Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process
Stochastic variations of the operation conditions and the resultant variations of the aerodynamic performance in Low-Pressure Turbine (LPT) can often be found. This paper studies the aerodynamic performance impact of the uncertain variations of flow parameters, including inlet total pressure, inlet...
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MDPI AG
2023-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/12/1022 |
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| author | Wenhao Fu Zeshuai Chen Jiaqi Luo |
| author_facet | Wenhao Fu Zeshuai Chen Jiaqi Luo |
| author_sort | Wenhao Fu |
| collection | DOAJ |
| description | Stochastic variations of the operation conditions and the resultant variations of the aerodynamic performance in Low-Pressure Turbine (LPT) can often be found. This paper studies the aerodynamic performance impact of the uncertain variations of flow parameters, including inlet total pressure, inlet flow angle, and turbulence intensity for an LPT cascade. Flow simulations by solving the Reynolds-averaged Navier–Stokes equations, the SST turbulence model, and <inline-formula><math display="inline"><semantics><mrow><mi>γ</mi><mo>−</mo><msub><mover accent="true"><mrow><mi>R</mi><mi>e</mi></mrow><mo>˜</mo></mover><mrow><mi>θ</mi><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula> transition model equations are first carried out. Then, a Gaussian process (GP) based on an adaptive sampling technique is introduced. The accuracy of adaptive GP (ADGP) is proven to be high through a function experiment. Using ADGP, the uncertainty propagation models between the performance parameters, including total pressure-loss coefficient, outlet flow angle, Zweifel number, and the uncertain inlet flow parameters, are established. Finally, using the propagation models, uncertainty quantifications of the performance changes are conducted. The results demonstrate that the total pressure-loss coefficient and Zweifel number are sensitive to uncertainties, while the outlet flow angle is almost insensitive. Statistical analysis of the flow field by direct Monte Carlo simulation (MCS) shows that flow transition on the suction side and viscous shear stress are rather sensitive to uncertainties. Moreover, Sobol sensitivity analysis is carried out to specify the influence of each uncertainty on the performance changes in the turbine cascade. |
| first_indexed | 2024-03-08T21:05:36Z |
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| id | doaj.art-82376a1e40fb429c885986bd6134ece0 |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-08T21:05:36Z |
| publishDate | 2023-12-01 |
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| series | Aerospace |
| spelling | doaj.art-82376a1e40fb429c885986bd6134ece02023-12-22T13:45:15ZengMDPI AGAerospace2226-43102023-12-011012102210.3390/aerospace10121022Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian ProcessWenhao Fu0Zeshuai Chen1Jiaqi Luo2School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaSchool of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, ChinaStochastic variations of the operation conditions and the resultant variations of the aerodynamic performance in Low-Pressure Turbine (LPT) can often be found. This paper studies the aerodynamic performance impact of the uncertain variations of flow parameters, including inlet total pressure, inlet flow angle, and turbulence intensity for an LPT cascade. Flow simulations by solving the Reynolds-averaged Navier–Stokes equations, the SST turbulence model, and <inline-formula><math display="inline"><semantics><mrow><mi>γ</mi><mo>−</mo><msub><mover accent="true"><mrow><mi>R</mi><mi>e</mi></mrow><mo>˜</mo></mover><mrow><mi>θ</mi><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula> transition model equations are first carried out. Then, a Gaussian process (GP) based on an adaptive sampling technique is introduced. The accuracy of adaptive GP (ADGP) is proven to be high through a function experiment. Using ADGP, the uncertainty propagation models between the performance parameters, including total pressure-loss coefficient, outlet flow angle, Zweifel number, and the uncertain inlet flow parameters, are established. Finally, using the propagation models, uncertainty quantifications of the performance changes are conducted. The results demonstrate that the total pressure-loss coefficient and Zweifel number are sensitive to uncertainties, while the outlet flow angle is almost insensitive. Statistical analysis of the flow field by direct Monte Carlo simulation (MCS) shows that flow transition on the suction side and viscous shear stress are rather sensitive to uncertainties. Moreover, Sobol sensitivity analysis is carried out to specify the influence of each uncertainty on the performance changes in the turbine cascade.https://www.mdpi.com/2226-4310/10/12/1022uncertainty quantificationlow-pressure turbineGaussian processadaptive samplingstatistical analysisSobol analysis |
| spellingShingle | Wenhao Fu Zeshuai Chen Jiaqi Luo Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process Aerospace uncertainty quantification low-pressure turbine Gaussian process adaptive sampling statistical analysis Sobol analysis |
| title | Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process |
| title_full | Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process |
| title_fullStr | Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process |
| title_full_unstemmed | Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process |
| title_short | Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process |
| title_sort | aerodynamic uncertainty quantification of a low pressure turbine cascade by an adaptive gaussian process |
| topic | uncertainty quantification low-pressure turbine Gaussian process adaptive sampling statistical analysis Sobol analysis |
| url | https://www.mdpi.com/2226-4310/10/12/1022 |
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