Multi-objective Horizontal Stabilizer Optimization Using Genetic Algorithm

Multi-objective optimization of horizontal tail of a conventional rotor helicopter is achieved using a genetic algorithm, which is coupled with comprehensive analysis tools, Flightlab® and in-house rotorcraft simulation tool TAI Originated Rotorcraft Simulation (TOROS). Genetic algorithm is used to design a tail that improves static longitudinal stability characteristics of the helicopter during autorotation as well as its longitudinal dynamic stability characteristics at high speeds. Another optimization target is to minimize pitch attitude change in transition to forward flight while keeping pitch attitude close to zero at 140 knots in cruise. This study shows a framework of horizontal stabilizer optimization over its aerodynamic lift characteristics, which can be altered either by introducing gurney flaps and/or slats to change lift characteristics, vortex generators (turbulators) to postpone flow separation or simply by changing incidence angle. By solving a multi-objective optimization problem, the aim of this study is to find an optimum lift curve slope, incidence angle and size of the horizontal stabilizer that satisfies performance and handling qualities requirements of the helicopter. It was witnessed that there are always some trade-offs between optimal design alternatives in Pareto Front and therefore selection of optimum horizontal tail configuration depends highly on these requirements. This study shows that multi-objective optimization can be used as a design tool to replace human effort prior to or during the flight tests by tuning aerodynamic configuration and size of the horizontal tail.