论文标题
多重执行器约束执法算法的数值演示熔融盐环
Numerical Demonstration of Multiple Actuator Constraint Enforcement Algorithm for a Molten Salt Loop
论文作者
论文摘要
为了推动核电站自动操作的范式，寻求数据驱动的机器学习方法。预计下一代反应堆设计的自主操作将增强安全性并改善经济性。但是，任何使用的算法都必须是可解释，适应性和稳健的。 在这项工作中，我们关注自主操作期间最佳控制的特定问题。我们将展示一种可解释和适应性的数据驱动的机器学习方法，以自主控制熔融盐环。为了解决解释性，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，将使用控制算法来修改执行器设定点，同时执行恒定和时间相关的约束。在这项工作中没有解决鲁棒性，并且是未来工作的一部分。为了展示该方法，我们设计了一个数值实验，需要干预以在负载遵循型瞬态期间执行约束。
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.
