Application of Surrogate Models and Probabilistic Design Methodology to Assess Creep Growth Limit of an Uncooled Turbine Blade

Predictive lifing with probabilistic treatment of key variables represents a promising approach to realizing the digital gas turbine of the future. In this paper, we present a predictive model for creep life assessment of an uncooled turbine blade. The model development methodology draws on well-established machine learning principles to develop and validate a surrogate model for creep life from engine performance parameters. Verified creep life results, obtained from 3D non-linear thermo-mechanical finite element simulation for varying engine operating conditions are used as the basis for model development. The selection of model response surface order is studied over a range of models by evaluating normalized residual error on training and uncorrelated validation data sets. A model that is fully quadratic in the data set features is shown to have excellent predictive capability, yielding nominal creep life predictions to within ± 3% on the validation data set. This work then considers probabilistic techniques to evaluate the impact of uncertainty associated with each key factor on the predicted nominal creep life in order to achieve a mandated life target with a defined probability of failure.