改进弦长抽样方法开发及在弥散燃料蒙特卡罗模拟的应用

Development of Improved Chord-length Sampling Method and Its Application in Monte Carlo Simulation of Dispersion Fuel

  • 摘要: 弥散型燃料广泛应用于高温气冷堆、事故容忍燃料、实验研究堆及核动力舰船等,是重要的燃料类型之一。弦长抽样(CLS)方法可简化弥散燃料几何建模,提高计算效率,然而传统CLS方法只能描述单种颗粒的填充,同时在高体积填充率时误差较大。针对CLS方法的两大问题,本文在自主化堆用蒙特卡罗程序RMC中开发了改进CLS方法,并应用于全陶瓷微胶囊封装燃料棒算例及含毒物颗粒的高温堆燃料球算例。计算结果表明,改进CLS方法可解决多种颗粒混合填充的问题,并且可保证体积填充率的准确性,为弥散燃料的临界及燃耗计算提供了高效、精确的方法。

     

    Abstract: Dispersion fuel is widely used in high-temperature gas-cooled reactor (HTGR), accident tolerant fuel, experimental research reactor, naval nuclear power plant and so on. The chord-length sampling (CLS) method can simplify the geometry modeling of dispersion fuel, which can improve the efficiency. However, traditional CLS can only handle the packing of single particle, and has large error when the packing fraction is high. Aiming to solve these two problems, the improve CLS method was developed in reactor Monte Carlo code RMC, and applied to the fully ceramic micro-encapsulated fuel pin case and HTGR fuel pebble with mixed fuel and poison particles. Results show that the proposed method can handle mixed particles with multiple types, and preserve the accuracy of packing fraction, which provide precise and high efficiency for the critical and burnup calculations.

     

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