Computational Design of Materials and Processes for Manufacturing and Energy Applications (04/05/21)

2 minute read

Speaker and Affliation:

Dr. Pikee Priya
Postdoctoral Research Associate
Department of Mechanical Science and Engineering
University of Illinois
Urbana-Champaign, IL, United States


04th May, 2021 (Tuesday), 04:00 PM (India Standard Time)


Microsoft Teams Meet-up


Despite the remarkable progress made in the areas of advanced manufacturing and energy technologies, there still exist roadblocks to ‘efficient processing’ along with ‘excellent functional performance’ of materials, making systematic design of both materials and processes a necessity. The understanding of the material physics embodied by chemistryprocessing-structure-property correlation falls short due to the rapid developments in these fields. In this talk, I will discuss microstructural evolution during advanced manufacturing processes like Selective Laser Melting (SLM). Processing parameters during laser based additive manufacturing affect the fluid flow, heat transfer, and solidification characteristics in the melt pool, leading to microstructural variants of texture, grain size, and morphology. A finite volume based Computational Fluid Dynamics model coupled with solidification physics has been developed to predict melting, flow, solidification, and resulting microstructural characteristics during such processes. The variation of texture, grain size and columnar/equiaxed morphology of the grains with laser power and speed has been verified against experiments. The difference in cooling curves and evolution of temperature gradients and cooling rates during solidification leading to a difference in each microstructural variant has been identified. Lower laser power and higher scan rates lead to “unconstrained” solidification with small variation of solidification times across the melt pool depth leading to finer grain structure with lower grain boundary misorientations. On the other hand, higher power and lower scan rates lead to “constrained” solidification, with huge variations in solidification times, coarser grains and highly misoriented grain boundaries. Hunt’s criterion-based processing maps for understanding the morphological variants has been prepared for systematic design of microstructure during the SLM process. In the next part, I will briefly present my work on a robust multiscale computational approach to design electrochemical materials for Solid Oxide Fuel/Electrolyzer Cells (SOF/EC). The depletion of fossil fuels and rapidly deteriorating ‘global warming’ situation with climate change has created a demand for the greener and renewable energy conversion and storage technologies. Understanding the electrochemical characteristics of materials used for these technologies is the first step towards material design. The prediction of electrochemical characteristics for protonconducting Yttrium doped Barium Zirconate based SOF/EC, using a multiscale framework combining physics at the atomistic, meso, and continuum scales will be discussed. I will also talk about uncertainty quantification and machine learning techniques such as neural networks and random forest to optimize chemistry for different temperatures and atmospheres for pure/doped perovskites. These projects provide new scientific insights and demonstrate the possible application of the introduced methodology for virtual certification of the next generation of high-performance materials and manufactured products.