Dynamic Model of a Vertical Piano Action Mechanism
Bibliographic record
Abstract
The dynamic behavior of a vertical piano action mechanism is studied using a simulation model and compared qualitatively to observations obtained by high-speed imaging of a real action. The simulated response of all components is obtained for two different prescribed input force profiles applied at the key front. These inputs represent in simplified form the general shape of a typical force input by a pianist measured at the key surface for a strong (forte) strike, or two key strikes in rapid succession. The graph-theoretic multibody model constructed represents the components and their interactions. Explicit contact edges provide forces generated between two bodies as a function of their kinematic states, using a special contact model to represent the compression of felt lined interfaces that can separate during the key stroke. Masses and geometrical parameters of the action were measured by importing scanned images from a real action into CAD software. The highly nonlinear system of five ordinary differential equations of motion was derived symbolically and solved by a numerical stiff solver in Maple. The effects of two components not present in the horizontal grand piano action, the bridle strap and hammer butt spring, were examined using simulations. The butt spring is seen to serve an important function in assisting the return of the hammer to its rest position on key release. The model will be useful in future studies to compare vertical actions to horizontal grand piano actions, as these are known to exhibit quite different playing characteristics.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".