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Science and Technology of Nuclear Installations
Volume 2014, Article ID 292916, 18 pages
http://dx.doi.org/10.1155/2014/292916
Research Article

Applying UPC Scaling-Up Methodology to the LSTF-PKL Counterpart Test

Technical University of Catalonia, Institute of Energy Technologies, Avenida Diagonal 647, 08028 Barcelona, Spain

Received 2 September 2013; Accepted 12 December 2013; Published 2 March 2014

Academic Editor: Eugenijus Ušpuras

Copyright © 2014 V. Martinez-Quiroga et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the framework of the nodalization qualification process and quality guarantee procedures and following the guidelines of Kv-scaled analysis and UMAE methodology, further development has been performed by UPC team resulting in a scaling-up methodology. Such methodology has been applied in this paper for analyzing discrepancies that appear between the simulations of two counterpart tests. It allows the analysis of scaling-down criterion used for the design of an ITF and also the investigation of the differences of configuration between an ITF and a particular NPP. For analyzing both, it applies two concepts “scaled-up nodalizations” and “hybrid nodalizations.” The result of this activity is the explanation of appeared distortions and its final goal is to qualify nodalizations for their use in the analysis of equivalent scenarios at an NPP scale. In this sense, the experimental data obtained in the OECD/NEA PKL-2 and ROSA-2 projects as counterpart test are of a great value for the testing of the present methodology. The results of the posttest calculations of LSTF-PKL counterpart tests have allowed the analyst to define which phenomena could be well reproduced by their nodalizations and which not, in this way establishing the basis for a future extrapolation to an NPP scaled calculation. The application of the UPC scaling up methodology has demonstrated that selected phenomena can be scaled-up and explained between counterpart simulations by carefully considering the differences in scale and design.