基于拟蒙特卡罗积分的随机离散纵标方法研究

Research on Random Discrete Ordinates Method Based on Quasi-Monte Carlo Integration Technique

  • 摘要: 离散纵标(SN)方法因其较高的计算精度和效率广泛应用于中子输运计算。然而,在含有局部源、弱散射介质的问题中,SN方法存在的射线效应问题一直未能得到较好的解决,严重制约了该方法在极端输运问题中的准确性和普适性。本文利用蒙特卡罗方法可对复杂函数进行精确数值积分的优点,提出了一种基于拟蒙特卡罗求积组的随机离散纵标(rSN)方法。初步研究表明,采用同等数量的离散角度时,该方法在强角度各向异性问题中的准确性优于传统求积组,而在弱角度各向异性问题中不如传统求积组。本文结合随机性和确定性求积组的优势,进一步提出了基于碰撞耦合的rSN-SN方法,并通过数值结果证明了该方法能以更少的计算时间和计算量获得更高的计算精度。

     

    Abstract: The discrete ordinates (SN) method proposed by Carlson constitutes an angular discretization for neutron transport equation. Owing to its superior computational accuracy and efficiency compared to alternative numerical approaches, the SN method has been extensively adopted in neutron transport applications, particularly in nuclear reactor design and radiation shielding optimization. Although the SN method demonstrates satisfactory performance and yields reliable results in most practical scenarios, it inherently suffers from ray effects, a well-documented numerical artifact that fundamentally limits its solution accuracy in the problems involving localized neutron source and wea scattering media. These angular discretization artifacts induce non-physical distortions in computed flux distributions, specifically generating artificial flux overestimations along preferential angular directions while suppressing flux magnitudes in geometrically unaligned regions. To address this defect, numerous of ray effect mitigation methods were developed. The virtual source method is formulated through the strategic introduction of pseudo-source terms into the SN equations, establishing equivalence with spherical harmonics (PN) formulations to eliminate ray effects. While this approach achieves angular flux correction by enforcing moment-matching conditions between SN and PN operators, its computational formalism becomes increasingly intricate due to higher-order spherical harmonic expansions, and suffers from convergence reliability concerns when handling multigroup problems with strongly anisotropic scattering coupling. The first collision source method, widely implemented in SN codes to mitigate ray effects, operates by employing alternative transport methodologies, such as ray tracing, Monte Carlo simulations, or PN expansions, to compute the uncollided flux component most severely impacted by ray effects, thereby reducing numerical distortions. However, this approach necessitates hybrid code architectures that integrate supplementary transport solvers with SN frameworks, substantially increasing computational complexity and programming challenges, while inheriting inherent limitations from the auxiliary methods: ray tracing struggles with reflective boundary conditions, Monte Carlo suffers from expensive computational costs, and PN method exhibit numerical oscillations in angularly anisotropic problems. In summary, ray effect severely constrains the computational accuracy of the SN method and have not yet been effectively resolved. In this paper, it is noticed that the SN method fundamentally constitutes a deterministic numerical integration scheme in angular dimension, whose characteristic ray effects arise from the inherent precision limitations of low-order angular quadrature techniques, particularly evident in non-smooth angular flux distributions with pronounced directional dependencies. Motivated by the capacity of Monte Carlo method to achieve precise integration of non-smooth functions, a novel random SN method based on quasi-Monte Carlo quadrature sets was proposed. Preliminary numerical results demonstrate that with equivalent numbers of discrete angular directions, the proposed method exhibits superior accuracy to conventional quadrature sets in strongly angularly anisotropic problems, but underperforms in weakly anisotropic scenarios. A collision-coupled rSN-SN methodology that synergistically integrates the advantages of stochastic and deterministic quadrature sets was further developed, with numerical verification confirming its capability to achieve enhanced computational precision at reduced computational time and resource expenditure compared to traditional implementations.

     

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