Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification

TL;DR

This paper presents a data-driven multi-scale modeling framework for composite structures, integrating uncertainty quantification and robust optimization to enhance material design and performance prediction across micro, meso, and macro scales.

Contribution

It introduces a novel multi-scale modeling approach using surrogate models and machine learning for composite materials, incorporating uncertainty quantification and optimization.

Findings

Effective surrogate models for microstructural properties.

Robust optimization improves composite material performance.

Uncertainty quantification enhances reliability of predictions.

Abstract

It is important to accurately model materials' properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can significantly reduce the amount of experimentation required to develop new technologies. Robustness analysis of fuel and structural performance for harsh environments (such as power uprated reactor systems or aerospace applications) using machine learning-based multi-scale modeling and robust optimization under uncertainties are required. The fiber and matrix material characteristics are potential sources of uncertainty at the microscale. The stacking sequence (angles of stacking and thickness of layers) of composite layers causes meso-scale uncertainties. It is also possible for macro-scale uncertainties to arise from system properties, like the load or the initial conditions. This chapter demonstrates advanced data-driven methods and outlines the specific capability that must be developed/added for the multi-scale modeling of advanced composite materials. This chapter proposes a multi-scale modeling method for composite structures based on a finite element method (FEM) simulation driven by surrogate models/emulators based on microstructurally informed meso-scale materials models to study the impact of operational parameters/uncertainties using machine learning approaches. To ensure optimal composite materials, composite properties are optimized with respect to initial materials volume fraction using data-driven numerical algorithms.