Venue: Weiser Hall, 6th Floor, 619
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Hari’s research interests include decarbonization of the power sector, climate impacts, computational modeling, and sustainable transformation. His current research in the center for sustainable systems is focused on predicting changes in the energy system — meteorology interaction with a transition to widespread renewable energy generation. He aspires to use this to inform long term planning of reliable power systems under a changing climate while ensuring a just transition.
Increasing meteorological extremes and renewable penetrations could challenge resource adequacy (RA) in the electric power system, as demonstrated by recent blackouts in California and Texas. We quantify meteorological drivers of RA in the Western U.S. power system, and examine how these drivers change with increasing renewable penetrations. Our analysis integrates an optimization-based capacity expansion model, stochastic RA model, and neural-network-based self-organizing maps. We find that RA failures are driven by high pressure circulation patterns which produce positive surface temperature anomalies and negative solar radiation and wind speed anomalies. Further, with increasing renewable penetration we find that the probability of failure attributed to patterns associated with heat waves over the region increases.
Vishwas is a Ph.D. candidate in the Department of Materials Science and Engineering. His research is primarily focused on simulating electrochemical phenomena on multiple scales.
Magnesium and its alloys are the lightest structural metallic materials known, and therefore, hold vast potential for reducing the weight for various transportation modes such as airplanes, cars, buses, etc. Although the alloying of Mg with elements such as Al, Mn, and rare earth (RE) elements is known to improve the mechanical properties of Mg, the process is often detrimental to the corrosion performance of Mg. This increase in the corrosion rate occurs because of the micro-galvanic couple that forms between the Mg-rich phase, which acts as an anode, and the alloying-element-rich phase, which acts as a cathode.
Using both experiments and modeling, it has been reported that the rate of micro-galvanic corrosion in the Mg-alloys depends on the alloying element and microstructure. However, a deeper understanding is required for quantifying the effect of microstructure characteristics such as the fraction of the two phases, spacing between the two phases, the geometry of the two phases, etc., on the corrosion rate. This understanding is crucial for designing Mg-alloys with optimal mechanical properties and high corrosion resistance.
To bridge this gap in our understanding, we perform the continuum-scale phase-field modeling of different microstructures observed in Mg-alloys. Furthermore, we complement the modeling work with theoretical analysis, where we develop analytical relations for studying the effect of various material and microstructural parameters on the characteristic corrosion length scale. The results from both these efforts will be summarized in our presentation.
This event is part of MICDE’s Fall 2022 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDEfirstname.lastname@example.org