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

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, modelbased 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 modelbased methods. In this paper, a novel creep life prediction approach using artificial neural networks is introduced as an alternative to the modelbased 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 rangebased, functionalbased, and sensorbased architectures. The new neural network creep life prediction approach has been tested with a model singlespool 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 sensorbased architecture is better than the other two in terms of accuracy, with 98% of the posttest samples possessing prediction errors within آ±0.4%.