Acoustic wavelength measurement ($\lambda =2d$)

An acoustic wavelength measurement makes an invisible spatial pattern readable with a ruler. The authored guide Interesting and inexpensive experiments for high school physics by Joe Wolfe places a loudspeaker near a pipe's open end, drives it with an oscillator, and slides a microphone attached to a metre rule through the pipe. An oscilloscope or computer displays the microphone signal. Its oscillation amplitude rises and falls with position; adjacent minima locate pressure nodes of a standing wave a distance $d=\lambda /2$ apart. A cap gives a pressure antinode at the closed end; an open end has a pressure node just outside it. These are pressure patterns, distinct from displacement patterns. The guide advises wavelengths longer than the pipe diameter, sufficient pipe length to locate both a node and an antinode, and frequency spectrum analysis to distinguish the intended tone from other sounds.^[ref]

The inference follows from superposition of two oppositely directed acoustic plane waves. In an ideal uniform, lossless pipe, taking equal pressure amplitudes $P$ and a rigid cap at $x=0$ givesThe time-varying pressure perturbation $p^{\prime }$ has local oscillation amplitude $2|P\cos (kx)|$. Its zeros are separated by $\pi /|k|=\lambda /2$. Unequal incident and reflected amplitudes leave nonzero minima, but their spacing remains $\lambda /2$ in this ideal model. A microphone detects that pressure variation through a changing electrical signal, rather than watching a wave crest move. Record the tone's frequency too, then infer phase speed $v_p=2fd$.

For proposed controls, keep the drive frequency and amplitude fixed during a scan, verify the frequency on the recorded signal, and repeat the scan in both directions. Fit several minimum positions $x_j=a+jd$ using linear regression to reduce dependence on one pair of positions. Check the ruler scale and use one consistent reference point on the microphone. Compare with the source switched off to locate background measurement noise; use frequency spectrum analysis to check for additional tones, and reduce drive if the trace ceases to resemble a sinusoid. Record air temperature and avoid strong air motion. These are suggested controls, not additional procedures or observations attributed to the guide.

The pipe must support approximately plane waves over the measured region: transverse structure at shorter wavelengths, the microphone's finite size, and disturbance by the ruler can compromise the interpretation. Weak reflection gives shallow minima, while damping and other reflections can shift the extrema of the simple two-wave model. Measure interior spacings rather than assuming the first minimum is exactly at the pipe lip. A short scan may not include enough minima to estimate $d$ reliably, and drift during sequential scanning can imitate spatial variation.

For an explicitly hypothetical calculation, $f=1000\mathrm{Hz}$ and $d=0.1715\mathrm{m}$ give $\lambda =0.3430\mathrm{m}$, $K\simeq 18.3183\mathrm{rad}{\mathrm{m}}^{-1}$, and $v_p=343.0\mathrm{m}{\mathrm{s}}^{-1}$. No such measurements are claimed here. If independent small measurement errors in $f$ and fitted $d$ have standard deviations ${\sigma }_f$ and ${\sigma }_d$, first-order error propagation gives ${\sigma }_{v_p}/v_p\simeq \sqrt{({\sigma }_f/f{)}^2+({\sigma }_d/d{)}^2}$. A systematic scale error or changing air conditions requires separate assessment.

An oscilloscope displays an electrical signal versus time. In an acoustic wavelength measurement, the microphone trace supplies the tone's frequency and oscillation amplitude as the detector changes position.

A connection that couples a measured electric potential difference into an oscilloscope. Its electrical resistance and capacitance contribute to instrument loading.

An oscilloscope that uses a deflected electron beam and a fluorescent screen to display a signal.

Propagation of uncertainty estimates how measurement errors in inputs affect a calculated result. For small input errors with covariance matrix $\Sigma$, linearizing a differentiable function $g$ gives the displayed approximation for its variance. For independent errors in a product $g=2fd$, the squared relative standard deviations add. Correlated errors need the covariance terms; systematic offsets require separate assessment.

If measured radiative flux is $S=A-B>0$ and $S=k{T}^4$ with known $k$, first-order uncertainty propagation gives the displayed result. Independent measurements have zero covariance; strict error bounds combine differently. Even a small fractional background contribution can matter if its relative uncertainty is large. Inferring the covariance or an unknown calibration factor from precision percentages alone is unjustified.

