ekolemen at princeton.edu or pppl.gov
Egemen’s research focuses on the application of dynamics and control theory to experimental plasma physics, primarily to address the challenges of fusion reactor design. He analyzes the dynamics of complex plasma phenomena using applied mathematics and dynamics theory with the aim of designing and implementing novel solutions to fusion problems.
Jaemin is a postdoc working on machine learning based plasma prediction and control in KSTAR and DIII-D. He got a Ph.D. degree from the department of nuclear engineering at Seoul National University. He developed a feedforward plasma control algorithm using a reinforcement learning technique in KSTAR during his Ph.D., and is now mainly working on the tearing mode prediction and control algorithm in DIII-D. He is also interested in a fast surrogate neural network model for the kinetic equilibrium reconstruction for a real-time implementation.
SangKyeun is a post-doc who majored in pedestal instability and nonlinear MHD simulation. During his Ph.D. course at Seoul National University, he worked on linear PBM stability analysis on JET, EURO-DEMO, and KSTAR using MISHKA and ELITE. He also studied nonlinear physics in ELMy heat flux and RMP driven ELM suppression with 3D MHD code, JOREK. In addition, he is the main developer of the kinetic-EFIT reconstruction system in KSTAR. He is now mainly working on adaptive RMP-ELM controller, mechanism of RMP-driven ELM suppression, and other 3D-ELM physics. Google scholar
J. A. Schwartz
Jacob Schwartz is a postdoc working to understand what sort of fusion reactors could be economically attractive as part of the electric grid, and more broadly, for industry and energy systems, in 2050 and beyond. He is also working on a “systems code” to generate and optimize candidate fusion reactors for various missions.
Sun is a postdoc of Princeton Plasma Physics Laboratory, working to develop liquid metal plasma-facing component in the divertor and/or wall of a fusion system. Using the LMX (Liquid Metal eXperiment) planform and simulation, Sun is studying on several key issues for fast liquid metal flow, such as velocity measurement, MHD, stability of open channel flow, tritium removal, and so on. Sun is also developing a new concept of liquid metal divertor, referred as ’Divertorlets’. Before attending Princeton, Sun led the experimental physics research on the effect of active Lithium/Boron injection on EAST plasma performance and involved in building liquid lithium limiter/circuit and their experiments in EAST and HT-7. Google scholar
Azarakhsh (Aza) is a Research Scholar at Princeton University with a Ph.D. in Computer Engineering from Ghent University (Belgium). His research focuses on Artificial Intelligence, signal processing and data-driven discovery research tracks including audio, visual and radar data analysis as well as multi-sensor signal processing for variety of applications such as object recognition, surveillance, predictive maintenance and anomaly detection. He investigates real-time, robust and adaptive data-driven models for condition monitoring and plasma control in the magnetic confinement devices to produce controlled thermonuclear fusion power.
Daniel is a 6th-year PhD student in the MAE department. Before joining the Kolemen group, he completed his bachelor’s degree in Mechanical Engineering at Bucknell University. Daniel’s research is on computational tools for calculating the equilibrium magnetic fields in stellarators, a type of fusion reactor with three-dimensional geometries. He also assists with work on flowing liquid metals for fusion applications.
Joe is a 5th-year graduate student working on model-predictive control to help operators achieve desirable plasma states in experimental fusion reactors. He and Rory are running a campaign of realtime tests on the algorithm at the DIII-D tokamak in San Diego.
Rory is a 5th-year graduate student in MAE, with an undergraduate background in mechanical engineering, physics, and film studies. He is developing machine learning algorithms for data driven control of fusion plasmas to predict and avoid instabilities and achieve new confinement regimes. He has developed new methods to streamline and automate the conversion and deployment of such algorithms for real time applications. He is also working on new numerical techniques for physics based predictions of restive instabilities in plasmas, and helping to develop a new stellarator equilibrium code.
Susan is a 5th-year PhD student in the MAE department. Before joining the Kolemen group, she completed her bachelor’s degree in Mechanical Engineering at Memorial University of Newfoundland and her master’s degree in Aerospace Engineering at the University of Toronto. Susan’s research is focused on optical control systems for balloon-borne telescopes and exoplanet-imaging space telescopes. Susan works in partnership with the Jones group on adaptive optics systems for the SuperBIT and GigaBIT balloon-borne telescopes as well as with the Kasdin group (emeritus) on high contrast imaging control algorithms for space telescopes.
