Lessons · shm

Damping: when the wobble dies

Regime 2 — calculus does what algebra cannot. The acceleration isn't constant, so the algebra formulas don't apply; calculus is the only road in, and SymPy proves the closed form solves the equation of motion.

Regime 2 · calculus does more Math machine-derived & checked by SymPy 2 modeling assumptions (author-asserted)

Add friction to the mass on a spring. As the damping grows the motion changes character — oscillating, then just barely returning, then crawling back. Where is the boundary, and what is the fastest a system can return to rest?

Natural angular frequency ω0=k/m\omega_0 = \sqrt{k/m}
4 1/s
Critical damping bc=2kmb_c = 2\sqrt{km}
8 kg/s
Newton's law with a spring and a drag force
m x′′=−k x−b x′    ⟹    x′′+2γx′+ω02x=0m\,x'' = -k\,x - b\,x' \;\;\Longrightarrow\;\; x'' + 2\gamma x' + \omega_0^2 x = 0

Restoring force −kx-kx plus a velocity-dependent damping −bx′-bx'. Here γ=b/2m\gamma = b/2m and ω02=k/m\omega_0^2 = k/m.

back-substitute each form into mx′′+bx′+kx=0m x'' + b x' + k x = 0; check the initial conditions; check dE/dt=−bv2dE/dt = -b v^2

  • ✓ Underdamped form e−γt(cos⁡,sin⁡)e^{-\gamma t}(\cos,\sin) solves x′′+2γx′+ω02x=0x'' + 2\gamma x' + \omega_0^2 x = 0. [structural]
  • ✓ Critical form (x0+… t) e−γt(x_0 + \dots\,t)\,e^{-\gamma t} solves x′′+2γx′+γ2x=0x'' + 2\gamma x' + \gamma^2 x = 0 (where ω0=γ\omega_0 = \gamma). [structural]
  • ✓ Overdamped form e−γt(cosh⁡,sinh⁡)e^{-\gamma t}(\cosh,\sinh) solves x′′+2γx′+(γ2−s2)x=0x'' + 2\gamma x' + (\gamma^2 - s^2)x = 0. [structural]
  • ✓ It matches the initial position x(0)=x0x(0) = x_0. [structural]
  • ✓ It matches the initial velocity x′(0)=v0x'(0) = v_0. [structural]
  • ✓ Energy dissipates at exactly dEdt=−bv2\tfrac{dE}{dt} = -b v^2 (here per unit mass, b/m=2γb/m = 2\gamma). [structural]

Dimensional homogeneity: checked by SymPy (holds).

✗ Common misconception: “More damping always makes a system return to rest faster.”

Past critical damping, adding more damping makes the return SLOWER — the overdamped system crawls back to equilibrium. The quickest return without overshoot is exactly at critical damping (ζ=1\zeta = 1); that is why door closers and instrument needles are tuned to it.

Modeling assumptions — author-asserted, disclosed not discharged
  • Linear (viscous) damping: the resistive force is -b·x' (proportional to velocity).
  • Ideal Hooke's-law spring; the mass is a point.

The stacked graph, fully annotated

A static rendering (Matplotlib) at the default parameters — the interactive version is in the Graph tab above.

2026-06-26T13:20:57.189683 image/svg+xml Matplotlib v3.11.0, https://matplotlib.org/