A multi-objective genetic algorithm (MOGA) for the optimization of aeronautical gas turbine discs is herein presented. The aim of this work is to propose an original design strategy that can be validated against laboratory tests but going further experimental conditions. Thus, the fatigue life prediction together with the total weight is taken as optimization objectives in the geometric design of a rig turbine disc. Sensitivity analysis of the system is here a basic tool to understand the behavior of the model to its parameters; therefore, a parametric study and its inherent design of experiments (DoE) are accomplished prior to develop a detailed low-fatigue life prediction using FEM. The analysis method used is based on surrogate modeling and robust design optimization methods. To begin with, the first case study considers laboratory test conditions, basically centrifugal loads due to the blades and the own mass of the disc, while the second case study considers real conditions such as the centrifugal loads, airflow forces and thermal loads. The results show that the design method successfully reveals the optimal geometric parameters both for laboratory and real conditions, so the main conclusions and qualitative differences are finally summarized.
7th International Conference on Advanced Computational Engineering and Experimenting - ACE-X 2013, Madrid (Spain). 01-04 July 2013
Publication date: July 2013.
F.J. García-Revillo, J.R. Jiménez-Octavio, A. Cantizano, C. Sanchez-Rebollo, Multi-objective optimization of gas turbine discs based on low-fatigue life prediction, 7th International Conference on Advanced Computational Engineering and Experimenting - ACE-X 2013. Madrid, Spain, 01-04 July 2013