Multi-Objective Optimization of a Maneuvering Small Aircraft Turbine Engine Rotor System

This paper presents the multi-objective optimization of a small aircraft turbine engine rotor system subjected to maneuver loads. Application of a clustering algorithm, an unsupervised machine learning technique, to the Pareto front developed from multi-objective optimization of maneuvering aircraft rotor system is the novelty of the present work. An in-house finite element code is developed using MATLAB for the analysis of rotor system. Hybrid Genetic Algorithm is employed to simultaneously minimize the rotor response at maximum speed during maneuver and rotor response at critical speed with restrictions imposed on critical speed. Shaft diameters and pedestal stiffness at both the bearing locations are identified as design variables. Pareto optimal solutions are generated, clustering is carried out in both objective space and decision space and the solution close to the utopia point is selected as final compromise solution. The average of the values of design variables for the selected cluster is compared with final compromise solution and is found in good agreement. The response of the rotor system and the critical speeds are verified by carrying out tests on ground.