@inproceedings{8e9a7f601bae4b4a9599173604dcfdd1,
title = "The use of an Artificial Neural Network for on-line prediction of Pin-Cell Discontinuity Factors in PARCS",
abstract = "During the last several years, the U.S. NRC has sponsored the development of multi-group, SP3 method in the Purdue Advanced Reactor Core Simulator (PARCS) primarily for MOX fuel analysis. These methods were implemented within the framework of pin-by-pin discretization using pin homogenized cross sections. Pin-Cell Discontinuity Factors (PDFs) were proposed in order to recover the error introduced by pin cell homogenization. The method showed the potential to improve the accuracy of the pin power prediction; however its application for practical core problems was limited because of the considerable amount of data required for whole core calculations and because of uncertainties in the application of PDFs to core conditions for which they were not generated. The work reported here is the development of innovative methods to implement PDFs for practical applications using an Artificial Neural Network (ANN). The work is demonstrated using the KAIST MOX benchmark.",
keywords = "Discontinuity Factors, Neural Network, Pin Cell",
author = "Tomasz Kozlowski and Deokjung Lee and Downar, {Thomas J.}",
year = "2004",
language = "English (US)",
isbn = "0894486837",
series = "Proceedings of the PHYSOR 2004: The Physics of Fuel Cycles and Advanced Nuclear Systems - Global Developments",
pages = "613--623",
booktitle = "Proceedings of the PHYSOR 2004",
note = "PHYSOR 2004: The Physics of Fuel Cycles and Advanced Nuclear Systems - Global Developments ; Conference date: 25-04-2004 Through 29-04-2004",
}