Experimental Examination of an Axial Compressor as aBasis for an Active Stall Avoidance System

Schulze, Richard; Hennecke, Dietmar K.; Le, Thuyen; Glesner, Manfred

doi:10.1155/s1023621x00000087

Cited by 5 publications

(3 citation statements)

References 3 publications

(4 reference statements)

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“…In a paper by Tryfonidis et al (1995), the emphasis is to study long length scale phenomenon that leads to rotating stall. Other investigations by Day (1993), Paric (1994), Camp & Day (1997), and Schulze (1997) reflect the detection of short length scale (pip) activity in several high speed compressors. In a recent report by on the active stabilization of high speed compressors, several pre-stall data sets showed modal activity, then a controller was used to suppress the modal waves, and subsequent pip activity instigated the controller to go unstable) With this in mind, it would be advantageous to develop analysis and prediction techniques that detect both short and long length-scale disturbances which lead to rotating stall.…”

Section: Introductionmentioning

confidence: 89%

Investigation of Pre-Stall Mode and Pip Inception in High Speed Compressors Through the Use of Correlation Integral

Bright

Qammar

Wang

1998

Volume 1: Turbomachinery

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Five high speed compressor configurations are used to identify pre-stall pressure signal activity under clean and distorted inlet conditions, and under steady injection and controlled injection conditions. Through the use of a nonlinear statistic called correlation integral, variations in the compressor dynamics are identified from the pre-stall pressure activity far before variations (modal or pip) are observed visually in the wall static pressure measurements. The correlation integral not only discerns changing dynamics of these compressors prior to stall, but is now used to measure the strength of the tip flow field for these five high speed compressors. Results show that correlation integral value changes dramatically when the stall inception is modal; and it changes less severely when the stall inception is through pip disturbances. This algorithm can therefore distinguish from the pre-stall pressure traces when a machine is more likely to stall due to pips versus modes. When used in this manner, the correlation integral thus provides a measure of tip flow strength. The algorithm requires no predisposition about the expected behavior of the data in order to detect changing dynamics in the compressor, thus no pre-filtering is necessary. However, by band-pass filtering the data, one can monitor changing dynamics in the tip flow field for various frequency regimes. An outcome of this is to associate changes in correlation integral value directly to frequency specific events occurring in the compressor, i.e. blade length scale events versus long length scale acoustic events. The correlation integral provides a potential advantage over linear spectral techniques because a single sensor is used for detection and analysis of the instabilities.

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Section: Introductionmentioning

confidence: 89%

Investigation of Pre-Stall Mode and Pip Inception in High Speed Compressors Through the Use of Correlation Integral

Bright

Qammar

Wang

1998

Volume 1: Turbomachinery

View full text Add to dashboard Cite

show abstract

“…For this reason, numerous investigations are under way to find reliable means for monitoring an eventual approach to the stability limit while running the machine (Ludwig and Nenni, 1978;Day et al, 1997;Höss et al, 1998;Walbaum and Rieß, 1998). Ways for alerts have been identified for individual test beds but a reliable warning of general validity also for foreign compressor designs is still unattained (Ludwig and Nenni, 1980;Schulze et al, 1998;Regnery, 1998;Grauer, 1998a, b), therefore the quest for proof of reliability initiates additional research. Different concepts have 388 F.-O.…”

Section: Introductionmentioning

confidence: 99%

The Pre-Stall Behavior of a 4-Stage Transonic Compressor and Stall Monitoring Based on Artificial Neural Networks

Methling¹,

Stoff²,

Grauer³

2004

The International Journal of Rotating Machinery

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Current research concerned with the aerodynamic instability of compressors aims at an extension of the operating range of the compressor towards decreased massflow. In practice, a safety margin is maintained between operating point and stability limit to prevent the compressor from going into stall and surge. In this article, we analyze the behavior of a 4-stage transonic axial compressor before entering the unstable range and present an approach to identifying incipient surge and stall using artificial neural networks. This method is based on measurements of the unsteady static wall pressure in front of the first rotor.Analyzing the static pressure signals by using the Fast Fourier Transform shows that peripheral disturbances (modal waves) can only be identified in a small range close to nominal speed (at 95%). At lower speeds (60 to 80% of nominal speed), the investigated compressor flow enters instability by spike-type stall.Monitoring stability over the entire speed range of the compressor relies on artificial neural networks using the unsteady wall pressure signal. In the present case, artificial neural networks show to be the most useful tool to indicate approaching instability. The method works reliably for both types of instabilities, spike-type stall as well as modal waves.

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Section: Introductionmentioning

confidence: 99%

The Pre‐Stall Behavior of a 4‐Stage Transonic Compressor and Stall Monitoring Based on Artificial Neural Networks

Methling¹,

Stoff²,

Grauer³

2004

International Journal of Rotating Machinery

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Current research concerned with the aerodynamic instability of compressors aims at an extension of the operating range of the compressor towards decreased massflow. In practice, a safety margin is maintained between operating point and stability limit to prevent the compressor from going into stall and surge. In this article, we analyze the behavior of a 4-stage transonic axial compressor before entering the unstable range and present an approach to identifying incipient surge and stall using artificial neural networks. This method is based on measurements of the unsteady static wall pressure in front of the first rotor.Analyzing the static pressure signals by using the Fast Fourier Transform shows that peripheral disturbances (modal waves) can only be identified in a small range close to nominal speed (at 95%). At lower speeds (60 to 80% of nominal speed), the investigated compressor flow enters instability by spike-type stall.Monitoring stability over the entire speed range of the compressor relies on artificial neural networks using the unsteady wall pressure signal. In the present case, artificial neural networks show to be the most useful tool to indicate approaching instability. The method works reliably for The stall measurements on the compressor rig were carried out at the University of Hannover, Institut für Strömungsmaschinen. The data analysis at the University of Bochum were supported by the German Ministry of Education and Research (BMBF) together with MTU during the project "AG TURBO II" under grant no. 0327061 J/9. We gratefully acknowledge all these contributions.Address correspondence to Frank-Oliver Methling, Fluid Energy Machines, Ruhr-University Bochum, 1B4-142, Universitatsstr. 150, Bochum, D-44801, Germany. E-mail: methling@methling.com both types of instabilities, spike-type stall as well as modal waves.

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Experimental Examination of an Axial Compressor as aBasis for an Active Stall Avoidance System

Cited by 5 publications

References 3 publications

Investigation of Pre-Stall Mode and Pip Inception in High Speed Compressors Through the Use of Correlation Integral

Investigation of Pre-Stall Mode and Pip Inception in High Speed Compressors Through the Use of Correlation Integral

The Pre-Stall Behavior of a 4-Stage Transonic Compressor and Stall Monitoring Based on Artificial Neural Networks

The Pre‐Stall Behavior of a 4‐Stage Transonic Compressor and Stall Monitoring Based on Artificial Neural Networks

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