Designing a Turbine Blade Cooling System Using a Generalised Regression Genetic Algorithm

dc.contributor.authorRoy, Rajkumar-
dc.contributor.authorTiwari, Ashutosh-
dc.contributor.authorCorbett, John-
dc.date.accessioned2014-06-19T04:00:54Z
dc.date.available2014-06-19T04:00:54Z
dc.date.issued2003-01-01T00:00:00Z-
dc.description.abstractThe design of a turbine blade cooling system is a multi-objective optimisation problem involving constraints and complex interaction among its design variables. The aim of this paper is to develop a methodology to optimise this design using Evolutionary Computing techniques. This paper presents Generalised Regression Genetic Algorithm (GRGA) and the mathematical model of a real-life turbine blade cooling system. Even in the presence of variable interaction, the methodology identifies a number of good feasible designs from which one could be finally chosen based on designer's preferences. The research also demonstrates that GRGA is capable of optimising a real-life design.en_UK
dc.identifier.citationR. Roy, A. Tiwari, J. Corbett. Designing a Turbine Blade Cooling System Using a Generalised Regression Genetic Algorithm. Cirp Annals-Manufacturing Technology, 2003, Vol 52, Issue 1, pages 415-418.
dc.identifier.issn0007-8506-
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/8552
dc.identifier.urihttp://dx.doi/10.1016/S0007-8506(07)60614-3
dc.language.isoen_UK-
dc.publisherTechnische Rundschau, Hallwag Publishers; 1999en_UK
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Cirp Annals. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Cirp Annals-Manufacturing Technology, 2003, Vol 52, Issue 1, pages 415-418. DOI:
dc.titleDesigning a Turbine Blade Cooling System Using a Generalised Regression Genetic Algorithmen_UK
dc.typeArticle-

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