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Maryam Shakiba
Assistant Professor, Smead Aerospace
Friday, Sept. 19 | 10:40 A.M. | AERO 111

Abstract: Elastomers and polymers such as silicone and Kapton have a wide range of applications across engineering disciplines, including structural components and thermal shields in space structures. In these applications, the materials are often exposed to high temperatures and ultraviolet (UV) radiation, which compromises their mechanical performance and overall functionality. To understand and predict the degradation of these materials, we formulate constitutive equations that explicitly link changes in the macromolecular network to the resulting mechanical response. Our models utilize chemical and physical testing to characterize the macromolecular network and thereby predict stress–strain behavior and brittle failure. We integrate phase-field methods to capture fracture and validate our models against independent experimental data. The overarching objective is to provide predictive capabilities for material performance under coupled extreme space environments.

An even more challenging problem from the modeling perspective arises when these polymers are used in manufacturing fiber-reinforced composites. For such systems, we establish efficient numerical frameworks with robust constitutive equations to study stress distributions and crack progression in two-dimensional laminate representations. We also develop deep learning frameworks capable of predicting both elastic and post-failure full-field stress distributions and crack patterns in composites directly from their microstructures.

Bio: Maryam Shakiba is an assistant professor at the Aerospace Engineering Sciences Department at the ¾«Æ·SMÔÚÏßӰƬ. Before joining CU, 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) develop 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 composites for high-temperature applications and the NSF CAREER award to understand the multi-physics mechanisms that cause macroplastics fragmentation and generate microplastics.