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Creep Life Prediction for Aero Gas Turbine Hot Section Component Using Artificial Neural Networks

[+] Author Affiliations
M. F. Abdul Ghafir

Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia

Y. G. Li, L. Wang

Cranfield University, Cranfield, Bedford, UK

Paper No. GT2012-68856, pp. 195-205; 11 pages
doi:10.1115/GT2012-68856
From:
  • ASME Turbo Expo 2012: Turbine Technical Conference and Exposition
  • Volume 3: Cycle Innovations; Education; Electric Power; Fans and Blowers; Industrial and Cogeneration
  • Copenhagen, Denmark, June 11–15, 2012
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4469-4
  • Copyright © 2012 by ASME

abstract

Accurate and reliable component life prediction is crucial to ensure safety and economics of gas turbine operations. In pursuit of such improved accuracy and reliability, model-based creep life prediction methods have become more and more complicated and therefore demand more computational time although they are more flexible in applications, in particular for new gas turbine engines. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to creep life prediction for production engines while at the same time maintain the same accuracy and reliability as that of the model-based methods. In this paper a novel creep life prediction approach using Artificial Neural Networks is introduced as an alternative to the model based creep life prediction approach to provide a quick and accurate estimation of gas turbine creep life. Multilayer feed forward back propagation neural networks have been utilised to form three neural network-based creep life prediction architectures known as the Range Based, Functional Based and Sensor Based architectures. The new neural network creep life prediction approach has been tested with a model single spool turboshaft gas turbine engine. The results show that good generalisation can be achieved in all three neural network architectures. It was also found that the Sensor-Based architecture is better than the other two in terms of accuracy, with 98% of the post-test samples possessing prediction errors within ± 0.4%. Overall, it can be concluded that the proposed neural network approach in creep life prediction is able to provide a good alternative to the more complicated model-based creep life prediction algorithms and can be applied to different types of gas turbine engines.

Copyright © 2012 by ASME

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