Model-Free Data-Driven Viscoelasticity in the Frequency Domain
arXiv:2205.06674 · doi:10.1016/j.cma.2022.115657
Abstract
We develop a Data-Driven framework for the simulation of wave propagation in viscoelastic solids directly from dynamic testing material data, including data from Dynamic Mechanical Analysis (DMA), nano-indentation, Dynamic Shear Testing (DST) and Magnetic Resonance Elastography (MRE), without the need for regression or material modeling. The problem is formulated in the frequency domain and the method of solution seeks to minimize a distance between physically admissible histories of stress and strain, in the sense of compatibility and equilibrium, and the material data. We metrize the space of histories by means of the flat-norm of their Fourier transform, which allows consideration of infinite wave trains such as harmonic functions. Another significant advantage of the flat norm is that it allows the response of the system at one frequency to be inferred from data at nearby frequencies. We demonstrate and verify the approach by means of two test cases, a polymeric truss structure characterized by DMA data and a 3D soft gel sample characterized by MRE data. The examples demonstrate the ease of implementation of the Data-Driven scheme within conventional commercial codes and its robust convergence properties, both with respect to the solver and the data.
Cited by in corpus (6)
- Automated identification of linear viscoelastic constitutive laws with EUCLID
- Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
- Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials
- Data-driven methods for computational mechanics: A fair comparison between neural networks based and model-free approaches
- Data-driven numerical site response
- Mesh d-refinement: a data-based computational framework to account for complex material response