Effects of incidence angle on a low-pressure turbine blade boundary layer evolution through large eddy simulation

Wang, Yunfei; Sui, Xiuming; Zhang, Kai; Xiang, Xin; Zhao, Qingjun

doi:10.1177/0957650917752806

Cited by 7 publications

(3 citation statements)

References 27 publications

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Section: Computational Overviewmentioning

confidence: 99%

“…The in-house multi-block structured compressible LES solver code is developed, optimized and tested on several canonical cases by Ye 18 and Wang. 19 The dimensionless Navier-Stokes equations in three dimensional Cartesian coordinates can be written as,where the overbar represents spatial filtering operation and the tilde refers Favre-filtering operation, ϕ ˜= ρϕtrue¯/trueρ¯.…”

Section: Computational Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Compressible large eddy simulation of the unsteady evolution process in a LPT Cascade with incoming wakes

Wang

Chen

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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An in-house large eddy simulation (LES) code based on three-dimensional compressible N-S equations is used to research the impact of incoming wakes on unsteady evolution characteristic in a low-pressure turbine (LPT) cascade. The Mach number is 0.4 and Reynolds number is 0.6 × 105 (based on the axial chord and outlet velocity). The reduced frequency of incoming wakes is Fred = 0 (without wakes), 0.37 and 0.74. A detailed analysis of Reynolds stresses and turbulent kinetic energy inside the boundary layer has been carried out. Particular consideration is devoted to the transport process of incoming wakes and the intermittent property of the unsteady boundary layer. With the increase of reduced frequency, the inhibiting effect of wakes on boundary layer separation gradually enhances. The separation at the rear part of the suction side is weakened and the separation point moves downstream. However, incoming wakes lead to an increase in dissipation and aerodynamic losses in the main flow area. Excessive reduced frequency ( Fred = 0.74) causes the main flow area to become one of the main source areas of loss. An optimal reduced frequency exists to minimize the aerodynamic loss of the linear cascade.

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Section: Computational Overviewmentioning

confidence: 99%

Section: Computational Overviewmentioning

confidence: 99%

Compressible large eddy simulation of the unsteady evolution process in a LPT Cascade with incoming wakes

Wang

Chen

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

Self Cite

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“…Changes in incidence significantly affect the thickness and state of the boundary layer, which aggravates the generation of profile loss. 5 This part of the additional loss is often called the incidence loss. 6 The traditional incidence loss prediction model consists of a series of empirical relations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive prediction of turbine profile loss and multi-objective optimization in a wide incidence range

Wang

Zhao

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The loss prediction model plays a crucial role in turbine design for fast performance prediction and a shorter design cycle. Owing to the design requirements of high-efficiency turbines under a wide range of operating conditions, the loss prediction of the off-design incidence is increasingly important. However, limited by the modeling database and traditional modeling methods, the accuracy and adaptability of the existing off-design incidence loss predictions are insufficient. This paper proposes an adaptive prediction method based on machine learning and develops a multi-objective optimization process based on adaptive prediction. Machine learning (neural network) is applied for more flexible and accurate loss predictions over a wide incidence range. Compared with two classic loss models (Ainley and Mathieson model and Benner model), the adaptive prediction model significantly improves the ability to predict turbine profile loss with off-design incidence, particularly under large incidence conditions. The prediction root mean square error can be reduced by up to 73.8% (absolute value: 0.063). Furthermore, the multi-objective optimization method based on adaptive prediction is applied to the aerodynamic optimization of the original cascades with a wide incidence range. The weighted objective of the optimized cascade (Cri = 0.211) is reduced by 8.7% compared with that of the original cascade (Cri = 0.231). Within the range of full incidence angle (−40° to +20°), the variation of profile loss is reduced by 24.0%. This study is a preliminary exploration aimed at establishing an accurate turbine loss prediction system based on machine learning, the feasibility, and superiority of this approach are confirmed.

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Compressible large eddy simulation of the boundary layer evolution in a low-pressure turbine cascade at different Reynolds numbers

Wang

Song

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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Effects of incidence angle on a low-pressure turbine blade boundary layer evolution through large eddy simulation

Cited by 7 publications

References 27 publications

Compressible large eddy simulation of the unsteady evolution process in a LPT Cascade with incoming wakes

Compressible large eddy simulation of the unsteady evolution process in a LPT Cascade with incoming wakes

Adaptive prediction of turbine profile loss and multi-objective optimization in a wide incidence range

Compressible large eddy simulation of the boundary layer evolution in a low-pressure turbine cascade at different Reynolds numbers

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