A time-domain derivation of the telegraph equation to describe sound propagation in rigid tubes
Bibliographic record
Abstract
Starting from first principles, we describe the propagation of sound waves in rigid tubes using a simple approach that derives a telegraph equation with frequency-independent parameters from the lossless wave equation by relaxing one assumption: that the dynamical variables are constant over the entire cross-sectional area of the tube. We do this by introducing a relatively narrow boundary layer at the wall of the tube, over which the dynamical variables decrease linearly to zero. This allows us to make very simple corrections to the lossless case, and to express them in terms of two unorthodox parameters, the viscous diffusion time constant and the thermal diffusion time constant. A comparison with the telegraph equation for the electrical transmission line establishes precise relationships between the electrical circuit elements and the physical properties of the fluid, which are thus proven a posteriori rather than asserted a priori. We thus arrive at an instructive and useful derivation of the acoustic telegraph equation, which takes viscous damping and thermal dissipation into account but does not resort to the combined heavy machinery of fluid dynamics and thermodynamics, does not assume that the waveforms are sinusoidal, and does not assume any particular cross sectional shape of the tube. The approach is novel in that a comparable treatment appears neither in the standard physics and acoustics texts nor in the literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".