Cruise range modeling of different flight strategies for transport aircraft using genetic algorithms and particle swarm optimization

Cruise flight profile accounts for the highest percentage of fuel consumption on long-haul flights, which makes the optimization of cruise range crucial for a net-zero, sustainable, secure, and affordable energy future. Therefore, flying at altitudes close to the optimum cruise altitude will significantly reduce fuel consumption and fuel-related emissions; It will also increase cruise range. In this context, accurate range calculations are critical to evaluate and understand the environmental and economic impacts of all aircraft. This paper proposes two novel non-conventional models of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to estimate the cruise range of jet powered transport aircraft as a first attempt in the literature. Using both methods, non-linear models which provide range formulations dependent on velocity, aircraft weight and cruise flight altitude were developed for the cruise flight strategies including constant altitude-constant lift coefficient and constant altitude-constant Mach number. The accuracies of both derived models were analyzed and it was seen that both models are capable of providing satisfactory cruise range prediction results.