Rory Conlin
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View article: Exponential Spectral Scaling: Robust and Efficient Stellarator Boundary Optimization via Mode-Dependent Scaling
Exponential Spectral Scaling: Robust and Efficient Stellarator Boundary Optimization via Mode-Dependent Scaling Open
Stellarator boundary optimization faces a fundamental numerical challenge: the extreme disparity between low- and high-mode amplitudes creates an optimization landscape in which direct full-spectrum approaches typically converge to poor lo…
View article: How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning
How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning Open
Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of ${\gt}200\,000$ nonlinear gyrokinetic sim…
View article: Extending near-axis equilibria in DESC
Extending near-axis equilibria in DESC Open
The near-axis description of optimised stellarator fields has proven to be a powerful tool both for design and understanding of this magnetic confinement concept. The description consists of an asymptotic model of the equilibrium in the di…
View article: Omnigenous umbilic stellarators
Omnigenous umbilic stellarators Open
To better understand the dependence of the magnetic field structure in the plasma edge on the plasma boundary shape, in the context of X-point and island divertor designs, we define and develop a class of stellarators called umbilic stella…
View article: How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning
How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning Open
Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyze this dependence using multiple machine learning methods and a dataset of > 200,000 nonlinear simulations of ion-te…
View article: High Order Free Boundary MHD Equilibria in DESC
High Order Free Boundary MHD Equilibria in DESC Open
In this work we consider the free boundary inverse equilibrium problem for 3D ideal MHD. We review boundary conditions for both fixed and free boundary solutions and under what circumstances a sheet current may exist at the plasma-vacuum i…
View article: Spectrally accurate, reverse-mode differentiable bounce-averaging algorithm and its applications
Spectrally accurate, reverse-mode differentiable bounce-averaging algorithm and its applications Open
We present a fast, spectrally accurate, automatically differentiable bounce-averaging algorithm implemented in the DESC stellarator optimization suite. Using this algorithm, we can perform efficient optimization of many objectives to impro…
View article: Omnigenous stellarator equilibria with enhanced stability
Omnigenous stellarator equilibria with enhanced stability Open
To build an economically viable stellarator, it is essential to find a configuration that satisfies a set of favorable properties to achieve efficient steady-state nuclear fusion. One such property is omnigenity, which ensures confinement …
View article: Stellarator optimization with constraints
Stellarator optimization with constraints Open
In this work we consider the problem of optimizing a stellarator subject to hard constraints on the design variables and physics properties of the equilibrium. We survey current numerical methods for handling these constraints, and summari…
View article: ZERNIPAX: A Fast and Accurate Zernike Polynomial Calculator in Python
ZERNIPAX: A Fast and Accurate Zernike Polynomial Calculator in Python Open
Zernike polynomials serve as an orthogonal basis on the unit disc, and have proven to be effective in optics simulations, astrophysics, and more recently in plasma simulations. Unlike Bessel functions, Zernike polynomials are inherently fi…
View article: Optimization of nonlinear turbulence in stellarators
Optimization of nonlinear turbulence in stellarators Open
We present new stellarator equilibria that have been optimized for reduced turbulent transport using nonlinear gyrokinetic simulations within the optimization loop. The optimization routine involves coupling the pseudo-spectral GPU-native …
View article: Stellarator Optimization with Constraints
Stellarator Optimization with Constraints Open
In this work we consider the problem of optimizing a stellarator subject to hard constraints on the design variables and physics properties of the equilibrium. We survey current numerical methods for handling these constraints, and summari…
View article: Avoiding fusion plasma tearing instability with deep reinforcement learning
Avoiding fusion plasma tearing instability with deep reinforcement learning Open
For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the obs…
View article: Magnetic fields with general omnigenity
Magnetic fields with general omnigenity Open
Omnigenity is a desirable property of toroidal magnetic fields that ensures confinement of trapped particles. Confining charged particles is a basic requirement for any fusion power plant design, but it can be difficult to satisfy with the…
