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A high-order model for spike-type instability in axial compression systems
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摘要
Inlet flow non-uniformity(inlet distortion) can influence the transient behavior of stall disturbances and trigger the compression system instability. Physical modeling is a direct and effective method to study stall phenomena of axial compression systems with inlet distortion. In this paper, by using the general Galerkin projection and the spatial discretization methods, highorder ordinary differential equations(ODEs) are derived for analyzing the complex spike-type stall inception process in axial compression systems with inlet distortion. Considering the high-order stall modes, the wave propagating around the compressor annulus has a rich harmonic structure for compression systems with inlet distortion, and then the high-order model can more accurately depict the dynamics of rotating stall, especially the transient behavior of spike-type stall inception. Finally, numerical simulations are performed on an eighteenth-order model to illustrate the effectiveness of the high-order model.
Inlet flow non-uniformity(inlet distortion) can influence the transient behavior of stall disturbances and trigger the compression system instability. Physical modeling is a direct and effective method to study stall phenomena of axial compression systems with inlet distortion. In this paper, by using the general Galerkin projection and the spatial discretization methods, highorder ordinary differential equations(ODEs) are derived for analyzing the complex spike-type stall inception process in axial compression systems with inlet distortion. Considering the high-order stall modes, the wave propagating around the compressor annulus has a rich harmonic structure for compression systems with inlet distortion, and then the high-order model can more accurately depict the dynamics of rotating stall, especially the transient behavior of spike-type stall inception. Finally, numerical simulations are performed on an eighteenth-order model to illustrate the effectiveness of the high-order model.
引文
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