Effects of Friction and Spacing on the Collaborative Behavior of Domino Toppling
Bibliographic record
Abstract
Inspired by the high-speed camera experiments of YouTuber Destin Sandlin (SmarterEveryDay) [D. Sandlin, Dominoes – hardcore mode (2017), [Online; accessed 15-Jul-2021].] on the toppling speed of dominoes over different surfaces, we performed discrete-element simulations of this process, varying the spacing between adjacent and evenly spaced blocks (dominoes). We also varied the block-block and block-surface friction coefficients over a wide range of values to have a complete picture of the behavior of these cooperative, dissipative mechanical systems. We found that a steady wavefront speed v exists for a specific interval of spacings between dominoes and coefficients of friction. Surprisingly, while v is more affected by the domino-domino friction, the domino-surface friction determines whether or not toppling anomalies can appear and stop the wave. Finally, our observations led us to propose a scaling law that is able to predict v based on the domino configuration and friction coefficients, and to correctly reproduce experimental tests.Received 16 December 2021Revised 29 March 2022Accepted 26 April 2022Corrected 24 June 2022DOI:https://doi.org/10.1103/PhysRevApplied.17.064021© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasClassical mechanicsFrictionScaling laws of complex systemsPhysical SystemsDynamical systemsFront propagationGeneral PhysicsNonlinear Dynamics
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".