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A New Formulation of the Unified Monte Carlo Approach (UMC-B) and Cross-Section Evaluation for the Dosimetry Reaction ~(55)Mn(n,γ)~(56)Mn

机译:〜(55)Mn(n,γ)〜(56)Mn剂量学反应的统一蒙特卡罗方法(UMC-B)的新公式和截面评估

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Two relatively new approaches to neutron cross section data evaluation are described. They are known collectively as Unified Monte Carlo (versions UMC-G and UMC-B). Comparisons are made between these two methods, as well as with the well-known generalized least-squares (GLSQ) technique, through the use of simple, hypothetical (toy) examples. These new Monte Carlo methods are based on stochastic sampling of probability functions that are constructed with the use of theoretical and experimental data by applying the principle of maximum entropy. No further assumptions are involved in either UMC-G or UMC-B. However, the GLSQ procedure requires the linearization of non-linear terms, such as those that occur when cross section ratio data are included in an evaluation. It is shown that these two stochastic techniques yield results that agree well with each other, and with the GLSQ method, when linear data are involved, or when the perturbations due to data discrepancies and nonlinearity effects are small. Otherwise, there can be noticeable differences. The present investigation also demonstrates, as observed in earlier work, that the least-squares approach breaks down when these conditions are not satisfied. This paper also presents an actual evaluation of the ~(55)Mn(n,γ)~(56)Mn neutron dosimetry reaction cross section in the energy range from "100keV to 20 MeV, which was performed using both GLSQ and UMC-G approaches.
机译:描述了两种相对新的中子截面数据评估方法。它们统称为统一蒙特卡洛(UMC-G和UMC-B版本)。通过使用简单的假设(玩具)示例,可以对这两种方法以及众所周知的广义最小二乘(GLSQ)技术进行比较。这些新的蒙特卡洛方法基于概率函数的随机采样,这些概率函数是通过应用最大熵原理使用理论和实验数据构建而成的。 UMC-G或UMC-B不涉及其他假设。但是,GLSQ过程要求将非线性项线性化,例如在评估中包含横截面比率数据时发生的非线性项。结果表明,当涉及线性数据时,或由于数据差异和非线性影响而引起的扰动较小时,这两种随机技术所产生的结果彼此之间以及与GLSQ方法的一致性都很好。否则,可能会有明显的差异。正如在早期工作中观察到的那样,本研究还表明,当不满足这些条件时,最小二乘法会失效。本文还介绍了在(100keV至20 MeV的能量范围内)〜(55)Mn(n,γ)〜(56)Mn中子剂量学反应截面的实际评估,该评估是使用GLSQ和UMC-G进行的方法。

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