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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01z316q451j
 Title: Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models Contributors: Mark D. BoyerU. S. Department of Energy Keywords: NSTX-Umachine learningoptimizationNUBEAMprofile control Issue Date: May-2020 Publisher: Princeton Plasma Physics Laboratory, Princeton University Related Publication: Proceedings of Machine Learning Research Abstract: One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time. URI: http://arks.princeton.edu/ark:/88435/dsp01z316q451j Appears in Collections: NSTX-U

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