反应堆周期算法及定值优化研究

Reactor Period Algorithm and Parameter Set-point Optimization-Study

  • 摘要: 采用6组缓发中子点堆模型,模拟堆芯在初始达临界和零功率物理试验时,引入20 pcm和60 pcm典型阶跃反应性,研究卡尔曼滤波算法和动态滤波算法。结果表明:卡尔曼滤波算法能减弱中子瞬跳的影响,具有较好的自适应性,但在瞬态初期测量误差偏大,在大反应性引入时启动保护的响应时间较长;动态滤波算法通过优化功率相对变化量定值LAMMA,并定期调整增益相关参数λ,具有测量准确、大反应性下保护响应快的优点,但频繁定值调整耗时较多,并增加了误操作的风险;静态滤波的增益为优化的常数,具有测量准确、节省时间和安全的优点。

     

    Abstract: A point reactor model with 6 groups delayed neutrons was adopted to simulate the typical 20 pcm and 60 pcm prompt reactivity insertion during the period of initial criticality and zero power physics test. Both Kalman filter and dynamic filter algorithms were studied. The results show that the Kalman filter approach can attenuate the impact of neutron prompt jump and gives features of self-adaptation. However, it reaches less accuracy at the earlier transient period and the protection response time is long when large reactivity is inserted. The dynamic filter approach gives high accuracy and response quickly through the optimization of the relative power change LAMMA and gain coefficient λ. However, frequent adjustment of λ takes time and increases the probability of operation mistakes. Static filter, with optimized constant gain coefficient, is accurate, timesaving and safe.

     

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