Data-Driven Multi-scale Modeling and Robust Optimization of Composite Structure with Uncertainty Quantification

Kazuma Kobayashi, Shoaib Usman, Carlos Castano, Ayodeji Alajo, Dinesh Kumar, Susmita Naskar and Syed Alam ()
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Kazuma Kobayashi: Missouri University of Science and Technology
Shoaib Usman: Missouri University of Science and Technology
Carlos Castano: Missouri University of Science and Technology
Ayodeji Alajo: Missouri University of Science and Technology
Dinesh Kumar: Missouri University of Science and Technology
Susmita Naskar: University of Southampton
Syed Alam: Missouri University of Science and Technology

A chapter in Handbook of Smart Energy Systems, 2023, pp 1333-1343 from Springer

Abstract: 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 multiscale 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 mesoscale uncertainties. It is also possible for macroscale 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 multiscale modeling of advanced composite materials. This chapter proposes a multiscale modeling method for composite structures based on a finite element method (FEM) simulation driven by surrogate models/emulators based on microstructurally informed mesoscale 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.

Keywords: Multiscale modeling; Robust optimization; Composite material; Sensitivity analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_207

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DOI: 10.1007/978-3-030-97940-9_207

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