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Solution of neutron diffusion equation in 2d polar (r,theta) coordinates using Nodal Integral Method

机译:使用节点积分法求解二维极坐标(r,θ)中子扩散方程

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The nodal methods are significantly more accurate than the traditional methods such as finite difference method (FDM), finite element method (FEM) etc. However, these methods can be used only for the nodes of only a few limited shapes such as rectangular (in 2D) or cuboidal (in 3D). In this paper the approach for solving neutron diffusion equation in 2-d cylindrical polar geometry (r,theta) using nodal method is discussed. The analytic nodal method using transverse integration process is used to solve the 2-d neutron diffusion equation in polar coordinate. The problem involved with the transverse integration in theta-direction has been resolved by approximating the average of products by product of averages. This approximation leads to three different formulations of the scheme. A detailed study of the numerical error for source problems having analytical solutions is carried out. Using this analysis it has been shown that the error is second order for these problems irrespective of the formulation used. In addition to that the methodology is used to solve criticality problems for which analytical or benchmark solutions are available. The solution of the source problem shows that method maintains its accuracy and order even with approximations made to deal with transverse integration in theta-direction. The comparison of eigenvalues obtained with current methodology with those obtained analytically or available as benchmark show that the method is capable of accurately predicting the eigenvalues. (C) 2017 Elsevier Ltd. All rights reserved.
机译:节点方法比诸如有限差分法(FDM),有限元方法(FEM)等传统方法要精确得多。但是,这些方法仅可用于只有少数有限形状的节点,例如矩形(in 2D)或长方体(3D)。本文讨论了用节点法求解二维圆柱极角几何(r,θ)中子扩散方程的方法。采用横向积分的解析节点法求解极坐标中的二维中子扩散方程。通过将乘积的平均值乘以平均值的乘积来解决与θ方向上的横向积分有关的问题。这种近似导致该方案的三种不同公式。对具有解析解的源问题的数值误差进行了详细研究。使用该分析表明,对于这些问题,误差是二阶的,与所使用的配方无关。除此之外,该方法还用于解决关键问题,对于这些关键问题,可以使用分析或基准解决方案。源问题的解决方案表明,即使采用近似方法来处理θ方向的横向积分,该方法仍能保持其准确性和有序性。用当前方法获得的特征值与通过分析获得或可作为基准的特征值进行比较,表明该方法能够准确地预测特征值。 (C)2017 Elsevier Ltd.保留所有权利。

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