Seminar: Design and Analysis of Soft Structures for Underwater Applications

Leixin Ma, Ph.D.
Assistant Professor
Arizona State University
Leixin.Ma@asu.edu

*Zoom only*
Meeting ID: 963 5962 3640
Passcode: OREseminar
https://hawaii.zoom.us/j/96359623640

Traditional engineering structures are made of rigid structures with simple geometry, such as ship hulls and wind turbine blades. In contrast, recent advancements in manufacturing open the door to designing and manufacturing soft structures with programmable and unconventional functionalities. An optimal and efficient design of these soft structures interacting with external loads, such as fluids, remains challenging. In this talk, I will discuss how a combined physics-based modeling and machine-learning approach can help tackle the challenges.
The key challenges involve the extraordinarily large structural design space and large structural deformation that are not amenable to conventional analytical tools. I will introduce a symmetry-constrained machine learning technique, combining Variational Autoencoder and Bayesian Optimization, to design soft composite shells of targeted functionalities. These soft composites – without rigid springs and hinges – are manufactured by combining kirigami and pre-stretch and can be scalably fabricated on a 2D plane. Despite fully planar fabrication, they can be programmed to assume a prescribed 3D shape without any external stimulus. We extended the design to structures with multistability. We demonstrate its effectiveness using soft grippers that can gently grasp delicate objects of different shapes and sizes. We also find additional design factors, such as the Cauchy number, need to be considered when these soft bistable structures are interacting with fluid flows. A prediction model for the dimensionless strain energy as a function of the Cauchy number is proposed.

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