Effective behavior of heterogeneous media governed by strain gradient elasticity

TL;DR

This paper investigates the behavior of heterogeneous media governed by strain gradient elasticity, revealing limitations of classical theories and proposing fractional and neural operator models for better descriptions across scales.

Contribution

It demonstrates that strain gradient elasticity does not capture the overall behavior of microstructured media and introduces fractional and neural operator models as effective alternatives.

Findings

Strain gradient theories are not invariant under averaging at microstructural scale.

Overall behavior can be described by a kernel-based nonlocal elasticity with oscillatory kernels.

Fractional strain gradient elasticity captures behavior across a range of length scales.

Abstract

Various mechanical phenomena depend on the length scale, and these have inspired a variety of nonlocal and higher gradient continuum theories. Mechanistically, it is believed that the length scale dependence arises due to an interplay between the length scale of heterogeneities in the material, the length scale of the material being probed and the phenomenon under study. In this paper, we seek to understand this interplay in a simple setting by studying the overall behavior of a one-dimensional periodic medium governed by strain gradient elasticity at the microstructural scale. We find through numerical experiments that the overall behavior is not described by a strain gradient elasticity. In other words, strain gradient theories are not invariant under averaging at this scale. We also find that the overall behavior may be described by a kernel-based nonlocal elasticity theory, but the kernel is highly oscillatory with slow decay. So we seek alternate characterization. First, we limit our interest to a range of length scales, and show that the behavior is described well by fractional strain gradient elasticity. Consequently, one can obtain various scaling laws with exponent between zero (classical elasticity) and one (strain-gradient elasticity). Second, we take a data-driven approach, and show that we can describe the overall behavior over a range of scales using a Fourier neural operator.