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

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Handbook of Smart Energy Systems

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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.

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Acknowledgement

The computational part of this work was supported in part by the National Science Foundation (NSF) under Grant No. OAC-1919789.

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Authors and Affiliations

  1. Department of Nuclear Engineering and Radiation Science, Missouri University of Science and Technology, Rolla, MO, USA

    Kazuma Kobayashi, Shoaib Usman, Carlos Castano, Ayodeji Alajo, Dinesh Kumar & Syed Alam

  2. Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK

    Susmita Naskar

  3. Department of Mechanical Engineering, University of Bristol, Bristol, UK

    Dinesh Kumar

Authors
  1. Kazuma Kobayashi
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  2. Shoaib Usman
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  3. Carlos Castano
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  4. Ayodeji Alajo
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  5. Dinesh Kumar
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  6. Susmita Naskar
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  7. Syed Alam
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Corresponding author

Correspondence to Syed Alam .

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Editors and Affiliations

  1. University of North Texas, Denton, TX, USA

    Michel Fathi

  2. Department of Energy, Politecnico di Milano, MILANO, Milano, Italy

    Enrico Zio

  3. Industrial & Systems Engineering, University of Florida, GAINESVILLE, FL, USA

    Panos M. Pardalos

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Kobayashi, K. et al. (2023). Data-Driven Multiscale Modeling and Robust Optimization of Composite Structure with Uncertainty Quantification. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-72322-4_207-1

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  • DOI: https://doi.org/10.1007/978-3-030-72322-4_207-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72322-4

  • Online ISBN: 978-3-030-72322-4

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