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Updated: Nov 28, 2025

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions
Published on: February 22, 2018
Periodic training of creeping solids.
Daniel Hexner1,2,3,4, Andrea J Liu4, Sidney R Nagel5,2,3
1Department of Physics, University of Chicago, Chicago, IL 60637; danielhe@me.technion.ac.il.
We demonstrate that periodic driving can train disordered solids to exhibit desired elastic properties. This method controls system responses even at large strains by exploiting material plasticity.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Nonlinear Dynamics
Background:
- Disordered solids exhibit complex mechanical behaviors due to microscopic element plasticity.
- Controlling elastic properties in these materials, especially at large strains, is challenging.
- Existing design methods struggle in the nonlinear regime where plasticity dominates.
Purpose of the Study:
- To investigate the use of periodic driving to control elastic properties in disordered solids.
- To explore the exploitation of material plasticity for tunable mechanical responses.
- To demonstrate control over both global moduli and local allosteric interactions.
Main Methods:
- Applying periodic driving to disordered solid systems.
- Analyzing the energy landscape and strain coupling during periodic driving.
- Characterizing the resulting elastic properties, including global and local interactions.
Main Results:
- Periodic driving successfully trains specific elastic properties into disordered solids.
- The method allows precise control over global elastic moduli.
- Local 'allosteric' interactions can also be tuned, demonstrating fine-grained control.
- Effective control is achieved even at large strains within the nonlinear regime.
Conclusions:
- Periodic driving offers a novel approach to engineer the mechanical properties of disordered solids.
- Exploiting plasticity through periodic excitation provides a powerful tool for materials design.
- This technique enables predictable control over complex material responses in the nonlinear regime.
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