Kim, Sung Eun | Engineering Papers

DOE OSTI2022

Strengthening of nanocrystalline Al using grain boundary solute additions: Effects of thermal annealing and ion irradiation

Strengthening of nanocrystalline Al by grain boundary solute additions was investigated for a series of dilute aluminum alloys, Al-Sc, Al-Sb, Al-Cr, and Al-W with grain sizes in the range of 50–200 nm. Thermal annealing of the alloys at low temperatures led to alloy softening, but with negligible change in the grain size. The re- duction in strength can be attributed to the loss of solute in the grain boundaries arising from grain boundary diffusion and precipitation. Annealing at higher temperatures led to grain growth, but with little additional loss of strength, a result of precipitation hardening. The Al-Sc and Al-Sb alloys were additionally subjected to ion irradiation at various temperatures. Furthermore, these studies revealed that annealed samples regained their hardness due to solute redistribution by ion beam mixing. Alloy strength was independent of grain size between 50 and 150 nms. Irradiation-induced segregation of Sb to grain boundaries in Al-Sb further enhanced strengthening.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Fast and scalable earth texture synthesis using spatially assembled generative adversarial neural networks

Not Available

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Connectivity-informed drainage network generation using deep convolution generative adversarial networks

Abstract Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb’s model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.

42 ENGINEERING↗