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Maryam Shakiba
Assistant Professor, Smead Aerospace
Friday, Sept. 15 | 10:40 a.m. | AERO 120

Abstract: This presentation discusses computational modeling of complex materials behaviors under multi-physics conditions for Aerospace applications. We develop chemistry, physics, and mechanics-based constitutive equations to explain complex systems. Then, we devise high-fidelity numerical ap-proaches and mechanistic machine learning to solve our problems.

The first part of the presentation focuses on progressive damage in fiber-reinforced composites. In such composites, cracks initiate around the fibers aligned transversely to the loading direction. The transverse cracks can cause leakage in specific applications or progress to inter-ply delamination and catastrophic failure. We integrate an efficient numerical framework with robust and accurate constitutive equations to study transverse behavior and multiple cracking of two-dimensional representations of fiber-reinforced composite laminates. We then develop deep learning frameworks to predict the elastic and post-failure full-field stress distribution and the crack pattern in two-dimensional representations of the composites based on their microstructures.

The second part of the presentation focuses on developing chemistry, physics, and mechanics-based constitutive equations to predict the stress and brittle failure responses of polymeric materials under multi-physics degradation. We connect the changes in the macromolecular network of materials due to multi-physics conditioning to their mechanical responses. The changes in the macromolecular network are obtained based on chemical characterization tests. The obtained constitutive equations predict the stress-strain response until failure with phase-field to capture the induced brittle failure. The constitutive equations are verified versus independent mechanical tests available in the literature for different types of materials.

Bio: Maryam Shakiba is an assistant professor at the Aerospace Engineering Sciences Department at the University of Colorado Boulder. Before joining CU Boulder, she was and assistant professor at Virginia Tech and a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign. She received her Ph.D. from Texas A&M University and her B.S. and M.S. degrees from Tehran Polytechnic. Shakiba's group develops physics, chemistry, and mechanics-based constitutive equations to simulate multi-physics conditions for different advanced materials. The group also devises high-fidelity as well as mechanistic machine-learning approaches to solve engineering problems. Our goals are to (1) de-velop theoretical frameworks to understand advanced material responses under extreme multi-factor conditions and (2) integrate the theoretical framework with machine learning approaches, as physics-based machine learning is the key technology to creating true digital twins. Shakiba is the recipient of the AFOSR Young Investigator Program (YIP) award to investigate additively manufactured compos-ites for high-temperature applications and the NSF CAREER award to understand the multi-physics mechanisms that cause macroplastics fragmentation and generate microplastics.