Nested sparse-grid Stochastic Collocation Method for uncertainty quantification of blade stagger angle

Wang Kun, Chen Fu, Yu Jianyang and Song Yanping

Energy, 2020, vol. 201, issue C

Abstract: In the present paper, the Nested Sparse-grid Stochastic Collocation Method (NSSCM) is utilized to investigate the uncertain effects of stochastic blade stagger angles on the aerodynamic performance of the turbine blade. Two typical test functions are used to validate the calculation efficiency and precision of the NSSCM in Uncertainty Quantification (UQ). Based on the validation, non-deterministic CFD (Computational Fluid Dynamics) simulations are applied to estimate the impact on the aerodynamic performance of 2D (two-dimensional) turbine blades cases, and the blade stagger angles are considered as the stochastic variables in a Gaussian distribution. Four schemes of a single blade, two adjacent blades, two separated blades, and five blades with stochastic blade stagger angle errors are studied. The calculation results show that the aerodynamic performance was significantly influenced, and the stochastic fluctuations of total pressure loss caused by uncertain stagger angles can be up to 40%. The uncertain errors of the blade stagger angle have a significant effect on the suction side, and the wake region, and the 30%–50% axial region on the blade suction side is the sensitive region. The effects on the flow field propagate from the pressure side to the suction side.

Keywords: Turbine blade; Aerodynamic performance; Uncertainty quantification; Stochastic collocation; Nested sparse-grid technology; Blade stagger angle error (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:201:y:2020:i:c:s0360544220306903

DOI: 10.1016/j.energy.2020.117583

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