Multiscale Surrogate Modeling and Uncertainty Quantification for Periodic Composite Structures
Charilaos Mylonas, Valentin Bemetz, Eleni Chatzi

TL;DR
This paper introduces a non-intrusive surrogate modeling approach to efficiently connect macro-scale structural behavior with micro-scale properties in periodic composite structures, significantly reducing computational costs in uncertainty quantification.
Contribution
It presents a novel surrogate modeling method that bypasses the need for costly PDE solutions for elastic correctors in multiscale analysis of composites.
Findings
Reduces computational cost of uncertainty quantification in micro-structured composites
Enables efficient incorporation of damage and fatigue effects in simulations
Provides accurate macro-micro scale linkage with fewer micro-scale evaluations
Abstract
Computational modeling of the structural behavior of continuous fiber composite materials often takes into account the periodicity of the underlying micro-structure. A well established method dealing with the structural behavior of periodic micro-structures is the so- called Asymptotic Expansion Homogenization (AEH). By considering a periodic perturbation of the material displacement, scale bridging functions, also referred to as elastic correctors, can be derived in order to connect the strains at the level of the macro-structure with micro- structural strains. For complicated inhomogeneous micro-structures, the derivation of such functions is usually performed by the numerical solution of a PDE problem - typically with the Finite Element Method. Moreover, when dealing with uncertain micro-structural geometry and material parameters, there is considerable uncertainty introduced in the…
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