An online intelligent controller design based on hybridization of CFD and ANN for fuel consumption minimization of an aircraft in the vicinity of wavy sea waters

Abstract

The economic cruise advantages and the ever-increasing number of near-sea airports have underlined the idea of flight in the vicinity of the water surface in which both the ground effect and the surface wave shape are the major contributors to airplane performance. No attempts have been made to relate this wavy water proximity effect to airplane performance and optimization. In this study, intensive numerical simulations have been carried out on a general aviation airplane flying near the water’s wavy surface and the effects of a wavy deformable surface on the airplane aerodynamics were studied for various sea wave amplitudes. A hybridized Genetic Algorithm (GA) and Artificial Neural Network (ANN) were employed to predict the maximum lift-to-drag ratio (L/D) and minimum drag for various wave amplitudes. An online intelligent control based on Proportional-Integral-Derivative (PID) has been designed for the throttle lever actuator whose associated gains were determined by the Particle Swarm Optimization (PSO) algorithm to deliver the best control performance to attain the minimum fuel consumption. Using the computational fluid dynamics (CFD) data manipulated by the ANN, the speed for minimum drag for a given wave amplitude is determined. Based on the transfer function for the actuator of the throttle lever, the PID controller produces the necessary command to the actuator to move the throttle lever to the proper position where the thrust required for minimum drag flight in proportion to variable wave amplitude over the wavy sea surface is provided.