View article: Optimization of Nonlinear Turbulence in Stellarators
Optimization of Nonlinear Turbulence in Stellarators Open
We present new stellarator equilibria that have been optimized for reduced turbulent transport using nonlinear gyrokinetic simulations within the optimization loop. The optimization routine involves coupling the pseudo-spectral GPU-native …
View article: Avoiding tokamak tearing instability with artificial intelligence
Avoiding tokamak tearing instability with artificial intelligence Open
For stable and efficient fusion energy production using a tokamak reactor, maintaining high-pressure hydrogenic plasma without plasma disruption is essential. Therefore, it is necessary to actively control the tokamak based on the observed…
View article: Multimodal Prediction of Tearing Instabilities in a Tokamak
Multimodal Prediction of Tearing Instabilities in a Tokamak Open
Tokamak is a torus-shaped nuclear fusion device that uses magnetic fields to confine fusion fuel in the form of plasma. Tearing instability in plasma is a major issue in which the magnetic field breaks and recombines in tokamak. Here, this…
View article: Implementation of AI/DEEP learning disruption predictor into a plasma control system
Implementation of AI/DEEP learning disruption predictor into a plasma control system Open
This paper reports on advances in the state‐of‐the‐art deep learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced in a 2019 NATURE publication [ https://doi.org/10.1038/s41586‐019‐1…
View article: The DESC stellarator code suite. Part 2. Perturbation and continuation methods
The DESC stellarator code suite. Part 2. Perturbation and continuation methods Open
A new perturbation and continuation method is presented for computing and analysing stellarator equilibria. The method is formally derived from a series expansion about the equilibrium condition $\boldsymbol {F} \equiv \boldsymbol {J}\time…
View article: The DESC stellarator code suite. Part 1. Quick and accurate equilibria computations
The DESC stellarator code suite. Part 1. Quick and accurate equilibria computations Open
Three-dimensional equilibrium codes are vital for stellarator design and operation, and high-accuracy equilibria are also necessary for stability studies. This paper details comparisons of two three-dimensional equilibrium codes: VMEC, whi…
View article: Magnetic Fields with General Omnigenity
Magnetic Fields with General Omnigenity Open
Omnigenity is a desirable property of toroidal magnetic fields that ensures confinement of trapped particles. Confining charged particles is a basic requirement for any fusion power plant design, but it can be difficult to satisfy with the…
View article: The DESC stellarator code suite Part 3: Quasi-symmetry optimization
The DESC stellarator code suite Part 3: Quasi-symmetry optimization Open
The DESC stellarator optimization code takes advantage of advanced numerical methods to search the full parameter space much faster than conventional tools. Only a single equilibrium solution is needed at each optimization step thanks to a…
View article: Greedy permanent magnet optimization
Greedy permanent magnet optimization Open
A number of scientific fields rely on placing permanent magnets in order to produce a desired magnetic field. We have shown in recent work that the placement process can be formulated as sparse regression. However, binary, grid-aligned sol…
View article: A general infrastructure for data-driven control design and implementation in tokamaks
A general infrastructure for data-driven control design and implementation in tokamaks Open
A general infrastructure for tokamak controllers based on data-driven neural net models is presented. The paradigm allows for more flexible choices of both the underlying model and the desired controlled variables and targets. The system i…
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: Greedy permanent magnet optimization
Greedy permanent magnet optimization Open
A number of scientific fields rely on placing permanent magnets in order to produce a desired magnetic field. We have shown in recent work that the placement process can be formulated as sparse regression. However, binary, grid-aligned sol…
View article: Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System
Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System Open
This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 Nature publication. In particular, the predictor now feature…
View article: The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations
The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations Open
3D equilibrium codes are vital for stellarator design and operation, and high-accuracy equilibria are also necessary for stability studies. This paper details comparisons of two three-dimensional equilibrium codes, VMEC, which uses a steep…