In the current paper, efficient uncertainty quantification analysis of the gas turbine blade with film cooling is performed using a bi-fidelity surrogate model. The proposed method is based on the combination of sparse polynomial chaos expansion and Kriging metamodels. The orthogonal matching pursuit technique is employed to retrieve dominant expansion basis functions via low-fidelity calculations and, the high-fidelity computations are estimated based on the most dominant basis. In the developed method, the selected dominant basis functions are used as the trend function of the Kriging method, while the stochastic Gaussian part is estimated via a limited number of high-fidelity calculations. Two challenging analytical test functions are considered to investigate the performance of the metamodel. In the blade leading edge film cooling test case, combination of ten operational and geometrical uncertain parameters are examined. The results show that implementing the multi-fidelity approach in sparse polynomial chaos expansion reduces the computational cost by more than 40% and 80% in comparison to the sparse and full polynomial chaos expansion methods, respectively. In addition, results of sensitivity analysis show that the mainstream angle of attack and the compound angle of coolant hole mostly influence the film cooling adiabatic effectiveness.
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