Josiah is a 5th-year student focused on magnetic control of plasmas. His research is focused on modelling and controlling the plasma shape boundary, with the aim of being able to control the plasma during the highly dynamic ramp-up phase at NSTXU. Accurately controling the ramp-up phase will allow for more consistent H-mode transitions in spherical tokamaks.
Ricardo joined the MAE Ph.D program at Princeton in 2018. Prior to starting the Ph.D program, he received a B.Sc. degree in Mechanical Engineering, an M.Sc. in Mechanical Engineering specializing in Control Systems Technology and an M.Sc. in Science and Technology of Nuclear Fusion from the department of Applied Physics of the University of Technology Eindhoven, The Netherlands. Ricardo’s research interests can be summarized as the development of control systems for fusion plasmas, and testing them in experimental campaigns. More specifically, he is developing a Feedback ELM Controller aimed at achieving and sustaining complete ELM suppression subject to optimization of plasma performance. Additionally, he is working on improvements of the plasma state observer (rtEFIT) through the inclusion of kinetic constraints. These kinetic constraints improve the reconstructed profiles to the point where these profiles may be controlled in real-time.
Dario is a 3rd-year graduate student in the MAE department interested in computational plasma physics, especially related to fusion energy. He completed his undergraduate in Nuclear, Plasma, and Radiological Engineering at UIUC before attending Princeton. Currently, Dario works on code development and error analysis of computational stellarator equilibria.
Francisco is a 3rd-year PhD student in the MAE department interested in computational/experimental liquid metal magnetohydrodynamics, especially related to the design of plasma-facing component of fusion systems. His undergraduate degree is in Mechanical Engineering from Universidad de Costa Rica. Currently, Francisco works on simulations and experiments to evaluate the performance of the new liquid metal divertor concept called ‘Divertorlets’. Additionally, he works on simulations of free-surface liquid metal flows for MHD drag calculations.
Brian is a 2nd year graduate student in MAE. Before joining the Kolemen group, he completed his Bachelor’s Degree in Chemical Engineering at The Ohio State University. Brian works with experiments and simulations on LMX-U (Liquid Metal eXperiment Upgrade) at PPPL. He is currently involved in testing the effects of a gradient magnetic field on free surface flow, as well as designing a magnetically driven centrifuge for lithium hydride separation from liquid lithium.
Andy is a 2nd year graduate student in MAE interested in data-driven approaches to plasma control. His work involved building machine learning models to find correlations between tokamak diagnostics and magnetic instabilities to build models capable of predicting magnetic instabilities in real-time. Before Princeton, he got his Bachelor’s Degree in Physics from Caltech and while performing research in astrodynamics and quantum optics.
Jalal studies the 3D field physics of edge-localized modes (ELMs) and their control in pursuit of long-pulse tokamak operation for KSTAR, ITER and beyond. Before Princeton, Jalal graduated from Columbia University, where he contributed to the disruption event characterization and forecasting code (DECAF), and CCSU. Jalal also worked as a research scientist at NASA Goddard Space Flight Center on novel satellite remote sensing techniques.
Recent visiting researchers
Jabir Al-Salami is a PhD student at Kyushu University, Japan, majoring in computational fluid dynamics (CFD). He obtained his undergraduate degree in Mechatronics from Sultan Qaboos University, Oman. His research interests include applying particle and high-order methods to challenging free-surface phenomena, such as magneto-hydrodynamical flows. He has been developing CFD codes to aid in the design of liquid metal plasma-facing components for use in nuclear fusion reactors.
Recent undergraduate students
Kaya is an undergraduate student at Princeton studying Electrical Engineering and Engineering Physics. As part of the stellarator team in the summer of 2022, Kaya contributed to the development of DESC, a stellarator equilibrium and optimization code. Outside of research, Kaya enjoys swimming and lifting.
Sam is a visiting undergraduate student from Harvard who is studying the effect of Neutral Beam Injection on Edge Localized Modes for her SULI project.
Shayaan is an undergraduate student from Yale who is working on implementing models for radiation losses in tokamaks. These models are to be implemented in the FAROES fusion systems code.
University of California, Berkeley
Nigel is undergraduate student at University of California Berkeley studying degrees in Nuclear Engineering and Physics. He is participating in the Summer 2021 SULI Program where he constructed a linear programming model of a fusion power plant with an attached thermal storage system using estimated hourly electricity price series for a 2030s-era wind-dominated grid. We study whether adding thermal storage to a fusion reactor can mitigate losses in availability associated with unexpected shutdowns and increase profits.
Laura is a summer student working on error analysis and uncertainty quantification for machine learning models in fusion.
