Rotor Wake Mixing Effects Downstream of a Compressor Rotor

Ravindranath, A.; Lakshminarayana, B.

doi:10.1115/1.3227251

Cited by 8 publications

(5 citation statements)

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“…This suggests that the formation of the vortex street is the primary loss-producing mechanism in the wake. A similar result was found by Ravindranath and Lakshminarayana (1982) behind a compressor rotor, although they made no comment on the presence or otherwise of a vortex street.…”

Section: Introductionsupporting

confidence: 68%

Loss Production in the Wake of a Simulated Subsonic Turbine Blade

Roberts

Denton

1996

Volume 1: Turbomachinery

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The time-averaged flow in the wake of a model of a turbine blade was surveyed using a three-hole pressure probe; boundary layer traverses were also carried out using a flattened pilot probe. Loss coefficients were derived from the mass-weighted deficit in stagnation pressure. Results showing the progression of loss with streamwise distance along the surface and the wake of the model are presented. It was found that the loss generated in the wake comprised one-third of the profile loss when a well-developed vortex street was present in the wake. This proportion was reduced by increasing the thickness of the suction surface boundary layer, and by simulating the deviation that occurs in a real turbine blade. In both cases the strength of the vortex street was also shown to have been reduced.

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Section: Introductionsupporting

confidence: 68%

Loss Production in the Wake of a Simulated Subsonic Turbine Blade

Roberts

Denton

1996

Volume 1: Turbomachinery

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“…The defect in the relative total velocity decreases monotonically with axial distance. However the data of Ravindranath and Lakshminarayana (1982) shows a slight increase in the defect in the trailing edge region and then a monotonical decrease and this is not predicted by the numerical scheme.…”

Section: Wake Decay Characteristicsmentioning

confidence: 78%

Numerical Prediction of Wakes in Cascades and Compressor Rotors Including the Effects of Mixing: Part II — Rotor Passage Flow and Wakes Including the Effects of Spanwise Mixing

Suryavamshi

Lakshminarayana

1991

Volume 1: Turbomachinery

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The results of a numerical investigation to predict the flow field including wakes and mixing in axial flow compressor rotors has been presented in this paper. The wake behaviour in a moderately loaded compressor rotor has been studied numerically using a three-dimensional incompressible Navier-Stokes solver with a high Reynolds number form of the k–ε turbulence model. The equations are solved using a time dependent implicit technique. The agreement between the measured data and the predictions are good; including the blade boundary layer profiles, wake mean velocity profiles and decay. The ability of the pseudo-compressibility scheme to predict the entire flow field including the near and far wake profiles and its decay characteristics, effect of loading and the viscous losses of a three-dimensional rotor flow field has been demonstrated. An analysis of the passage averaged velocities and the pressure coefficients shows that the mixing in the downstream regions away from the hub and annulus walls is dominated by wake diffusion. In regions away from the walls, the radial mixing is predominantly caused by the transport of mass, momentum and energy by the radial component of velocity in the wake.

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“…Some symbols in all figures below need to explain. The symbol “EXP” represents the data come from experiments 28 and “CAL_tra”, “CAL_mix”, “CAL_nn” represent the predicting results based on the traditional loss and deviation models, 5,13–21 the spanwise mixing models, 22,23 and the surrogate model developed in this paper, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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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Rotor Wake Mixing Effects Downstream of a Compressor Rotor

Cited by 8 publications

References 0 publications

Loss Production in the Wake of a Simulated Subsonic Turbine Blade

Loss Production in the Wake of a Simulated Subsonic Turbine Blade

Numerical Prediction of Wakes in Cascades and Compressor Rotors Including the Effects of Mixing: Part II — Rotor Passage Flow and Wakes Including the Effects of Spanwise Mixing

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

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