- Title Information
- Title
- Big-Data-Driven Multi-Scale Experimental Study of Nanostructured Block Copolymer’s Dynamic Toughness
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Jin, Hanxun
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Kim, Kyung-Suk
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Gao, Huajian
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Guduru, Pradeep R.
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Karniadakis, George Em
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Engineering: Mechanics of Solids
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2022
- Physical Description
- Extent
- XVIII, 123 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2022
- Genre (aat)
- theses
- Abstract
- In this thesis, we studied the dynamic toughening mechanisms of a hierarchically nanostructured copolymer, polyurea. We first quantitively measured the dynamic fracture toughness as well as the cohesive parameters of polyurea under an extremely high crack-tip loading rate, from a deep-learning analysis of a big-data-generating experiment. We invented a novel Dynamic Line-Image Shearing Interferometer (DL-ISI), which can generate the displacement-time profiles along a line on a sample covering the crack initiation and growth process in a single plate impact experiment. Then, we proposed a convolutional neural network (CNN) based deep-learning framework that can inversely determine the accurate cohesive parameters from DL-ISI fringe images. Plate-impact experiments on a polyurea sample with a mid-plane crack have been performed, and the generated DL-ISI fringe image has been inpainted by a Conditional Generative Adversarial Networks (cGAN). For the first time, the dynamic cohesive parameters of polyurea have been successfully obtained by the pre-trained CNN architecture with the computational dataset. Apparent dynamic toughening is found in polyurea, where the cohesive strength is found to be nearly three times higher than the spall strength under the symmetric impact with the same impact speed. Furthermore, we chased a molecular-level understanding of polyurea’s dynamic toughening through in-situ AFM experiments and mesoscale simulations. For this purpose, we designed and manufactured a novel in-situ AFM loading device with invariant observation sight. Using this device, we collected high-resolution in-situ AFM tapping-mode phase images of polyurea under stress-relaxation at various fixed strains ranging from 0% to 260%. From the in-situ AFM and coarse-grained MD simulation study, we found the hard domain fragmentation process promotes ductility of the copolymer. Furthermore, the hard domain fragmentation process helps maintain the high crack-growth-incubation toughness under high-strain-rate extreme loading. The relaxation mechanism further provides unusually high dynamic running-crack toughness. These experimental results fill the gap in the current understanding of copolymer’s cooperative failure strength under extreme local conditions near the crack tip.
- Subject
- Topic
- Solid Mechanics
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01032630")
- Topic
- Nanostructured materials
- Subject
- Topic
- Deep Learning
- Subject
- Topic
- Experimental mechanics
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20220118