System optimization of turbofan engines using genetic algorithms

Abstract This paper presents an application of genetic algorithms to the system optimization of turbofan engines. Genetic algorithms are relatively new general-purpose optimization algorithms that apply the rules of natural genetics to explore a given search space. In order to characterize the many measures of aricraft engine performance, two different criteria are chosen for evaluation. These criteria are thrust per unit mass flow rate and overall efficiency. These criteria are optimized using four key parameters including Mach number, compressor pressure ratio, fan pressure ratio, and bypass ratio. After observing how each parameter influences objective functions independently, the two objective functions are combined to examine their interaction in a multiobjective function optimization. Numerical results indicate that genetic algorithms are capable of optimizing a complex system quickly. The resultant parameter values agree well with previous studies.