Yuno is a summer student working on machine learning driven real-time Bayesian estimation of the state of plasmas in fusion devices.
Azmaine was a senior thesis student (2020-2021) continuing work on autoencoder models for learning controllable linear dynamics. He also developed a model predictive controller using these machine learning models.
Aaron was a summer intern in 2020 working on using autoencoders to find control oriented linear embeddings of nonlinear plasma dynamics. He has since continued on as an undergraduate researcher to work on GPU algorithms for real time plasma stability analysis
Milan A. Wolff
The College of William & Mary
Milan was a summer student through the SULI program in 2020. He used machine learning techniques for automatic classification of tokamak experiments and identification of different regimes of plasma operation.
Bora was a senior thesis student (2019-2020) examining linear and auto-regressive reduced order models for predicting plasma profile evolution.
Alex was a summer student in 2019, helping to develop machine learning models for plasma profile prediction. He performed extensive sensitivity analysis to determine which signals were the most important for making predictions, and which actuators had the greatest effect on the state of the plasma.
Central Connecticut State University
Jalal was a summer student through the SULI program in 2018. He developed machine learning models using a Long Short-Term Memory recurrent neural network (LSTM-RNN) to predict the evolution of temperature and density profiles in tokamaks.
Nathaniel was a summer student through the SULI program in 2017. He used regression trees to attempt to predict the probability of high current disruptions in tokamaks using only low current disruptions as training data, a key need for ITER where accurate predictions will be needed with limited training data.
Neil is an undergraduate engineering student at Princeton university with an interest in energy science and engineering. Neil has worked on calibration techniques and low friction modifications to Lorentz force velocimeters. Outside of research and studying Neil enjoys distance running, cycling and baseball.
Gerrit was a member of the Kolemen group during Summer 2017 as part of the SULI program at PPPL. Gerrit developed a non-contact liquid metal depth sensor. He built several prototypes of the sensor and compared measured data with predictions from a finite element analysis software called FEMM. Gerrit is interested in nuclear energy (fission and fusion) as well as particle accelerators. He is an NRC-licensed reactor operator on the ISU AGN-201 nuclear fission reactor. Gerrit is also an avid snowboarder and loves to work on his 1971 Chevrolet Nova SS.
Dhruval worked on liquid metals, developing a diagnostic tool to resolve the surface wave topology of a flowing liquid metal. The setup uses a single camera to acquire an image being reflected off the liquid metal surface. Methods and algorithms were developed with the goal of decreasing the computation time and error. A 1D simulation of the problem is created to study the efficacy of each algorithm. This paper discusses the differences between he methods and compares the relative error and computation time required for each method. It was concluded that images with lower resolution can be used to get reliable results using any of these methods. Crude approximation using the linear method can yield results quickly with a reasonable margin of error.
Dhruvit was with the Kolemen group during Summer 2017 as part of the SULI program at PPPL. During that time, he designed and build several flow-straighteners and nozzles for liquid metal flows and tested them on the Liquid Metal Experiment (LMX). Dhruvit has pursued research in various fields of engineering and physics, such as aerodynamics, condensed matter physics, fluid dynamics (MHD) as well as computational analysis and simulations in order to better understand his future goals. He will be applying to graduate school Fall 2017 to various physics/engineering programs in Atomic/Laser/Plasma Physics.
Pat is an 8th-year PhD student in the MAE department. His research focuses on the development of control systems for fusion plasmas in order to address the challenges of plasma shape and heat flux control in tokamaks. Pat is currently leading the development of the advanced divertor control systems for the NSTX-U and DIII-D tokamaks. This work includes physics-based control algorithm design and simulation, implementation of the real-time algorithms for use during plasma operations, and experiments to assess controller performance and divertor physics. Before attending Princeton, Pat worked as a full-time propulsion engineer at SpaceX.
Tim is a 5th-year graduate student co-advised by Prof. Kolemen and Prof. Yiguang Ju. His research entails utilizing laser diagnostics to study plasma-assisted combustion and fuel reforming.
Oak is a 5th-year graduate student in the Plasma Physics Program. His thesis (“Comprehensive dynamic analysis of the H-mode pedestal in DIII-D”) is a combination of three separate experimental thrusts: (1) an investigation into the relationship between the neutral fueling profile and the pedestal structure, (2) experimental identification of turbulent processes that dictate transport through the pedestal and (3) 2D modeling of pedestal/SOL interactions with UEDGE. Oak was worked on several additional projects, including the development of several OMFIT modules for DIII-D analysis, studies of advanced ECE implementation, investigations into locked mode dynamics, implementation of automated ELM control, and surface chemistry experiments regarding the sputtering of lithium surfaces.
