Mark D. Boyer
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View article: Feedforward equilibrium trajectory optimization with GSPulse
Feedforward equilibrium trajectory optimization with GSPulse Open
One of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver. However, most standard equilibrium solvers are …
View article: Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV
Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV Open
The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics…
View article: Design and development of an off-normal warning system for SPARC
Design and development of an off-normal warning system for SPARC Open
The SPARC tokamak is a compact, high-field, and high-current device that will rely on disruption mitigation and prevention strategies to address the risks associated with its high stored energy densities. In order to implement these strate…
View article: Feedforward equilibrium trajectory optimization with GSPulse
Feedforward equilibrium trajectory optimization with GSPulse Open
One of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver. However, most standard equilibrium solvers are …
View article: Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV Open
The rampdown in tokamak operations is a difficult to simulate phase during which the plasma is often pushed towards multiple instability limits. To address this challenge, and reduce the risk of disrupting operations, we leverage recent ad…
View article: Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV Open
The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics…
View article: Reversed magnetic shear scenario development in NSTX-U using TRANSP
Reversed magnetic shear scenario development in NSTX-U using TRANSP Open
Understanding and control of electron thermal transport is a critical point of research in magnetic fusion experiments. Previous experiments have shown that operation with reversed magnetic shear (RMS) can suppress electron thermal transpo…
View article: Neural networks for estimation of divertor conditions in DIII-D using C III imaging
Neural networks for estimation of divertor conditions in DIII-D using C III imaging Open
Deep learning approaches have been applied to images of C III emission in the lower divertor of DIII-D to develop models for estimating the level of detachment and magnetic configuration (X-point location and strike point radial location).…
View article: DIII-D research to provide solutions for ITER and fusion energy
DIII-D research to provide solutions for ITER and fusion energy Open
The DIII-D tokamak has elucidated crucial physics and developed projectable solutions for ITER and fusion power plants in the key areas of core performance, boundary heat and particle transport, and integrated scenario operation, with clos…
View article: NSTX-U research advancing the physics of spherical tokamaks
NSTX-U research advancing the physics of spherical tokamaks Open
The objectives of NSTX-U research are to reinforce the advantages of STs while addressing the challenges. To extend confinement physics of low- A , high beta plasmas to lower collisionality levels, understanding of the transport mechanisms…
View article: Automated experimental design of safe rampdowns via probabilistic machine learning
Automated experimental design of safe rampdowns via probabilistic machine learning Open
Typically the rampdown phase of a shot consists of a decrease in current and injected power and optionally a change in shape, but there is considerable flexibility in the rate, sequencing, and duration of these changes. On the next generat…
View article: Real time detection of multiple stable MHD eigenmode growth rates towards kink/tearing modes avoidance in DIII-D tokamak plasmas
Real time detection of multiple stable MHD eigenmode growth rates towards kink/tearing modes avoidance in DIII-D tokamak plasmas Open
Real time detection of time evolving growth rates of multiple stable magnetohydrodynamic (MHD) eigenmodes has been achieved in DIII-D tokamak experiments via multi-mode three-dimensional (3D) active MHD spectroscopy. The measured evolution…
View article: Exploration via Planning for Information about the Optimal Trajectory
Exploration via Planning for Information about the Optimal Trajectory Open
Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or rob…
View article: Neural net modeling of equilibria in NSTX-U
Neural net modeling of equilibria in NSTX-U Open
Neural networks (NNs) offer a path towards synthesizing and interpreting data on faster timescales than traditional physics-informed computational models. In this work we develop two NNs relevant to equilibrium and shape control modeling, …
View article: NSTX-U theory, modeling and analysis results
NSTX-U theory, modeling and analysis results Open
The mission of the low aspect ratio spherical tokamak NSTX-U is to advance the physics basis and technical solutions required for optimizing the configuration of next-step steady-state tokamak fusion devices. NSTX-U will ultimately operate…
View article: Development and experimental qualification of novel disruption prevention techniques on DIII-D
Development and experimental qualification of novel disruption prevention techniques on DIII-D Open
Novel disruption prevention solutions spanning a range of control regimes are being developed and tested on DIII-D to enable ITER success. First, a new real-time control algorithm has been developed and tested for regulating nearness to st…
View article: Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction Open
We introduce Neural Dynamical Systems (NDS), a method of learning dynamical\nmodels in various gray-box settings which incorporates prior knowledge in the\nform of systems of ordinary differential equations. NDS uses neural networks to\nes…
View article: Neoclassical toroidal viscosity torque prediction via deep learning
Neoclassical toroidal viscosity torque prediction via deep learning Open
GPECnet is a densely connected neural network that has been trained on GPEC data, to predict the plasma stability, neoclassical toroidal viscosity (NTV) torque, and optimized 3D coil current distributions for desired NTV torque profiles. U…
View article: Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks
Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks Open
This paper describes a real-time capable algorithm for identifying the safe operating region around a tokamak operating point. The region is defined by a convex set of linear constraints, from which the distance of a point from a disruptiv…
View article: DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy
DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy Open
DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, c…
View article: Development and experimental qualification of novel disruption prevention techniques on DIII-D
Development and experimental qualification of novel disruption prevention techniques on DIII-D Open
Novel disruption prevention solutions spanning a range of control regimes are being developed and tested on DIII-D to enable ITER success. First, a new real-time control algorithm has been developed and tested for regulating nearness to st…
View article: DIFFUSION MONTE CARLO USING MACHINE LEARNING POTENTIAL ENERGY SURFACES
DIFFUSION MONTE CARLO USING MACHINE LEARNING POTENTIAL ENERGY SURFACES Open
Diffusion Monte Carlo (DMC) is a technique for obtaining the ground-state solution to the vibrational time-independent Schrödinger equation based on a stochastic sampling of an electronic potential energy surface (PES). Ideally, the electr…
View article: Identification of multiple eigenmode growth rates towards real time detection in DIII-D and KSTAR tokamak plasmas
Identification of multiple eigenmode growth rates towards real time detection in DIII-D and KSTAR tokamak plasmas Open
The successful application of three-dimensional (3D) magnetohydrodynamic (MHD) spectroscopy enables us to identify the multi-mode eigenvalues in DIII-D and KSTAR tokamak experiments with stable plasmas. The temporal evolution of the multi-…
View article: Prediction of electron density and pressure profile shapes on NSTX-U using neural networks
Prediction of electron density and pressure profile shapes on NSTX-U using neural networks Open
A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the firs…
View article: Prediction of electron density and pressure profile shapes on NSTX-U using neural networks
Prediction of electron density and pressure profile shapes on NSTX-U using neural networks Open
A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the firs…
View article: Model predictive control of KSTAR equilibrium parameters enabled by TRANSP
Model predictive control of KSTAR equilibrium parameters enabled by TRANSP Open
Due to the complex behavior of tokamak plasmas and the importance of optimizing performance while avoiding instabilities and machine limits, plasma control algorithms are becoming increasingly dependent on sophisticated model-based control…