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contributor authorWang, Haohao
contributor authorGao, Limin
contributor authorYang, Guang
contributor authorLi, Ruiyu
contributor authorWu, Baohai
date accessioned2024-12-24T18:54:38Z
date available2024-12-24T18:54:38Z
date copyright7/4/2024 12:00:00 AM
date issued2024
identifier issn0742-4795
identifier othergtp_146_11_111004.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4302968
description abstractIn engineering practice, the amount of measured data is often scarce and limited, posing a challenge in uncertainty quantification (UQ) and propagation. Data-driven polynomial chaos (DDPC) is an effective way to tackle this challenge. However, the DDPC method faces problems from the lack of robustness and convergence difficulty. In this paper, a preconditioner-based data-driven polynomial chaos (PDDPC) method is developed to deal with UQ problems with scarce measured data. Two numerical experiments are used to validate the computational robustness, convergence property, and application potential in case of scarce data. Then, the PDDPC is first applied to evaluate the uncertain impacts of real leading edge (LE) errors on the aerodynamic performance of a two-dimensional compressor blade. Results show that the overall performance of compressor blade is degraded and there is a large performance dispersion at off-design incidence conditions. The actual blade performance has a high probability of deviating from the nominal performance. Under the influence of uncertain LE geometry, the probability distributions of the total pressure loss coefficient and static pressure ratio have obvious skewness characteristics. Compared with the PDDPC method, the UQ results obtained by the fitted Gaussian and Beta probability distributions seriously underestimate the performance dispersion of compressor blade. The mechanism analysis illustrates that the large flow variation around the leading edge is the main reason for the overall performance degradation and the fluctuations of the entire flow field.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Preconditioner-Based Data-Driven Polynomial Expansion Method: Application to Compressor Blade With Leading Edge Uncertainty
typeJournal Paper
journal volume146
journal issue11
journal titleJournal of Engineering for Gas Turbines and Power
identifier doi10.1115/1.4065787
journal fristpage111004-1
journal lastpage111004-14
page14
treeJournal of Engineering for Gas Turbines and Power:;2024:;volume( 146 ):;issue: 011
contenttypeFulltext


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