Two-dimensional RANS simulations of the flow through a compressor cascade with jet flaps

Fischer, Susanne; Saathoff, H.; Radespiel, Rolf

doi:10.1016/j.ast.2008.02.006

Cited by 15 publications

(2 citation statements)

References 13 publications

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“…Several studies have shown that constructing the Coanda surface of the compressor blade and using suitable jet flow rates can effectively inhibit boundary layer separation while reducing the compressor's solidity. It has also been demonstrated that the static pressure rise and total pressure loss are significantly influenced by the jet flow rate (Hill et al 2007;Fischer 2008;Guendogdu et al 2008;Schwerdt et al 2016). In a study by Li (2019), the effectiveness of the Coanda jet method on aerodynamic performance and flow separation in a highly-loaded compressor was investigated.…”

Section: Graphic Abstract 1 Introductionmentioning

confidence: 99%

Simulation and Experimental Study on Active Flow Control Strategy for a Compressor Cascade with Coanda Jet Flap

Yang

Zhang

et al. 2023

Preprint

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The flow separation caused by the increase in load on aero-engine compressors often results in flow separation, leading to decreased flow stability and increased flow loss. This paper investigates active flow control strategies based on a highly-loaded compressor cascade with Coanda jet flaps to effectively suppress flow separation and decrease aerodynamic losses of aero-engine compressors. To analyse the influence of the jet on aerodynamic performance, three-dimensional unsteady computational fluid dynamics simulations are conducted. The dynamic characteristic model of the controlled object is established using the time-delay neural network (TDNN) method based on time-domain data obtained from wind tunnel experiments. Three control strategies, including adaptive radial basis function (ADA-RBF) neural network control, PID control, and RBF-PID control strategies, are designed to achieve a fast-response and adaptive control effect. Simulation results demonstrate that RBF-PID control achieves faster convergence speeds than conventional PID control. Given that the adaptive RBF neural network control method, provides both faster convergence speed and more accurate initial jet flow rates, corresponding experimental studies are carried out to validate the proposed approach. The findings demonstrate that ADA-RBF neural network control can effectively inhibit flow separation, even with continuously changing inflow velocity and pressure in the wind tunnel. The total pressure loss coefficient is decreased by 6.13% and 7.87% at Mach numbers of 0.4 and 0.6, respectively.

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Section: Graphic Abstract 1 Introductionmentioning

confidence: 99%

Simulation and Experimental Study on Active Flow Control Strategy for a Compressor Cascade with Coanda Jet Flap

Yang

Zhang

et al. 2023

Preprint

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show abstract

“…Although the aerodynamic design of blade is essential to the research and development of compressors, it is normally considered to be a daunting task due to the complexity of three-dimensional (3D) flows inside the rotating blade passages and a large number of design parameters [1][2][3]. Therefore, the 2D compressor cascade is an important experimental and theoretical model, which has indeed played a critical role in understanding the turbomachinery flow physics and thus improving the turbomachinery flow design [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multi-point and multi-objective optimization design method for industrial axial compressor cascades

Zhang

2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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Modern aerodynamic optimization design methods for the industrial axial compressor cascade mainly aim at improving both design point and off-design point performance. In this study, a multi-point and multi-objective optimization design method is established for the cascade, particularly aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. The design objectives are to maximize the static pressure ratio and minimize the total pressure loss coefficient at the design point, and to maximize the operating range for the positive and negative incidences. To alleviate the computational load, a design of experiment (DOE)-based GA-BP-ANN model is constructed to rapidly approximate the cascade aerodynamic performance in the optimization process. The artificial neural network (ANN) is trained by the genetic algorithm (GA) technique and back propagation (BP) algorithm, where the training cascades are sampled by the DOE method and analysed by the computational fluid dynamics method. The multi-objective genetic algorithm is used to search for a series of Pareto-optimum solutions, from which an optimal cascade is found out whose objectives are all better than (ABT) those of the original design. The ABT cascade is characterized by the lower camber and higher turning angle, leading to better aerodynamic performance in a widened operating range. Compared with the original design, the ABT cascade decreases the total pressure loss coefficient by 1.54 per cent, 23.4 per cent, and 7.87 per cent at the incidences of 5 • , −9 • , and 13 • , respectively. The established optimization design method can be extended to the three-dimensional aerodynamic design of axial compressor blade.

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Aerodynamic Performance Improvement of a Highly Loaded Compressor Airfoil with Coanda Jet Flap

Zhang

et al. 2022

J. Therm. Sci.

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Two-dimensional RANS simulations of the flow through a compressor cascade with jet flaps

Cited by 15 publications

References 13 publications

Simulation and Experimental Study on Active Flow Control Strategy for a Compressor Cascade with Coanda Jet Flap

Simulation and Experimental Study on Active Flow Control Strategy for a Compressor Cascade with Coanda Jet Flap

Multi-point and multi-objective optimization design method for industrial axial compressor cascades

Aerodynamic Performance Improvement of a Highly Loaded Compressor Airfoil with Coanda Jet Flap

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