For $c=a/b-1$, the sensitivity coefficients are $1/b$ and $-a/b^2$. The first-order uncertainty propagation law gives the displayed variance; independent inputs remove the covariance term. Subtracting a known constant does not change absolute uncertainty, but can greatly change relative uncertainty when the resulting value is small. The result is a standard uncertainty estimate, not automatically a worst-case error bound or a confidence interval.

For $z=\sqrt{R^2+X^2}$ with reactance $X=\omega L$, linear differentiation gives $dz=(RdR+XdX)/z$. Independent standard uncertainties give the title formula; correlated inputs add $2RX\mathrm{Cov}(R,X)/z^2$. Hard error bounds instead add absolute linear contributions. The meaning of an error bar is an additional measurement assumption, not a consequence of the impedance formula.

For air approximated by index one, $n=\sin i/\sin r$, so $dn/n=\cot idi-\cot rdr$. Taking the variance gives the displayed first-order error formula, with angles and their errors in radians. Independence removes the covariance term; hard error bounds require a different combination rule. Common angular-zero errors can produce covariance and should not automatically be added in quadrature.

For axial moment of inertia $I=m(a^2+b^2)/2$, linear sensitivity to mass and outer/inner radius is $dI=(a^2+b^2)dm/2+mada+mbdb$. Independent standard uncertainties give ${\sigma }_I^2=[(a^2+b^2){\sigma }_m/2{]}^2+(ma{\sigma }_a{)}^2+(mb{\sigma }_b{)}^2$. Correlated measurements instead require the full covariance quadratic form. For deterministic bounds, add the absolute linear contributions rather than their squares.

For $S=A\cos (q\theta )$ with independent standard uncertainties, linear propagation gives ${\sigma }_S^2={\cos }^2(q\theta ){\sigma }_A^2+[qA\sin (q\theta ){]}^2{\sigma }_{\theta }^2$. Angular uncertainty must be in radians when using this derivative. Covariance adds cross terms for correlated inputs. Strict error bounds instead add absolute first-order contributions; they do not justify Gaussian significance tests.

For positive measured inputs and $f=C{\prod }_i{x}_i^{a_i}$, logarithmic differentiation gives $df/f={\sum }_i{a}_{i}d{x}_i/x_i$. Applying propagation of uncertainty yields the displayed covariance formula. For independent input errors it reduces to ${\sigma }_f/|f|\simeq [{\sum }_i(a_i{\sigma }_i/x_i{)}^2{]}^{1/2}$. If the input uncertainties are strict bounds rather than standard deviations, a first-order conservative bound is $|\Delta f|/|f|\lesssim {\sum }_i|a_i|\Delta x_i/|x_i|$. These are different statistical interpretations; a product formula alone does not choose between them.

For the same massless spring obeying Hooke's law, the frequency is $\nu =(2\pi {)}^{-1}\sqrt{k/m}$. The displayed mass ratio follows. For small independent frequency errors with common fractional standard uncertainty $ϵ$, monomial uncertainty propagation gives ${\sigma }_R/R\simeq \sqrt{8}ϵ$. If $ϵ$ instead describes individual measurements and each sample mean uses $n$ independent trials, its standard error is smaller by $\sqrt{n}$, giving ${\sigma }_R/R\simeq \sqrt{8/n}ϵ$. Four times as many trials halve this statistical uncertainty; a common calibration error needs its covariance included.

For simple pendulum oscillation period $T$ in the small-angle approximation, $g=4{\pi }^{2}L/T^2$. If the length measurement and $N$ identically precise timing measurements have independent errors, the timing sample mean has standard error ${\sigma }_T/\sqrt{N}$. Propagation of uncertainty gives the title formula. A target fractional standard uncertainty $ϵ$ requires $N\ge 4({\sigma }_T/T{)}^2/[{ϵ}^2-({\sigma }_L/L{)}^2]$, rounded upward to an integer. No amount of repeated timing can beat the floor ${\sigma }_L/L$ without improving the length measurement; correlated errors or a timing systematic error require a different model.