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"Stacey, Weston M"
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A Strategic Opportunity for Magnetic Fusion Energy Development
The realities of energy development and the perception of and support for magnetic fusion in the US are briefly summarized as background for proposing a strategic opportunity for magnet fusion energy development as fusion neutron sources for subcritical advanced burner (transmutation) reactors for the destruction of long-lived transuranics in spent nuclear fuel.
Journal Article
The ITER decision and U.S. fusion R&D: to keep pace with the cutting edge of fusion research, the United States must participate in the planned international research reactor. (International Thermonuclear Experimental Reactor)
The US stands to benefit from its involvement in the construction and implementation phase of the International Thermonuclear Experimental Reactor (ITER). Aside from providing opportunity for advancing the development of fusion plasma science, such an offer of host site will also mean expertise in fusion engineering and will be a significant contribution to the local economy. Science and technology will also gain from the participation of US firms in the production of ITER parts in terms of the resulting applications and fusion devices.
Journal Article
A Generalized Multinodal Model for Plasma Particle and Energy Transport
We present a generalized multinodal model for simulating particle and energy transport in toroidal plasma configurations, developed to support burning plasma analysis and reactor-scale modeling. Unlike fixed-node models, this formulation allows an arbitrary number of nodes, offering increased flexibility for coupling with core-edge or core-pedestal simulations. The model derives nodal balance equations for each plasma species by volume-averaging the continuity and energy conservation equations across toroidal shell nodes. Particle and energy transport terms are expressed in terms of internodal fluxes, linked to radial gradients via linear diffusion laws for particle density and temperature, respectively. The resulting transport contributions are characterized through effective particle and energy transport times, derived explicitly in terms of nodal geometry and diffusivities. This generalized framework facilitates efficient, modular implementation of radial transport dynamics in reduced-order or integrated plasma simulations, and is compatible with data-driven approaches such as NeuralPlasmaODE for model calibration and inference from experimental data.
Paper
Sensitivity Analysis of Transport and Radiation in NeuralPlasmaODE for ITER Burning Plasmas
Understanding how key physical parameters influence burning plasma behavior is critical for the reliable operation of ITER. In this work, we extend NeuralPlasmaODE, a multi-region, multi-timescale model based on neural ordinary differential equations, to perform a sensitivity analysis of transport and radiation mechanisms in ITER plasmas. Normalized sensitivities of core and edge temperatures and densities are computed with respect to transport diffusivities, electron cyclotron radiation (ECR) parameters, impurity fractions, and ion orbit loss (IOL) timescales. The analysis focuses on perturbations around a trained nominal model for the ITER inductive scenario. Results highlight the dominant influence of magnetic field strength, safety factor, and impurity content on energy confinement, while also revealing how temperature-dependent transport contributes to self-regulating behavior. These findings demonstrate the utility of NeuralPlasmaODE for predictive modeling and scenario optimization in burning plasma environments.
Paper
Optimizing External Sources for Controlled Burning Plasma in Tokamaks with Neural Ordinary Differential Equations
Achieving controlled burning plasma in tokamaks requires precise regulation of external particle and energy sources to reach and maintain target core densities and temperatures. This work presents an inverse modeling approach using a multinodal plasma dynamics model based on neural ordinary differential equations (Neural ODEs). Given a desired time evolution of nodal quantities such as deuteron density or electron temperature, we compute the external source profiles, such as neutral beam injection (NBI) power, that drive the plasma toward the specified behavior. The approach is implemented within the NeuralPlasmaODE framework, which models multi-region, multi-timescale transport and incorporates physical mechanisms including radiation, auxiliary heating, and internodal energy exchange. By formulating the control task as an optimization problem, we use automatic differentiation through the Neural ODE solver to minimize the discrepancy between simulated and target trajectories. This framework transforms the forward simulation tool into a control-oriented model and provides a practical method for computing external source profiles in both current and future fusion devices.
Paper
Forum
Mark Schneider's work on the wide variation in the economic value of postsecondary educational programs, as described in The Value of Sub-baccalaureate Credentials, is of great importance because it reflects new labor market realities that affect nearly everyone in the US. Before the 1980s, high school was enough to provide middle-class earnings for most people. In the 1970s, for example, nearly three in four workers had a high school education or less, and the majority of these workers were still in the middle class. Degrees and other postsecondary credentials have multiplied and diversified to include traditional degrees measured in years of seat time; bite-sized credentials that take a few months; boot camps, badges, stackable certificates, and massive open online courses that take a few weeks; and test-based certifications and licenses based on proven competencies completely unmoored from traditional classroom training.
Journal Article
Forum
Critique's of several scholarly articles about various aspects of science and technology are presented.
Journal Article
Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics
The dynamics of burning plasmas in tokamaks are crucial for advancing controlled thermonuclear fusion. This study applies the NeuralPlasmaODE, a multi-region multi-timescale transport model, to simulate the complex energy transfer processes in ITER deuterium-tritium (D-T) plasmas. Our model captures the interactions between energetic alpha particles, electrons, and ions, which are vital for understanding phenomena such as thermal runaway instability. We employ neural ordinary differential equations (Neural ODEs) for the numerical derivation of diffusivity parameters, enabling precise modeling of energy interactions between different plasma regions. By leveraging transfer learning, we utilize model parameters derived from DIII-D experimental data, enhancing the efficiency and accuracy of our simulations without training from scratch. Applying this model to ITER's inductive and non-inductive operational scenarios, our results demonstrate that radiation and transport processes effectively remove excess heat from the core plasma, preventing thermal runaway instability. This study underscores the potential of machine learning in advancing our understanding and control of burning plasma dynamics in fusion reactors.
Paper