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Robust Design Optimization of a Low Pressure Turbine Rotor Discs Secondary Air System

[+] Author Affiliations
Giulia Antinori, Andreas Fischersworring-Bunk

MTU Aero Engines, Munich, Germany

Ilya Arsenyev

Ferchau Engineering, Munich, Germany

Paper No. GT2017-63289, pp. V07AT32A001; 12 pages
doi:10.1115/GT2017-63289
From:
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 7A: Structures and Dynamics
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5092-3
  • Copyright © 2017 by ASME

abstract

Low pressure turbine (LPT) rotor discs undergo high thermal and mechanical loads during normal aircraft missions. Therefore, to meet the minimum requirement for life, temperatures and stresses in the disk need to be maintained within certain limits. This is achieved by carefully designing the disk shape and the cooling system. The complexity of this multi-physics problem together with a large number of design parameters require the use of numerical optimization methods for the Secondary Air System (SAS) design. Moreover, possible variations in the boundary conditions due to ambient parameters (e.g. temperatures, pressures) and manufacturing tolerances of the SAS components should be taken into account within the system design and optimization phase. In this paper an application of robust optimization methods for the design of a LPT secondary air system is proposed. The objective is to increase the engine efficiency by minimizing the amount of cooling flow, which is needed to guarantee a minimum required number of life cycles and to keep maximal temperatures within the limits. In order to predict the disks life accurately, transient thermal-structural analysis is used, which is computationally demanding. For this reason, optimization should be performed with a very limited amount of system evaluations. The dimension of the parameter space is reduced through the application of global sensitivity analysis methods by selecting the parameters that most affect the results. Optimization methods are sped up by the use of surrogate models, created over the reduced parameter space, which approximate the objective function and the constraints.

Copyright © 2017 by ASME

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