Design Optimization of Aircraft Landing System Rolling Stock and Bogie
Gaussian Process Regression
With the rapid development in the aircraft designs of the aerospace industry, the traditional select and pick design routine needs to be developed and improved and a new design methodology with fewer iterations must be proposed to meet all design metrics. This research aims to develop a method that designs the landing gear rolling stock and bogie configuration for the preliminary design stage, which integrates a high-fidelity multidisciplinary design optimization and Gaussian Process Regression techniques focusing on weight minimization, structure and configuration optimization. The overall design optimization process is wrapped in NASA OpenMDAO open source framework and contains four analysis subsystems: tire selection analysis, flotation analysis, brakes and wheels analysis, and structural linking beams analysis. In the tire selection analysis, the radial tire is represented by using four continuous design variables and Gaussian Process Regression is used to do the metamodeling based on the known tire data. In the flotation analysis, the unpaved airfield compatibility analysis is conducted to make sure the aircraft can operate on the required runway. In the brakes and wheels analysis, the required brake size is calculated based on the kinetic energy of the landing or rejected takeoff and the mass of brake assembly and wheel are estimated based on the empirical equations. In the structural linking beams analysis, the dimensions of each tube are computed based on the loading case and safety margins are defined by the user. The mass of each tube is added up to be the total structural mass. The overall mass of the landing gear consists of tire mass, brakes assembly mass, wheels mass, and structural mass. It should be noted that the safety margins refer to the safety factors minus 1 in this study. Finally, by implementing the case studies on A320-200 landing gear, the capability and feasibility of the proposed methodology have been demonstrated with several optimized solutions under different constraint conditions. If the same constraint conditions as the original design are considered as re-design 4, the total weight can be reduced by 10%. If loose constraint conditions are considered, the weight can be reduced up to 28.7%, 10.9%, and 10.7%, respectively as re-design 1, re-design 2, and re-design 3.