The Use of an Artificial Neural Network for On-Line Prediction of Pin-Cell Discontinuity Factors in PARCS

机译：人工神经网络在线预测PARCS中针状细胞不连续性因素

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During the last several years, the U.S. NRC has sponsored therndevelopment of multi-group, SP3 method in the Purdue Advanced ReactorrnCore Simulator (PARCS) primarily for MOX fuel analysis. These methodsrnwere implemented within the framework of pin-by-pin discretization usingrnpin homogenized cross sections. Pin-Cell Discontinuity Factors (PDFs)rnwere proposed in order to recover the error introduced by pin cellrnhomogenization. The method showed the potential to improve thernaccuracy of the pin power prediction; however its application for practicalrncore problems was limited because of the considerable amount of datarnrequired for whole core calculations and because of uncertainties in thernapplication of PDFs to core conditions for which they were not generated.rnThe work reported here is the development of innovative methods tornimplement PDFs for practical applications using an Artificial NeuralrnNetwork (ANN). The work is demonstrated using the KAIST MOXrnbenchmark.

机译：在过去的几年中，美国NRC赞助了在Purdue Advanced ReactorrnCore Simulator（PARCS）中主要用于MOX燃料分析的多组SP3方法的开发。这些方法是在使用销均质化横截面的逐销离散化框架内实现的。提出了销孔不连续性因素（PDFs）以恢复销孔不均质化引入的误差。该方法显示了改善引脚功率预测准确性的潜力。但是，由于整个核心计算需要大量数据，并且由于将PDF应用于未生成核心条件的不确定性，因此它在实际核心问题中的应用受到限制。这里报道的工作是开发创新方法来实现PDF的实现。使用人工神经网络（ANN）的实际应用。使用KAIST MOXrnbenchmark演示了这项工作。

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来源
《PHYSOR 2004 - the physics of fuel cycles and advanced nuclear systems: global developments》|2004年|1-11|共11页
会议地点 Chicago IL(US)
作者
Tomasz Kozlowski; Deokjung Lee; Thomas J. Downar;
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作者单位

Purdue University, 1290 Nuclear Engineering Building, West Lafayette, IN 47907 USA Tel. 765-494-7828, FAX 756-494-9570, E-mail: tomasz@ecn.purdue.edu;

Purdue University, 1290 Nuclear Engineering Building, West Lafayette, IN 47907 USA;

Purdue University, 1290 Nuclear Engineering Building, West Lafayette, IN 47907 USA;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Pin Cell; Discontinuity Factors; Neural Network;

机译：销细胞;不连续性因素；神经网络;

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The Use of an Artificial Neural Network for On-Line Prediction of Pin-Cell Discontinuity Factors in PARCS

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