Prestraininduced contraction in onedimensional random elastic chains
Abstract
Prestrained elastic networks arise in a number of biological and technological systems ranging from the cytoskeleton of cells to tensegrity structures. Motivated by this observation, we here consider a minimal model in one dimension to set the stage for understanding the response of such networks as a function of the prestrain. To this end we consider a chain [onedimensional (1D) network] of elastic springs upon which a random, zero mean, finite variance prestrain is imposed. Numerical simulations and analytical predictions quantify the magnitude of the contraction as a function of the variance of the prestrain, and show that the chain always shrinks. To test these predictions, we vary the topology of the chain, consider more complex connectivity and show that our results are relatively robust to these changes.
 Publication:

Physical Review E
 Pub Date:
 June 2022
 DOI:
 10.1103/PhysRevE.105.065002
 arXiv:
 arXiv:2110.11049
 Bibcode:
 2022PhRvE.105f5002A
 Keywords:

 Condensed Matter  Statistical Mechanics;
 Condensed Matter  Disordered Systems and Neural Networks;
 Nonlinear Sciences  Adaptation and SelfOrganizing Systems
 EPrint:
 doi:10.1103/PhysRevE.105.065002