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Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

机译：使用人工神经网络稳定热核反应堆中的燃烧条件

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In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN. [References: 31]
机译：在这项工作中，我们开发了一个人工神经网络（ANN），用于在接近点燃的燃烧条件下稳定热核反应堆的反馈。体积平均的零维非线性模型用于表示电子密度，α粒子的相对密度和等离子体温度的时间演化，其中其他作者先前使用的能量限制时间的特定比例定律，被采纳了。控制动作包括同时调节D-T加油率，注入中性He-4光束和辅助加热功率调节，这些调节必须取最大和最小范围内的值。为此，使用反向传播穿越时间技术来训练具有S形激活功能的前馈多层人工神经网络。数值示例用于说明所得的ANN动态系统配置的行为。结论是，所得的人工神经网络可以在接近点火条件下成功地稳定热核反应堆的非线性模型，因为温度和密度偏离其标称运行值明显较远。相对于用于训练NN的区域内的扰动，α动力系统的时间相对于α粒子的热化时间表现出了较强的鲁棒性。 [参考：31]

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