A Genetic algorithms based optimisation tool for the preliminary design of gas turbine combustors

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dc.contributor.advisor Rubini, Philip A. en_UK
dc.contributor.author Rogero, J. M. en_UK
dc.date.accessioned 2005-11-23T14:33:28Z
dc.date.available 2005-11-23T14:33:28Z
dc.date.issued 2002-11 en_UK
dc.identifier.uri http://hdl.handle.net/1826/93
dc.description.abstract The aim of this research is to develop an optimisation tool to support the preliminary design of gas turbine combustors by providing a partial automation of the design process. This tool is to enable better design to be obtained faster, providing a reduction in the development costs and time to market of new engines. The first phase of this work involved the analysis of the combustor design process with the aim of identifying the critical tasks that are suitable for being automated and most importantly identifying the key parameters describing the performance of a combustor. During the second phase of this work an adequate design methodology for this problem was defined. This led to the development of a design optimisation Toolbox based on genetic algorithms, containing the tools required for it's proper integration into the combustor preliminary design environment. For the development of this Toolbox, extensive work was performed on genetic algorithms and derived techniques in order to provide the most efficient and robust optimisation method possible. The optimisation capability of the Toolbox was first validated and metered on analytical problems of known solution, where it demonstrated excellent optimisation performance especially for higher-dimensional problems. In a second step of the testing and validation process the combustor design capability of the Toolbox was demonstrated by applying it to diverse combustor design test cases. There the Toolbox demonstrated its capacity to achieve the required performance targets and to successfully optimise some key combustor parameters such as liner wall cooling flow and NOx emissions. In addition, the Toolbox demonstrated its ability to be applied to different types of engineering problems such as wing profile optimisation. en_UK
dc.format.extent 1883 bytes
dc.format.extent 3977944 bytes
dc.format.mimetype text/plain
dc.format.mimetype application/pdf
dc.language.iso en_UK en_UK
dc.publisher Cranfield University en_UK
dc.relation.uri http://www.cranfield.ac.uk/~p.a.rubini en_UK
dc.subject.other optimisation tool en_UK
dc.subject.other liner wall cooling flow en_UK
dc.subject.other NOx emissions en_UK
dc.subject.other fuel injection en_UK
dc.subject.other genetic algorithms en_UK
dc.title A Genetic algorithms based optimisation tool for the preliminary design of gas turbine combustors en_UK
dc.type Thesis or dissertation en_UK
dc.type.qualificationlevel Doctoral
dc.type.qualificationname PhD
dc.publisher.department School of Engineering

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