Gas turbine performence based creep life estimation using soft computing technique - Mohamed Zarti, Almehdi (2012)
Accurate and simple prediction system has become an urgent need in most disciplines. Having the accurate prediction system for gas turbine components will allow the user to produce reliable creep life prediction. Focusing on the turbine blades and its life, the current method to calculate its creep life is complex and consumes a lot of time. For this reason, the aim of this research is to use an alternative performance–based creep life estimation that is able to provide a quick solution and obtain accurate creep life prediction. By the use of an artificial neural network to predict creep life, a neural network architecture called Sensor Life Based (SLB) architecture that produces a direct mapping from gas path sensor to predict the blade creep life was created by using the gas turbine simulation performance software. The performance of gas turbine and the effects of multiple operations on the blade are studied. The result of the study is used to establish the input and output to train the Sensor Life Based network. The result shows that the Sensor Life-Based architecture is able to produce accurate creep life predictions yet performing rapid calculations. The result also shows that the accuracy of prediction depends on the way, how the gas path sensor is grouped together.