Multi-parameter aerodynamic design of a horizontal tail using an optimization approach

As one of the main components of conventional aircraft, the horizontal tail is of significant importance to safety; therefore, the horizontal tail is expected to achieve its goals in an optimum manner. The paramount importance of this study is addressing the designer's need to achieve harmonization between flight sciences such as aerodynamics, stability, and control, as well as manufacturing easiness, maintainability, and manufacturing cost throughout its service life. The main problem is defined as the minimum horizontal tail area that can meet the requirements of civil aviation regulations and other safety issues while improving cruise performance. Designing a horizontal tail with the smallest area has crucial advantages, such as lighter weight, lower drag, the forward-located center of gravity, lower slipstream effect when the propeller-on, longer cruise range, and lower manufacturing costs. This paper presents multi-parameter optimization of the horizontal tail using a multi-objective genetic algorithm, whereas the algorithm is fed by a stability derivative generator that is created using the artificial neural network trained with 225 different horizontal tail geometries' stability derivatives. The capabilities of the optimized geometry are examined by using a 6-degree-of-freedom mathematical model courtesy of the broad aerodynamic database of the base aircraft.