Inderscience Publishers

Optimisation of energy and exergy of turbofan engines using genetic algorithms

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This paper presents an application of genetic algorithm (GA) metaheuristics to optimise the design of two-spool separated-flow turbofan engines based on energy and exergy laws. The GA was used to seek the optimum values of eight parameters that defined the turbofan engine. A computer program called the TurboJet-Engine Optimiser v1.0 (TJEO-1.0) has been developed to perform thermodynamic property calculations of the engine and implement the optimisations. The TJEO-1.0 was integrated with Pyevolve, an open source GA optimisation framework built for use with Python programming language. The optimum designs created by TJEO-1.0 were evaluated with the following criteria: 1) energy efficiency; 2) exergy efficiency; 3) combination of both of them. Compared with the designs optimised for maximum energy efficiency, the designs optimised with the combination of energy and exergy efficiencies were able to produce 3.3%–11.0% extra specific thrust at the expense of 1.5%–2.3% extra fuel consumption.

Keywords: global optimisation, exergy, energy, genetic algorithms, aircraft engines, turbofan

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