Adam completed his PhD in 2020, working on thin fast-flowing liquid metals for fusion reactor applications. Specifically, his research aims to study flow relevant to a liquid metal divertor on a tokamak. He is currently working on LMX-U at PPPL, a galinstan (GaInSn) channel flow within a magnetic field that can be injected with external electric currents.
Yichen is a 4th-year graduate student at Princeton. He is interested in plasma physics, especially in theory and computation. Yichen is currently working on using machine learning algorithms to predict the onset of several important tokamak phenomena, including disruptions and neoclassical tearing nodes (NTMs).
Mike has extensive experience working with liquid metals. From 2008-2013, he worked with high-temperature sodium systems at the University of Wisconsin-Madison. From 2013-2016, he was the Lead Research Engineer for the Mechanisms Engineering Test Facility at Argonne National Laboratory. Since May 2016, Mike has been working on the Liquid Metal eXperiment (LMX) and the Flowing Liquid Metal Torus (FLIT) experiment at Princeton University and Princeton Plasma Physics Laboratory.
Olivier works on theoretical and computational plasma physics. His recent results include (1) tokamak edge simulations with the multi-fluid transport code UEDGE and the turbulence code BOUT++, (2) development of the grid generator Gingred in collaboration with M. Umansky (LLNL) for single-null, double-null, snowflake divertor geometries, or field-reversed-configurations, (3) participation in the creation of the integrated framework OMFIT that allows accessing experimental data, running codes and creating post-analyses, (4) development of a new analytic non-Maxwellian distribution function based on non-orthogonal basis sets, (5) development of the generalized fluid theory that avoids using ad-hoc nonlocal transport coefficients, and (6) development of a new 5D kinetic theory called the gyrointegrated kinetic theory valid for arbitrary gradient scale lengths, Larmor radii, magnetic geometry and distribution functions. Olivier also has extensive experience peer-reviewing articles and was awarded as the “Reviewer of the Year” for the journal Plasma Physics and Controlled Fusion in 2017.
Florian is a post-doc working primarily on plasma edge and pedestal physics. During his PhD studies at TU Wien, he investigated the inter-ELM pedestal evolution on ASDEX Upgrade and operated the lithium beam diagnostic. After receiving his PhD, he joined the Kolemen group in June 2017. Florian’s current research includes the implementation of a real-time Thomson Scattering diagnostic on NSTX-U as well as the running of experiments on DIII-D, which aim to develop a deeper understanding of pedestal fluctuations. Furthermore, he is involved in the development of pedestal controllers and studies the impact of low-Z impurities on the pedestal.
David is interested in control systems, primarily focused on tokamak divertors and heat flux, as well as general plasma control, boundary physics, and pedestal physics.
Alex worked on simulation of fueling of fusion reactors and control of the plasma edge. He analyzed the penetration of Deuterium/Tritium and Lithium pellets into the fusion core using M3D-C1 and developed control algorithms for EAST tokamak. He is now working at University of York.
Mikhail worked on simulation of liquid metals under magnetohydrodynamical conditions with an International Postdoc Grant from the Swedish Research Council (VR) for collaborative research in Princeton University. He is now working at Instituto de Astrofísica de Canarias.
Matthijs is a graduate student at the University of Eindhoven studying for a dual degree in Mechanical Engineering and Science and Technology of Nuclear Fusion. Before enrolling at Eindhoven, he earned his bachelor’s degree in Mechanical Engineering from the University of Twente. In the Kolemen group, Matthijs developed new methods for automatically reconstructing tokamak plasma equilibria with kinetic constraints and employed a technique called the unscented transform to compute uncertainty estimates of the ideal MHD stability criterion (δW).
Kornee is a graduate student at the University of Eindhoven studying for a dual degree in Systems and Control and Science and Technology of Nuclear Fusion. He earned a bachelor’s degree in Mechanical Engineering from the University of Twente. In the Kolemen group, Kornee used various machine learning algorithms such as regression trees to create plasma disruption predictors for the DIII-D and NSTX-U tokamaks.
Koji works on heat transfer in various heat exchanger systems. He also studies hydrodynamics flow in oil plants.
Shoki studies the measurement of MHD pressure drops under high magnetic field (3T). He describes himself as full of curiosity for everything and having a passion for talking. Shoki enjoys sports – especially volleyball and baseball – and also fine foreign cuisine, including the American hot dog.