Coupling fluid plasma and kinetic neutral models using correlated Monte Carlo methods
Not Available
Parker, G. J.↗
Engineering topics
Dudson, B. D.
Publications and source records attributed to Dudson, B. D..
Critical Role of the Public Theory & Modeling Program for Commercial Fusion Energy
This white paper contributes input from the Executive Committee of the Theory Coordinating Committee to the 2024 FESAC Decadal Plan Subcommittee. It is argued that the public Theory and Modeling program plays a critical role in the pursuit of commercial fusion energy. A new mechanism for fostering engagement between the fusion industry and the public Theory and Modeling program could provide better alignment between the goals of the public program and the needs of private industry.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
The Compelling Need for a Mid-Scale Stellarator Facility
In the pursuit of the goal of commercial fusion as an abundant and safe source of energy, the stellarator is a leading concept with compelling attractiveness and demonstrated performance. In this white paper we, as a community of US researchers from Universities, National Laboratories, and Private Industry, involved in studying the stellarator concept, lay out the programmatic and technical motivation for a new mid-size stellarator research facility in the US. This contribution is complementary to several other whitepapers authored by members of our community which address different mid-scale stellarator aspects. A community based technical facility proposal has been prepared by F. Parra, et al: Flexible Stellarator Physics Facility. Two private stellarator companies have submitted proposals supporting the development of a mid-scale stellarator: Thea Energy (C.P.S. Swanson, et al.), Type One Energy (W. Guttenfelder, et al.).
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
The Compelling Need for a Mid-Scale Stellarator Facility
In the pursuit of the goal of commercial fusion as an abundant and safe source of energy, the stellarator is a leading concept with compelling attractiveness and demonstrated performance. A new mid-size stellarator is needed to retire risks and innovate towards a high performance, economically attractive, stellarator Fusion Pilot Plant. In this presentation we, as a community of US researchers from Universities, National Laboratories, and Private Industry, involved in studying the stellarator concept, lay out the programmatic and technical motivation for a new and modern mid-size stellarator research facility. A new mid-scale stellarator is needed to realize the potential predicted by a solid body of theory and simulation along with advances in computational tools for optimization and non-linear turbulence modeling. Notably, it is possible to combine the advantages of the stellarator (steady state, no current drive, no disruptions) with the good confinement regularly achieved in tokamaks. The top priorities for experimental work, and the motivation for a mid-scale stellarator experiment are: turbulence control, non-resonant divertor, MHD stability at large beta, confinement of fast particles, and coil simplification. A new mid-size quasi-symmetric stellarator, built as a user facility, would complement existing research at Wendelstein 7-X and LHD and strongly augment private industry. It would provide a program of innovative research, concept validation, theoretical advancement, and workforce development. Growing support and interest for stellarators by the fusion community and private industry affirms this rationale.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Tokamak divertor plasma emulation with machine learning
Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