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Numerical methods applied to pin power reconstruction based on coarse-mesh nodal calculation

机译:基于粗网格节点计算的引脚功率重构数值方法

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The proposed reconstruction processes combine the two-dimensional (2D) neutron diffusion equation discretized by finite difference method (FDM) and Galerkin finite element method (GFEM) with homogeneous flux distributions. The Finite Element Reconstruction Method (FERM) uses a homogeneous flux distribution over the extremities of each interval that constitute the faces of the fuel assembly (FA). In turns, the Finite Difference Reconstruction Method (FDRM) uses a homogeneous flux distribution over each interval that constitute the faces of the fuel assembly (FA). These flux distributions are obtained for each face of the FA from one-dimensional (1D) polynomial expansions. Such boundary conditions (flux distributions) are based on the average fluxes on the node faces, which are provided by the coarse-mesh nodal calculation performed in homogeneous nodes with dimensions of a FA. The Nodal Expansion Method (NEM) is used for nodal calculation and also provides the multiplication factor of the problem. These reconstruction methods use homogeneous nuclear parameters providing homogeneous flux distributions within the FA. The modulation method is applied to obtain heterogeneous distributions within the FA. To validate the results obtained by the reconstruction methods, such reconstructed heterogeneous distributions are compared with the reference values and with reconstruction performed by the PARCS code. The results show the good accuracy and efficiency of both methods. (C) 2018 Elsevier Ltd. All rights reserved.
机译:拟议的重建过程将通过有限差分法(FDM)和Galerkin有限元方法(GFEM)离散的二维(2D)中子扩散方程与均匀通量分布相结合。有限元重建方法(FERM)在组成燃料组件(FA)面的每个区间的末端使用均匀的通量分布。反过来,有限差分重建方法(FDRM)在构成燃料组件(FA)面的每个间隔上使用均匀的通量分布。这些通量分布是根据一维(1D)多项式展开式为FA的每个面获得的。这种边界条件(通量分布)是基于节点面上的平均通量,这是由在具有FA尺寸的齐次节点中执行的粗网格节点计算提供的。节点扩展方法(NEM)用于节点计算,并且还提供了问题的倍增因子。这些重建方法使用均质核参数,在FA内提供均质通量分布。应用调制方法以获得FA内的异构分布。为了验证通过重构方法获得的结果,将这种重构的异构分布与参考值以及由PARCS代码执行的重构进行比较。结果表明两种方法均具有良好的准确性和效率。 (C)2018 Elsevier Ltd.保留所有权利。

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