Oct 2023
Abstract
Mechanical design is non-intuitive. Despite our years of experience, the complex behavior of physical systems, coupled with our limited understanding, can result in unexpected or even extreme optimal geometries (for example, the bulbous bow of a ship). However, finding these non-intuitive optimal designs is often impossible due to the computational/experimental expense of evaluating the optimization objectives. In addition, the geometric constraints and designer biases imposed by conventional parameterization methods can restrict the designs to a particular design space, and make design space expansion to include non-intuitive designs impossible.
My research addresses the twin difficulties of constrained design spaces and limited computational/ experimental budgets in a two-pronged approach: (1) Design-by-Morphing (DbM), a novel strategy for creating a continuous and constraint-free design search space by morphing homeomorphic shapes, that can produce radical extrapolated shapes, something which is unique from existing design strategies; and (2) a first of its kind Mixed-variable, Multi-Objective Bayesian Optimization (MixMOBO) algorithm, that can optimize expensive, black-box problems with a small number of functions calls. In tandem, these algorithms produce a powerful DbM-MixMOBO design framework for creating novel optimal designs for a wide range of expensive engineering design problems, and have made previously intractable problems possible to optimize. These methodologies have been utilized in designing architected meta-materials, optimizing the shapes of airfoils, draft-tubes, and ribbed-channel designs. In all instances, these approaches have resulted in non-intuitive groundbreaking designs which exhibit considerable enhancements.
Bio
I am a post-doctoral researcher at the CFD Lab at University of California, Berkeley. Originally from Pakistan, I got my Bachelor’s degree from University of Engineering and Technology, Lahore in 2016. Under the guidance of Dr. Philip Marcus, I earned my Masters and PhD in Mechanical Engineering at the University of California, Berkeley in 2022 while on a Fulbright Scholarship. My research interests are on creating comprehensive end-to-end data-driven design optimization methods applicable to real-world engineering problems, crowd-flow simulations, and wind turbines. In my spare time, I enjoy going on hikes and taking in the spectacular sunsets over Berkeley.