A data-driven polynomial chaos method for uncertainty quantification of a subsonic compressor cascade with stagger angle errors
Haohao Wang,
Limin Gao,
Guang Yang
et al.
Abstract:The probability-based uncertainty quantification (UQ) methods require a large amount of sampled data to construct the probability distribution of uncertain input parameters. However, it is a common situation that only limited and scarce sampled data are available in engineering applications due to expensive tests. In the present paper, the Data-Driven Polynomial Chaos (DDPC) method is adopted, which can propagate input uncertainty in the case of scarce sampled data. The calculation accuracy and convergence of … Show more
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