TL;DR
This paper introduces a novel, simulation-free method to determine the RVE size of microstructures using Fisher scores, relying solely on micrograph analysis, thus saving computational resources in multiscale modeling.
Contribution
It proposes a new approach that estimates RVE size based on micrograph data and nonstationarity detection, eliminating the need for extensive FE simulations.
Findings
The method accurately identifies RVE sizes consistent with FE property convergence.
It reduces computational cost by avoiding multiple FE simulations.
The approach works for both simple and complex microstructures.
Abstract
A representative volume element (RVE) is a reasonably small unit of microstructure that can be simulated to obtain the same effective properties as the entire microstructure sample. Finite element (FE) simulation of RVEs, as opposed to much larger samples, saves computational expense, especially in multiscale modeling. Therefore, it is desirable to have a framework that determines RVE size prior to FE simulations. Existing methods select the RVE size based on when the FE-simulated properties of samples of increasing size converge with insignificant statistical variations, with the drawback that many samples must be simulated. We propose a simulation-free alternative that determines RVE size based only on a micrograph. The approach utilizes a machine learning model trained to implicitly characterize the stochastic nature of the input micrograph. The underlying rationale is to view RVE…
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