Optimization of a centrifugal fan impeller using kriging simulated annealing

Sertac Cadirci*, Bugra Selenbas, Hasan Gunes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

In this study we present models for the parametric optimization of a centrifugal fan impeller using krigingsimulated annealing (SA) meta-algorithm. First, a kriging model is constructed using a limited number of CFD simulations for the centrifugal fan impeller to be optimized. The inlet and outlet blade angles are chosen to optimize the impeller. A dataset consisting of 22 different blade angles are determined by Latin Hypercube Sampling (LHS). After validation of the kriging model, it is used in conjunction with the simulated annealing and thus a meta-algorithm is developed for the solution of global optimization problem for the impeller optimization. Within the desired range of parameters, it is shown that this meta-algorithm provides a robust, reliable and fast optimization method. The procedures can be used to many problems in engineering. In this study a centrifugal fan impeller is successfully optimized using this procedure.

Original languageEnglish
Title of host publicationFluids and Thermal Systems; Advances for Process Industries
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages991-997
Number of pages7
EditionPARTS A AND B
ISBN (Print)9780791854921
DOIs
Publication statusPublished - 2011
EventASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011 - Denver, CO, United States
Duration: 11 Nov 201117 Nov 2011

Publication series

NameASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011
NumberPARTS A AND B
Volume6

Conference

ConferenceASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011
Country/TerritoryUnited States
CityDenver, CO
Period11/11/1117/11/11

Keywords

  • Computational fluid dynamics (CFD)
  • Impeller design
  • Kriging
  • Optimization
  • Simulated annealing (SA)

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