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Deep learning informed multiphysics material modeling of fiber reinforced polymer nanocomposites

Publikation: Qualifikations-/Studienabschlussarbeit › Dissertation

Autorschaft

  • Betim Bahtiri

Organisationseinheiten

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OriginalspracheEnglisch
QualifikationDoktor der Ingenieurwissenschaften
Gradverleihende Hochschule
Betreut von
Datum der Verleihung des Grades5 Nov. 2024
ErscheinungsortHannover
PublikationsstatusVeröffentlicht - 13 Nov. 2024

Abstract

The development of high-performance fiber-reinforced polymer nanocomposites is a major focus in the field of lightweight materials for engineering applications due to their improved mechanical properties. However, the complex interactions between nanoparticles, fibers, and polymeric matrices pose a significant challenge in accurately and comprehensively understanding the multiphysics behavior of the nanocomposites, which is essential for reliably developing material models. The thesis at hand contributes to this challenge by providing an enhanced and deep learning-informed framework for multiphysics material modeling of fiber-reinforced polymer nanocomposites, combining molecular simulations, nonlinear finite element simulations, and deep learning techniques. The framework is designed to capture the intricate structure-property relationships of these materials, including the effects of moisture, temperature, and nanoparticle incorporation on material behavior.

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Deep learning informed multiphysics material modeling of fiber reinforced polymer nanocomposites. / Bahtiri, Betim.
Hannover, 2024. 122 S.

Publikation: Qualifikations-/Studienabschlussarbeit › Dissertation

Bahtiri, B 2024, 'Deep learning informed multiphysics material modeling of fiber reinforced polymer nanocomposites', Doktor der Ingenieurwissenschaften, Gottfried Wilhelm Leibniz Universität Hannover, Hannover. https://doi.org/10.15488/18108
Bahtiri, B. (2024). Deep learning informed multiphysics material modeling of fiber reinforced polymer nanocomposites. [Dissertation, Gottfried Wilhelm Leibniz Universität Hannover]. https://doi.org/10.15488/18108
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