Mathematical Model on Impact of Reynolds Number on Cascading Balls

Authors

DOI:

https://doi.org/10.62054/ijdm/0301.09

Abstract

This study develops a rigorous mathematical framework to quantify the influence of Reynolds number on cascading ball dynamics in rhythmic throwing and juggling systems. Building on classical projectile motion and Lagrangian mechanics, the model explicitly incorporates aerodynamic drag as a Reynolds-number dependent force. By linking drag coefficients to flow regimes ranging from laminar to turbulent, the analysis reveals how Reynolds variation alters flight time, trajectory shape, and rhythmic synchronization conditions. The framework is extended to an arbitrary number of balls, including the dense-limit case where the number of balls approaches infinity. Numerical simulations demonstrate that increasing Reynolds number systematically reduces flight duration and trajectory height, while preserving rhythmic stability through compensatory timing adjustments. The results establish Reynolds number as a governing parameter in cascading dynamics, providing a physically grounded bridge between classical mechanics, fluid dynamics, and rhythmic coordination. This work offers new insights into the robustness of juggling motions and provides a scalable foundation for applications in biomechanics, robotics, and flow-sensitive rhythmic systems

Author Biography

  • Nicholas N. Topman, Department of Mathematics, Enugu State University of Science and Technology (ESUT), Nigeria

    my recent work on ball juggling

References

Abu Salem, K. (2024). The key role of research in flight dynamics, control, and simulation for advancing aeronautical sciences. Aerospace, 11(9), 734. https://doi.org/10.3390/aerospace11090734

Bradshaw, J. L. (2023). Projectile motion with quadratic drag. American Journal of Physics, 91(3), 258–263. https://doi.org/10.1119/5.0095643

Geller, N., Moringen, A., & Friedman, J. (2023). Learning juggling by gradually increasing difficulty versus learning the complete skill results in different learning patterns. Frontiers in Psychology, 14, Article 1284053. https://doi.org/10.3389/fpsyg.2023.1284053

Huys, R., Beek, P. J., & van Santvoord, A. A. M. (2004). Multiple time scales and multiform dynamics in learning three-ball cascade juggling. Journal of Experimental Psychology: Human Perception and Performance, 30(4), 665–681. https://doi.org/10.1037/0096-1523.30.4.665

Jobunga, E. O., Warui, K., Menge, B. K., Mugambi, E., & Dillmann, B. (2024). Analytical solution of projectile motion under a linear drag force. Journal of Applied Mathematics, 2024, Article 8881003. https://doi.org/10.1155/2024/8881003

Kovačević, M., Kuzmanović, L., & Milošević, M. (2024). An experiment for the study of projectile motion. Revista Mexicana de Física E, 21, 020217. https://doi.org/10.31349/revmexfise.21.020217

Nasution, B. (2023). Basic mechanics of Lagrange and Hamilton as reference. Journal of Physics: Conference Series, 2920, 012345. https://doi.org/10.1088/1742-6596/2920/1/012345

Nickl, R. W., Daniels, G. L., & Sternad, D. (2019). Complementary spatial and timing control in rhythmic arm tasks. Journal of Neurophysiology, 121(3), 1027–1042. https://doi.org/10.1152/jn.00194.2018

Nie, J.-M., Liu, X.-B., & Zhang, X.-L. (2024). An integrated Lagrangian modeling method for mechanical systems with memory elements. Machines, 12(3), 208. https://doi.org/10.3390/machines12030208

Putnam, C. A. (1993). Sequential motions of body segments in throwing skills. Journal of Biomechanics, 26(1), 125–135. https://doi.org/10.1016/0021-9290(93)90084-R

Said, A. A., Mbewe, H. P., Mgimba, M. M., Namanolo, H. S., Rashid, S. M., & Ussi, S. (2025). Mass-dependent computational analysis of projectile motion under quadratic air drag using the Runge–Kutta method. Open Journal of Applied Sciences, 15, 4023–4042. https://doi.org/10.4236/ojapps.2025.1512260

Topman, N. N., Mbah, G. C. E., & Asor, V. E. (2025). Mathematical model on dimensional analysis of stratified deep water equations under modified gravity and Coriolis effect to obtain Reynolds number. Journal of the Nigerian Association of Mathematical Physics, 71(627), 627. https://doi.org/10.60787/jnamp.vol71no.627

Yamamoto, K. (2020). Attractor stability in coordination patterns of expert jugglers. Scientific Reports, 10, Article 60066. https://doi.org/10.1038/s41598-020-60066-7

Yamamoto, K., Mitoma, H., & Okada, M. (2021). Differences in anchoring strategy underlie coordination during three-ball juggling tasks. Human Movement Science, 78, Article 102678. https://doi.org/10.1016/j.humov.2021.102678

Zago, M., & Lacquaniti, F. (2017). Multi-segmental movement patterns reflect rhythmic coordination in juggling. Human Movement Science, 51, 111–122. https://doi.org/10.1016/j.humov.2017.01.002

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Published

2026-03-28

Data Availability Statement

the data is available

How to Cite

Mathematical Model on Impact of Reynolds Number on Cascading Balls. (2026). International Journal of Development Mathematics (IJDM), 3(1), 116-124. https://doi.org/10.62054/ijdm/0301.09