Uncertainty Quantification for Aerothermal Characteristics of HP Turbine Vanes Under Combined Hot-Streak and Turbulence Intensity Effects

This study presents a systematic framework for quantifying aerothermal uncertainties in high-pressure turbine nozzle guide vanes (NGV) under combustor-turbine interaction, focusing on the combined impacts of hot streak spatial variations and turbulence intensity fluctuations. By integrating parametric modeling of combustor-exit temperature fields, non-intrusive polynomial chaos expansion (PCE), and Sobol sensitivity analysis, the methodology enables probabilistic evaluation of aerothermal performance across arbitrary turbine locations. Conjugate heat transfer simulations were conducted to analyze the effect of stochastic parameters on the NGV metal temperature uncertainty. The findings reveal that cooled NGVs exhibit an 80% increase in mean total pressure loss and 42% higher fluctuation amplitudes, driven by enhanced midspan mixing and counter-rotating vortices. Localized metal temperature fluctuations reach 4.3% of inlet total temperature, concentrated in cooling transition zones and secondary flow paths. Turbulence intensity dominates uncertainty contributions, while hot streak circumferential variations show minimal influence. The PCE based framework, augmented by Hammersley sampling, achieves computational efficiency with 20 samples, demonstrating robust capability for cooling system design under realistic inflow uncertainties. This work advances probabilistic aerothermal analysis methodologies, offering critical insights for turbine architectures operating under lean-burn combustor conditions.

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Publication Details

Journal
Aerospace Research Communications
Published
2025-08-29
DOI
https://doi.org/10.3389/arc.2025.14801
Citations
1
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
1.40

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article

Uncertainty Quantification for Aerothermal Characteristics of HP Turbine Vanes Under Combined Hot-Streak and Turbulence Intensity Effects

Ruocheng Li, Liangliang Wang, Zhenping Feng, Jinze Li et al.
1 citations
Aerospace Research Communications
Probabilistic and Robust Engineering Design
1.40
article

Uncertainty Quantification for Aerothermal Characteristics of HP Turbine Vanes Under Combined Hot-Streak and Turbulence Intensity Effects

Ruocheng Li, Liangliang Wang, Zhenping Feng, Jinze Li, Zhiduo Wang, Bei Zhang, X. Du, Xiangyu Wang
article en
1 citations

Abstract

This study presents a systematic framework for quantifying aerothermal uncertainties in high-pressure turbine nozzle guide vanes (NGV) under combustor-turbine interaction, focusing on the combined impacts of hot streak spatial variations and turbulence intensity fluctuations. By integrating parametric modeling of combustor-exit temperature fields, non-intrusive polynomial chaos expansion (PCE), and Sobol sensitivity analysis, the methodology enables probabilistic evaluation of aerothermal performance across arbitrary turbine locations. Conjugate heat transfer simulations were conducted to analyze the effect of stochastic parameters on the NGV metal temperature uncertainty. The findings reveal that cooled NGVs exhibit an 80% increase in mean total pressure loss and 42% higher fluctuation amplitudes, driven by enhanced midspan mixing and counter-rotating vortices. Localized metal temperature fluctuations reach 4.3% of inlet total temperature, concentrated in cooling transition zones and secondary flow paths. Turbulence intensity dominates uncertainty contributions, while hot streak circumferential variations show minimal influence. The PCE based framework, augmented by Hammersley sampling, achieves computational efficiency with 20 samples, demonstrating robust capability for cooling system design under realistic inflow uncertainties. This work advances probabilistic aerothermal analysis methodologies, offering critical insights for turbine architectures operating under lean-burn combustor conditions.

Aerospace Research CommunicationsVol. 3
Air Force Engineering University (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, National Science and Technology Major Project
Openalex Percentile: Top 13%
Probabilistic and Robust Engineering Design
1.40
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