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Speaker: Egemen Kolemen, Princeton University
Title: Overview of Fusion Research at Plasma Control Group
Abstract: Fusion promises to be the ultimate green energy source of the future, as it is abundant, clean, and greenhouse-emission free, without the intermittency and location restrictions of solar and wind energy or fission’s safety and waste issues. While our current knowledge of plasma physics and technical capabilities is sufficiently mature for us to attempt to build fusion power reactors, the path to economic competitiveness lies with compact, high-energy-density fusion reactors. This requires operation at, simultaneously, physics parameters that are close to the edge of plasma instabilities and the technical possibilities of materials engineering and nuclear operation, which is challenging.
Plasma Control Group at Princeton University try to tackle these challenges with a practical engineering perspective that incorporates relevant insights from plasma physics. I will talk about some of our recent accomplishments: 1) DESC, a fast and accurate stellarator code suit for solving equilibria and optimizing 2) Divertorlets, a non-evaporative liquid metal divertor that allows low recycling plasma regime that may allow high temperature from the plasma core to the wall 3) Application of machine learning for real-time fusion plasma behavior prediction and manipulation 4) What does the optimization studies tell us about the fusion reactor design for the future electric grid dominated by renewables?
Bio: Egemen Kolemen in an Associate Professor at Princeton University’s Mechanical & Aerospace Engineering jointly appointed with the Andlinger Center for Energy and the Environment and the Princeton Plasma Physics Laboratory (PPPL). He is the Director of Program in Sustainable Energy, recipient of the David J. Rose Excellence in Fusion Engineering Award and an ITER Scientist Fellow. His research combines engineering and physics analysis to enable economically feasible fusion reactors. He currently leads research on machine learning, real-time diagnostics and control at KSTAR, NSTX-U and DIII-D. He directs liquid metal divertor and low temperature diagnostics labs. On the theoretical side, his group develops software for stellarator optimization and economical analysis of fusion reactor.