Interaction of Compressor Rotor Blade Wake With Wall Boundary Layer/Vortex in the End-Wall Region

Lakshminarayana, B.; Ravindranath, A.

doi:10.1115/1.3227302

Cited by 9 publications

(7 citation statements)

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“…[4] In the 1980s, Lakshminarayana and co-workers carried out experiments on an axial compressor rotor. [5][6][7][8][9][10][11] These studies allow to depict the main features of the tip clearance flow. The leakage flow occurs on the suction side between 25% and 50% of the chord length and involves a low-momentum region.…”

Section: Introductionmentioning

confidence: 99%

RANS and LES computations of the tip-leakage vortex for different gap widths

Decaix

Balarac

Dreyer

et al. 2015

Journal of Turbulence

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In hydraulic turbines, the tip-leakage vortex is responsible for flow instabilities and for promoting erosion due to cavitation. To better understand the tip vortex flow, Reynoldsaveraged Navier-Stokes (RANS) and large eddy simulation (LES) computations are carried out to simulate the flow around a NACA0009 blade including the gap between the tip and the wall. The main focus of the study is to understand the influence of the gap width on the development of the tip vortex, as for instance its trajectory. The RANS computations are performed using the open source solver OpenFOAM 2.1.0, two incidences and five gaps are considered. The LESs are achieved using the YALES2 solver for one incidence and two gaps.The validation of the results is performed by comparisons with experimental data available downstream the trailing edge. The position of the vortex core, the mean velocity and the mean axial vorticity fields are compared at three different downstream locations. The results show that the mean behaviour of the tip vortex is well captured by the RANS and LES computations compared to the experiment. The LES results are also analysed to bring out the influence of the gap width on the development of the tip-leakage vortex. Finally, a law that matches the vortex trajectory from the leading edge to the mid-chord is proposed. Such a law can be helpful to determine, in case of cavitation, if the tip vortex will interact with the walls and cause erosion.

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Section: Introductionmentioning

confidence: 99%

RANS and LES computations of the tip-leakage vortex for different gap widths

Decaix

Balarac

Dreyer

et al. 2015

Journal of Turbulence

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“…Schlichting, Das, and Kiock 16,17 found that for the rear stages of compressors, the turbulence intensity increased by about 6%, indicating that mixing cannot be ignored. By measuring the radial turbulence intensity, Lakshminarayana 18–21 found that the radial turbulence intensity decayed much more slowly than the tangential and streamwise directions. As a result, he believed that high turbulence levels significantly contribute to spanwise mixing.…”

Section: Introductionmentioning

confidence: 99%

The genetic algorithm-radial basis function neural network to quickly predict aerodynamic performance of compressors

Tang

Liu

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, compressor aerodynamic performance has been predicted based on throughflow theory, combined with a surrogate model, which is a combination of the Genetic Algorithm (GA) and generalized Radial Basis function (RBF) neural network. And the predicting results have been compared with those from the traditional models and spanwise mixing model, which still widely be used to predict the aerodynamic performance. We first predicted the deviation angle and total-pressure loss coefficient (TPLC) by the surrogate model, and then using these two intermediate variables connected the model with throughflow theory. The pressure ratio and efficiency, representing the compressors’ total performance parameters, are predicted and compared with experimental data. In order to increase the accuracy of prediction, a data augmentation method based on the piecewise cubic Hermite interpolation (PCHIP) algorithm is introduced to enlarge the training database. At the same time, considering the vast differences of deviation angle and loss in different working conditions as well as aerodynamic and geometric differences of rotor and stator, the database and the network should be split into six components based on the choke, the normal and the stall conditions as well as rotor and stator. Then, the performance curves of pressure ratio and efficiency can be determined by an iteration process. The predicting results are compared with experimental data, which shows that the surrogate model matches experiments much better than those from the traditional models and spanwise mixing model.

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“…These issues have motivated a number of experimental and computational inves-tigations of the compressor tip-clearance flow, and some basic understanding has been achieved (see e.g. [4][5][6][7][8][9][10][11]).…”

Section: Introductionmentioning

confidence: 99%

Vortex Dynamics and Mechanisms for Viscous Losses in the Tip-Clearance Flow

You

Wang

Moin

et al. 2005

Volume 1: Symposia, Parts a and B

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The tip-clearance flow in axial turbomachines is studied using large-eddy simulation with particular emphasis on understanding the underlying mechanisms for viscous losses in the end-wall region and the unsteady characteristics of the tip-leakage vortical structures. Systematic and detailed analysis of the mean flow field and turbulence statistics has been made in a linear cascade with a moving end-wall. The tip-leakage jet and tip-leakage vortex are found to produce significant mean velocity gradients, leading to the production of vorticity and turbulent kinetic energy. These are the major causes for viscous losses in the cascade end-wall region. An analysis of the energy spectra and space-time correlations of the velocity fluctuations suggests that the tip-leakage vortex is subject to a pitchwise low frequency wandering motion.

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Interaction of Compressor Rotor Blade Wake With Wall Boundary Layer/Vortex in the End-Wall Region

Cited by 9 publications

References 0 publications

RANS and LES computations of the tip-leakage vortex for different gap widths

RANS and LES computations of the tip-leakage vortex for different gap widths

The genetic algorithm-radial basis function neural network to quickly predict aerodynamic performance of compressors

Vortex Dynamics and Mechanisms for Viscous Losses in the Tip-Clearance Flow

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