熔盐回路多执行器约束增强算法的数值验证
Numerical Demonstration of Multiple Actuator Constraint Enforcement Algorithm for a Molten Salt Loop
摘要：
为了推进核电站自主运行的范例，寻求一种数据驱动的机器学习控制方法。下一代反应堆设计的自主运行预计将加强安全性并提高经济性。然而，所使用的任何算法都需要是可解释的、可适应的和鲁棒的。在这项工作中，我们专注于自主操作过程中的最优控制的具体问题。我们将展示一种可解释和适应性强的数据驱动机器学习方法，用于熔盐回路的自主控制。为了解决可解释性，我们利用数据驱动的算法来识别状态空间表示中的系统动态。为了解决适应性问题，将利用控制算法来修改致动器设定点，同时强制执行恒定和时间相关的约束。鲁棒性在本工作中没有涉及，并且是未来工作的一部分。为了证明这种方法，我们设计了一个数值实验，需要干预，以强制执行的负载跟随型瞬态过程中的约束。
英文摘要：
To advance the paradigm of autonomous operation for nuclear power plants, a data-driven machine learning approach to control is sought. Autonomous operation for next-generation reactor designs is anticipated to bolster safety and improve economics. However, any algorithms that are utilized need to be interpretable, adaptable, and robust. In this work, we focus on the specific problem of optimal control during autonomous operation. We will demonstrate an interpretable and adaptable data-driven machine learning approach to autonomous control of a molten salt loop. To address interpretability, we utilize a data-driven algorithm to identify system dynamics in state-space representation. To address adaptability, a control algorithm will be utilized to modify actuator setpoints while enforcing constant, and time-dependent constraints. Robustness is not addressed in this work, and is part of future work. To demonstrate the approach, we designed a numerical experiment requiring intervention to enforce constraints during a load-follow type transient.
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