Announcement Detail

Wednesday, May 6, 2026

1:00PM CDT

Join via Zoom: https://us06web.zoom.us/j/87331120699?pwd=MExkQ085ZHh1aDc3ZnBwQzFlUHI4UT09

Large Scale Structural Systems and Optimal Design Colloquium

Topology Optimization of Compositionally Graded Alloys for Extreme Thermo-Mechanical Environments

Speaker 1

Dr. Aaditya Chandrasekhar, Northwestern University

Abstract

Compositionally Graded Alloys (CGAs) offer unprecedented design flexibility by enabling spatial variations in composition to tailor material properties to local loading conditions. This flexibility leads to components that are stronger, lighter, and more cost-effective than traditional monolithic counterparts. This capability is particularly advantageous for components subjected to large thermo-mechanical loads, such as turbine blades, where the selective placement of critical materials like high-temperature alloys is crucial for achieving optimal performance and cost efficiency. To address the computational challenges associated with large-scale problems, this talk details the underlying high-performance differentiable simulation frameworks developed to model these complex systems. By leveraging scalable solvers and automatic differentiation, the presentation will demonstrate optimization methodologies that efficiently generate designs resistant to thermomechanical creep while strictly accounting for manufacturing and gradation constraints.

Bio

Aaditya Chandrasekhar is a postdoctoral researcher working with Prof. Wei Chen in the mechanical engineering department at Northwestern University. His research interests include machine learning for computational design, topology optimization, differentiable simulation, and computational mechanics. Prior to his current role, he was a postdoctoral researcher at Argonne National Laboratory. He received his PhD in mechanical engineering from the University of Wisconsin-Madison.

Gradient-based Design Optimization for Structural Dynamics

Speaker 2

Dr. Brianna Macnider, Lawrence Livermore National Laboratory

Abstract

Transient, gradient-based design optimization is central to rate-dependent phenomena and wave-dominated applications, such as impact mitigation and vibration problems, yet it remains a challenging optimization problem. Explicit dynamic time integration schemes are well suited for short-duration events, but their small stable time steps can require thousands of steps within a single optimization iteration. This talk discusses key challenges and lessons learned in formulating topology and shape optimization problems for structural dynamics. We also discuss considerations for the computational cost associated with large numbers of time steps in even relatively low degree of freedom 2D problems and consider how these challenges are expected to scale to larger 3D systems.

Bio

Brianna is a postdoctoral researcher at Lawrence Livermore National Laboratory and a recipient of the Laboratory Residency Graduate Fellowship. She received her Ph.D. in Aerospace Engineering from the University of California, San Diego in 2024. Her current research centers on design optimization for a variety of applications, including structural dynamics, mechanical logic, and smart materials.