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 complicated and demand higher computational time. 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 backpropagation neural networks have been utilized 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 generalization 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%.
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March 2014
Research-Article
Creep Life Prediction for Aero Gas Turbine Hot Section Component Using Artificial Neural Networks
L. Wang
L. Wang
e-mail: L.Wang2010@hotmail.co.uk
School of Engineering,
School of Engineering,
Cranfield University
,Cranfield, Bedford MK43 0AL
, UK
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M. F. Abdul Ghafir
Y. G. Li
e-mail: i.y.li@cranfield.ac.uk
L. Wang
e-mail: L.Wang2010@hotmail.co.uk
School of Engineering,
School of Engineering,
Cranfield University
,Cranfield, Bedford MK43 0AL
, UK
Contributed by the Cycle Innovations Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received April 29, 2013; final manuscript received September 2, 2013; published online November 14, 2013. Editor: David Wisler.
J. Eng. Gas Turbines Power. Mar 2014, 136(3): 031504 (9 pages)
Published Online: November 14, 2013
Article history
Received:
April 29, 2013
Revision Received:
September 2, 2013
Citation
Abdul Ghafir, M. F., Li, Y. G., and Wang, L. (November 14, 2013). "Creep Life Prediction for Aero Gas Turbine Hot Section Component Using Artificial Neural Networks." ASME. J. Eng. Gas Turbines Power. March 2014; 136(3): 031504. https://doi.org/10.1115/1.4025725
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