WING SIZING OPTIMIZATION OF TURBOPROP AIRCRAFT BASED ON L/D MAXIMUM AND OPERATING EMPTY WEIGHT USING MULTIOBJECTIVE GENETIC ALGORITHM METHOD

• Main Author: Husnayati, Nila
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/46510
• Institution: Institut Teknologi Bandung

This thesis discusses about wing sizing optimization of turboprop aircraft in the conceptual design phase by using multiobjective genetic algorithm method. The constraints used in this research are geometry and performance. The main objective of this wing sizing optimization is to obtain a wing planform that provide maximum aircraft lift-to-drag ratio (L/D) and Operating Empty Weight (OEW) to meet the specified mission profile requirements: payload of 80 passengers, design range of 800nm, cruise altitude of 20,000 ft, cruise speed of 290 kts, and by using existing engines that are already available. This objective is motivated by the customer requirement for the aircraft to be competitive in its class, to conduct short and medium haul flights in terms of fuel consumption and weight so that itcan ultimately become one of the aspects that provide the minimum operating costs. In addition to the consideration of minimum operational costs, several other operational aspects will be used in the process of engineering judgment when selecting wing planforms resulting from multiobjective genetic algorithm calculations. Such as aircraft wing length is limited by the availability of fabrication in producing wing boxes, the maximum capacity of the fuel tank that can be carried in the aircraft’s wing, the stability of the aircraft and the influence of the aircraft on cabin height in general. The method used in this research is empirical calculation and optimization using MATLAB multiobjective genetic algorithm method as tools. The parameters used in this study are R80 aircraft design data. As an initial research, optimization is carried out on the wing planform assuming that the other components in aircraft are made constant. Optimization of the multiobjective genetic algorithm results a number of optimum point of wing design from both objectives. Then from a number of optimum points generated one point is choosen as the most optimum design by engineering judgments or design priorities, such as a design with lower MTOW.