end{bmatrix} x ( 0 ) = [ c 1 c 2 ] = [ p ( 0 ) p ˙ ( 0 ) ] . Thus we see that a single mass connected to a fixed support by a spring oscillates with frequency k m \sqrt{\frac{k}{m}} m k in the absence of external forces like friction or damping.
4 System with Friction ¶ Next, we modify our problem to make it a bit more realistic to add friction, which is proportional to p ˙ \dot{p} p ˙ . Newton’s law then becomes:
m p ¨ + β p ˙ + k p = 0. m\ddot{p} + \beta\dot{p} + kp = 0. m p ¨ + β p ˙ + k p = 0. Defining x = [ p p ˙ ] \vv x = \bm p \\ \dot{p}\em x = [ p p ˙ ] as before, our first order reformulation becomes:
x ˙ = [ 0 1 − k m − b m ] [ p p ˙ ] = A x . \dot{x} = \begin{bmatrix} 0 & 1 \\ -\frac{k}{m} & -\frac{b}{m} \end{bmatrix} \begin{bmatrix} p \\ \dot{p} \end{bmatrix} = A \vv x. x ˙ = [ 0 − m k 1 − m b ] [ p p ˙ ] = A x . The eigenvalues of A A A are the solutions to the characteristic equation:
m λ 2 + β λ + k = 0. m\lambda^2 + \beta\lambda + k = 0. m λ 2 + β λ + k = 0. There are three possible cases:
4.1 Overdamped ¶ If β > 2 m k \beta > 2\sqrt{mk} β > 2 mk then this equation has two negative real roots
λ ± = − β ± b 2 − 4 m k 2 m , \lambda_{\pm} = -\frac{\beta \pm \sqrt{b^2 - 4mk}}{2m}, λ ± = − 2 m β ± b 2 − 4 mk , and thus the solution is given by a linear combination of two decaying exponentials
x ( t ) = c 1 e λ + t v + + c 2 e λ − t v − , \vv x(t) = c_1 e^{\lambda_+ t} \vv v_+ + c_2 e^{\lambda_- t} \vv v_-, x ( t ) = c 1 e λ + t v + + c 2 e λ − t v − , for v ± \vv v_{\pm} v ± the corresponding real eigenvectors of λ ± \lambda_{\pm} λ ± . Such a system has so much friction that it no longer oscillates!
4.2 Underdamped ¶ If 0 < β < 2 m k 0 < \beta < 2\sqrt{mk} 0 < β < 2 mk , then (9) has two complex-conjugate roots:
λ ± = − β 2 m ± i 4 m k − b 2 2 m = : − μ ± i r . \lambda_{\pm} = -\frac{\beta}{2m} \pm i\frac{\sqrt{4mk-b^2}}{2m} =: -\mu \pm ir. λ ± = − 2 m β ± i 2 m 4 mk − b 2 =: − μ ± i r . With a little bit of effort, we can compute the matrix exponential here as
e A t = e − μ t [ cos r t sin r t − sin r t cos r t ] e^{At} = e^{-\mu t} \begin{bmatrix} \cos rt & \sin rt \\ -\sin rt & \cos rt \end{bmatrix} e A t = e − μ t [ cos r t − sin r t sin r t cos r t ] so that x ( t ) = e − μ t [ cos r t sin r t − sin r t cos r t ] [ p ( 0 ) p ˙ ( 0 ) ] \vv x(t) = e^{-\mu t}\begin{bmatrix} \cos rt & \sin rt \\ -\sin rt & \cos rt \end{bmatrix} \begin{bmatrix} p(0) \\ \dot{p}(0) \end{bmatrix} x ( t ) = e − μ t [ cos r t − sin r t sin r t cos r t ] [ p ( 0 ) p ˙ ( 0 ) ] . In contrast to the overdamped setting, we see here that oscillations continue at fixed frequency r = k m − b 2 4 m 2 r = \sqrt{\frac{k}{m} - \frac{b^2}{4m^2}} r = m k − 4 m 2 b 2 ,
while the entire trajectory also decays exponentially at rate μ = b 2 m \mu = \frac{b}{2m} μ = 2 m b . Thus
for small friction, we eventually stop moving, but continue to oscillate as we
go to the origin.
4.3 Critically damped ¶ The borderline case occurs when β = β ∗ = 2 m k \beta = \beta_* = 2\sqrt{mk} β = β ∗ = 2 mk , in which case
our matrix A A A has one eigenvalue λ = − β 2 m \lambda = -\frac{\beta}{2m} λ = − 2 m β , and is similar to the Jordan block
J λ = [ − β 2 m 1 0 − β 2 m ] J_{\lambda} = \begin{bmatrix} -\frac{\beta}{2m} & 1 \\ 0 & -\frac{\beta}{2m} \end{bmatrix} J λ = [ − 2 m β 0 1 − 2 m β ] Using the techniques we saw in the previous lecture, we compute the matrix exponential
e A t = [ e λ t t e λ t λ e λ t e λ t + λ t e λ t ] (please double check!) e^{At} = \begin{bmatrix}
e^{\lambda t} & te^{\lambda t} \\
\lambda e^{\lambda t} & e^{\lambda t} + \lambda te^{\lambda t}
\end{bmatrix} \quad \text{(please double check!)} e A t = [ e λ t λ e λ t t e λ t e λ t + λ t e λ t ] (please double check!) and thus our solution is
[ p ( t ) p ˙ ( t ) ] = e A t [ p ( 0 ) p ˙ ( 0 ) ] = e λ t ( [ 1 λ ] p ( 0 ) + [ t 1 + λ t ] p ˙ ( 0 ) ) . \begin{bmatrix}
p(t) \\
\dot{p}(t)
\end{bmatrix} = e^{At} \begin{bmatrix}
p(0) \\
\dot{p}(0)
\end{bmatrix} = e^{\lambda t} \left(
\begin{bmatrix}
1 \\
\lambda
\end{bmatrix} p(0) +
\begin{bmatrix}
t \\
1 + \lambda t
\end{bmatrix} \dot{p}(0)
\right). [ p ( t ) p ˙ ( t ) ] = e A t [ p ( 0 ) p ˙ ( 0 ) ] = e λ t ( [ 1 λ ] p ( 0 ) + [ t 1 + λ t ] p ˙ ( 0 ) ) . Even though the above formula looks quite different, the qualitative behavior is very similar to the overdamped case, as when t → ∞ t \to \infty t → ∞ , the exponential decay e − β 2 m t e^{-\frac{\beta}{2m}t} e − 2 m β t governs the system’s behavior. This corresponds to a non-vibrating solution with the slowest possible decay rate -- any further reduction in β will allow for a damped slowly oscillatory vibration to appear.