About this Abstract |
Meeting |
2024 ASC Technical Conference, US-Japan Joint Symposium, D30 Meeting
|
Symposium
|
2024 ASC Technical Conference, US-Japan Joint Symposium, D30 Meeting
|
Presentation Title |
Multiscale simulation of failure in fiber composites |
Author(s) |
Shabnam Semnani |
On-Site Speaker (Planned) |
Shabnam Semnani |
Abstract Scope |
Composite materials are multi-scale in nature. Their macroscopic failure behavior depends on microscopic features such as properties, shape, and distribution of the constituents. One of the major challenges associated with multi-scale modeling of composites includes computational costs of high-fidelity simulations and transferring the information across different length scales. Consequently, design and analysis of these systems often involves phenomenological continuum-scale constitutive models that are implemented in a computational framework such as the Finite Element Method (FEM). Recently, machine learning-based techniques have emerged as a promising alternative to the traditional constitutive models and FE simulations. In this work, we present the recent advances in machine learning-enhanced modeling of multi-scale mechanical behavior of composites and their applications in capturing material failure. Simulation results will be validated using experimental mechanical testing results and conventional methods in the literature. The proposed framework will be applied to study the impact of constituents on the failure mechanisms of composite materials. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |