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      NEWS



  • Homage to Lofti A. Zadeh

    At IPMU2018 a well-deserved homage will be paid to his scientific work.
    Cádiz, Spain, June 11th - 15th, 2018

  • ISAPEP'21

    5th I W on Intelligent Systems for Agriculture Production and Environment Protection
    Dubai, United Arab Emirates, June 21th - 22th, 2021

  • NIP
    NIP-Software tool to manage
    low quality datasets
    © Univ. Murcia 2012
    R.P.I. nº 8/2012/700

  • FCTA 2011
    Best Student Paper Award
    "Constructing Fuzzy Partitions from Imprecise Data"
    J.M. Cadenas, M.C. Garrido, R. Martinez

  • FCTA 2012
    Best Paper Award
    "Towards an Approach to Select Features from Low Quality Datasets"
    J.M. Cadenas, M.C. Garrido, R. Martinez

Results

Miscellaneous experimental studies and results

We show some relevant research papers and some of their results. For each study, we provide its reference (plain text and BibTeX formats) and abstract.


  Year   Experimental Studies  Link   BibTex   Results 
2020 J.J Ortiz-Servin, D.A. Pelta, J.M. Cadenas, A. Castillo. A new methodology to speed-up fuel lattice design optimization using decision trees and new objective functions. Annals of Nuclear Energy xx, XXX--XXX, 2021.
  doi
zip.gif PPi
zip.gif UUi
Abstract [▲/▼]

In this paper a new methodology to speed up the fuel lattice design optimization in a BWR is explored. In previous works, fuel lattice optimization was made using LPPF (Local Power Peaking Factor) at the beginning of the fuel lattice life. However, undesirable LPPF vs. fuel lattice exposure behaviors were observed. Due to this, LPPF vs fuel lattice exposure was calculated through out fuel lattice life burnup. From a computational point of view, such calculation is very expensive when done using the CASMO-4 code. A new methodology to speed up the optimization was proposed based on two aspects: in one side, using objective functions that take into account LPPF vs. fuel lattice exposure and residual gadolinia; and in other side, using decision trees to estimate some fuel lattice parameters in a fast and reliable way. It could be verified that decision trees estimations had the enough reliability to be used into an optimization process to discard bad fuel lattice configurations and speed up the optimization process. At the end of this process, CASMO-4 code is used to calculate the final fuel lattice parameters. In this way, fuel lattice optimization time was reduced from 6 hours to 15 minutes obtaining good LPPF vs exposure behaviors.




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