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Evaluating Importance Maps for TRIPOLI-4® Using Deterministic or On-Line Methods

机译:使用确定性或在线方法评估TRIPOLI-4®的重要性图

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摘要

Variance reduction is a key ingredient for solving radiation-protection problems with Monte-Carlo particle-transport codes. Many variance-reduction methods require the definition of an importance map and exhibit optimal performance if the importance map is given by the adjoint flux, the solution of the adjoint Boltzmann equation. This paper presents the implementation of the Consistent Adjoint-Driven Importance Sampling (CADIS) methodology via a coupling between the TRIPOLI-4® Monte-Carlo particle-transport code and the IDT deterministic solver. Additionally, we describe the implementation of a new TRIPOLI-4® score that makes it possible to estimate the adjoint flux during a direct Monte-Carlo calculation. These new features are expected to simplify the solution of difficult shielding problems.
机译:减少差异是解决蒙特卡洛粒子传输代码的辐射防护问题的关键要素。如果重要性图由伴随通量(伴随Boltzmann方程的解)给出,则许多减少方差的方法都需要定义重要性图并表现出最佳性能。本文通过TRIPOLI-4®Monte-Carlo粒子传输代码与IDT确定性求解器之间的耦合,介绍了一致性伴随驱动的重要性抽样(CADIS)方法的实现。此外,我们描述了新的TRIPOLI-4®评分的实现方式,该评分使得可以在直接蒙特卡洛计算过程中估算伴随通量。这些新功能有望简化难于屏蔽的问题的解决方案。

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