  This paper presents an application of Genetic Algorithm (GA) metaheuristics
to optimise the design of two-spool turbofan engines based on exergy and energy
theories. The GA is used to seek the optimal values of eight parameters that
define the turbofan engine. These parameters are encoded as chromosomes (a
metaphor) in GA. A computer program called TurboJet-Engine Optimizer v1.0
(TJEO-1.0) was developed by the authors to perform calculations of
thermodynamic properties of the engine and implement the optimisations. The
caloric properties of the working fluids are obtained by using an 8th-order
polynomial fluid model taken from an existing literature. The turbo-mechanical
components in the turbofan engine are as well assumed to exhibit polytropic
efficiencies of today's technology level. The TJEO-1.0 is integrated with
Pyevolve, an open source GA optimisation framework built for used with Python
programming language. The optimal design created by TJEO-1.0 was evaluated with
the following criteria: (1.) energy efficiency, (2.) exergy efficiency, and
(3.) combination of both of them. The results suggest that the design of
turbofan engine optimised based on the third criterion is able to produce
higher specific thrust (by more than 30 %), compared with the ones optimised
based on the first and the second criteria.

Optimization and Control