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Published on: April 26, 2019
Memory effects in spiral diffusion of rotary self-propellers
Parvin Bayati1,2, Amir Nourhani1,3,4
1Department of Mechanical Engineering, University of Akron, Akron, Ohio 44325, USA.
Self-propelled particle motion, combining rotation and diffusion, creates spiral paths. This study analyzes spiral diffusion under realistic noise conditions, revealing diverse dynamic behaviors based on key physical parameters.
Area of Science:
- Physics
- Statistical Mechanics
- Soft Matter
Background:
- Self-propelled particles exhibit complex trajectories due to coupled rotational and translational motion.
- Previous work analyzed spiral diffusion in the white-noise limit.
Purpose of the Study:
- To extend spiral diffusion analysis to more realistic noise models.
- To investigate the impact of Gaussian memory and Ornstein-Uhlenbeck processes on self-propeller dynamics.
Main Methods:
- Mathematical modeling of self-propeller motion.
- Analysis of stochastic processes with Gaussian memory.
- Incorporation of Ornstein-Uhlenbeck processes for orientational fluctuations.
Main Results:
- The coupling of deterministic rotation and stochastic diffusion results in spiral trajectories.
- A variety of dynamical regimes are observed, including crossovers in angular dynamics.
- These regimes are dependent on inertial timescale, orientational diffusivity, and angular speed.
Conclusions:
- The study provides a more realistic framework for understanding spiral diffusion.
- The findings highlight the influence of noise characteristics on particle dynamics.
- This work contributes to the understanding of active matter systems.
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