Disordered origins, deterministic outcomes: How the architecture of elastic networks imprints relaxed structure and mechanics

TL;DR

This paper investigates how different types of disorder in elastic networks influence their relaxed structures and mechanical properties, revealing that structural differences can be inferred from experimental measures.

Contribution

It introduces a method to classify elastic networks based on disorder types and links microstructural features to macroscopic behavior.

Findings

Relaxed structures vary significantly with disorder type.

Experimental measures can infer microstructural details.

Different disorder classes lead to distinct mechanical responses.

Abstract

This work targets the influence of disorder on the relaxed structure and macroscopic mechanical properties of elastic networks. We construct network classes of different types of disorder (length, topology and stiffness), which are subsequently equilibrated in a finite kinematics setting. Relaxed network structures are distinct among network classes, which opens the path towards exploiting easily accessible experimental measures as a way of inferring further microstructural details.