Adam Ihusan, Bagnoli Franco, Fanelli Duccio, Mahadevan L, Paoletti Paolo
Department of Information Engineering, University of Florence, Florence 50019, Italy.
Department of Physics and Astronomy, and CSDC, University of Florence, Sesto Fiorentino 50019, Italy.
Phys Rev E. 2022 Jun;105(6-2):065002. doi: 10.1103/PhysRevE.105.065002.
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 [one-dimensional (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.
预约束弹性网络出现在许多生物和技术系统中,从细胞的细胞骨架到张拉整体结构。受此观察结果的启发,我们在此考虑一维的最小模型,为理解此类网络作为预应变函数的响应奠定基础。为此,我们考虑一个由弹性弹簧组成的链(一维(1D)网络),在其上施加随机的、零均值、有限方差的预应变。数值模拟和分析预测量化了收缩量作为预应变方差的函数,并表明链总是收缩的。为了检验这些预测,我们改变链的拓扑结构,考虑更复杂的连通性,并表明我们的结果对这些变化相对稳健。