Comparison and Sensibility Analysis of Warning Parameters for Rotating Stall Detection in an Axial Compressor

Margalida, Gabriel; Joseph, Pierric; Roussette, Olivier

doi:10.3390/ijtpp5030016

Cited by 13 publications

(12 citation statements)

References 16 publications

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“…In the undistorted case, the spike usually starts in the region between 90° and 135 Such a feature has a very high reproducibility, most likely because the tip clearan in that region decreases and reaches the minimum value due to the imperfect circular of the casing as already observed [13]. The perturbation then develops rapidly (in abou rotor revolutions) in amplitude, while its speed decreases from about 90% to 40% of rotor speed.…”

Section: Stall Inceptionmentioning

confidence: 64%

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Experimental and Numerical Analysis of a Compressor Stage under Flow Distortion

Baretter

Godard

Joseph

et al. 2021

IJTPP

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On many occasions, fan or compressor stages have to face azimuthal flow distortion at inlet, which affects their performance and stability. These flow distortions can be caused by external events or by some particular geometrical features. The aim of this work is to propose a joined numerical and experimental analysis of the flow behavior in a single axial compressor stage under flow distortion. The distortions are generated by different grids that are placed upstream to the rotor. Experimentally, the flow analysis is based on the measurements obtained by a series of unsteady pressure sensors flush-mounted at the casing of the machine rotor. URANS computations are conducted using the elsA software. The flow distortion is simulated by a drop of stagnation pressure ratio at the inlet boundary condition. The study is focusing first on the ability of a pressure drop, imposed as an inlet boundary condition in CFD, to reproduce accurately the effect of a flow distortion. The analysis is conducted using singular value decomposition (SVD) and dynamic mode decomposition (DMD). A special attention is then paid, on the experimental level, to the arising of rotating stall, from the onset of the instability up to completely developed stall cells.

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Section: Stall Inceptionmentioning

confidence: 64%

“…The throttling valve was closed progressively, starting from the last sta point to induce rotating stall. It has been observed, in previous works [12,13] that the p turbation arising in CME2 is always of spike type. A typical transition to stall is rep sented in Figure 8.…”

Section: Stall Inceptionmentioning

confidence: 95%

Experimental and Numerical Analysis of a Compressor Stage under Flow Distortion

Baretter

Godard

Joseph

et al. 2021

IJTPP

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“…For all instantaneous measurements (i.e. pressure fields and perturbation monitoring), the throttling process is continuous and similar to the procedure described in a previous paper (Margalida et al 2020). For performance measurements however, the process is realized point by point, interspersed with stabilization phase, by carefully decreasing the mass flow from one operating point to the next one.…”

Section: Fig 5 Measurement Setupmentioning

confidence: 99%

Active flow control in an axial compressor for stability improvement: on the effect of flow control on stall inception

2021

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In this paper, the effect of an active flow control system on the onset mechanisms of rotating stall in an axial compressor is experimentally investigated. A particular emphasis is thus given on the flow patterns present in the tip region right before stall and how they evolve when active flow control is applied. The mechanisms involved during stall formation without control are carefully assessed and compared to the literature. To this purpose, the development of the first instability into rotating stall is discussed by monitoring its speed inside the compressor. This analysis also relies on instantaneous pressure measurements at the casing of the compressor.

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“…According to this phenomenon, the correlation analysis method has been widely applied in stall detection. Dhingra et al 12 successfully detected the stall precursor on a low-speed axial compressor through the auto-correlation analysis of the pressure signals from sensors mounted over the rotor; Margalida et al 13 used cross-correlation analysis and the RMS method to detect stall precursors on a single axial compressor and studied the effects of sensor position and the throttling process on the sensitivity of the two methods. In general, the time-domain analysis method has the advantages of simple algorithm, low complexity, and fast calculation so that it is more convenient to be applied in engineering practice.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction for the surge of turboshaft engine using multi-branch feature fusion neural network

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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The existing aeroengine instability precursor detection methods can be summarized as applying advanced signal processing technologies to various signals from the compressor test rig rather than the whole engine. Besides, these methods seriously depend on the artificial designed feature and threshold and also ignore the limit on the sensors onboard. Thus, with the help of the powerful feature extraction ability of the deep neural network, a real-time surge prediction method based on the multi-branch feature fusion neural network (MBFFNN) is proposed. First, the dataset can be obtained by using overlapping slices to divide surge test data into a sample sequence and using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to label each sample precisely. Second, for each sample, the time-domain statistical parameters are calculated and the recurrence plot is obtained by using phase space reconstruction. Finally, the MBFFNN with mixed data type input is designed, and its performance is evaluated by the generated dataset. The experimental results show that compared with multilayer perceptron (MLP), long short-term memory (LSTM), and deep residual network (DRN), MBFFNN has the best performance on two datasets for different surge tests, which demonstrates that the proposed method for surge prediction can accurately judge the state of the aeroengine, identify the instability precursor before the surge, and give an early warning in advance.

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Comparison and Sensibility Analysis of Warning Parameters for Rotating Stall Detection in an Axial Compressor

Cited by 13 publications

References 16 publications

Experimental and Numerical Analysis of a Compressor Stage under Flow Distortion

Experimental and Numerical Analysis of a Compressor Stage under Flow Distortion

Active flow control in an axial compressor for stability improvement: on the effect of flow control on stall inception

Real-time prediction for the surge of turboshaft engine using multi-branch feature fusion neural network

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