Discrete port-Hamiltonian system model of a single-reed woodwind instrument
Bibliographic record
Abstract
Time-domain simulation of woodwind instruments typically involves the development of separate discrete-time sub-models for the excitation mechanism and the resonator. These components have largely been modeled via digital waveguide or finite-difference time-domain (FDTD) methods. We present a separate approach based on the modular and energy-based port-Hamiltonian system (PHS) framework. We recast the three main components of a woodwind instrument—the single-reed, the bore, and the tonehole—as PHS models and incorporate novel elements in each derivation. In the beating reed model, we make use of recent work on energy quadratization to formulate a linearly implicit scheme of the nonlinear Hunt-Crossley contact force coupled to a nonlinear Bernoulli flow. In the horn model, we discretize a distributed PHS representing the horn equation with a generalized symplectic Störmer-Verlet scheme, verifying previously proposed FDTD schemes. In the tonehole model, we propose a new low-frequency model of the tonehole and model note transitions with a switching PHS. The benefit of describing each element as a PHS is demonstrated by the ability to interconnect all sub-models in a modular and energy-conserving manner to simulate a complete instrument. Simulations are performed on a test instrument and the numerical stability of the overall scheme is demonstrated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".