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

Methling, Frank-Oliver; Stoff, H.; Grauer, Frank

doi:10.1155/s1023621x04000399

Cited by 7 publications

(1 citation statement)

References 6 publications

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“…They found that by using the proposed method, the compressor is allowed to stably work with high efficiency and pressure ratio beyond the boundary of surge. In another work, Methling et al (2004) used wall static pressure signals to train a network to indicate when instability was being neared. They found that the provided monitoring system based on the ANN has the ability to cover the whole working range of the compressor to provide adequate training datasets.…”

Section: Surge and Stallmentioning

confidence: 99%

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Pakatchian

Ziamolki²,

Nazari³

2023

Front. Energy Res.

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A compressor is one of the key components of a gas turbine engine and its performance and characteristics significantly affect the overall performance of the engine. Axial flow compressors are one of the most conventional types of compressors and are widely used in turbine engines for large-scale power generation. Intelligent techniques are useful for numerical simulation, characterization of axial compressors, and predicting their performance. The present work reviews studies applying different intelligent methods for performance forecasting and modeling different aerodynamic aspects of axial compressors. Corresponding to the outcomes of the considered research works, it can be expressed that by using these methods, axial compressors can be characterized properly with acceptable exactness. In addition, these techniques are useful for performance prediction of the compressors. The accuracy and performance of these methods is impacted by several elements, specifically the employed method and applied input variables. Finally, some suggestions are made for future studies in the field.

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Section: Surge and Stallmentioning

confidence: 99%

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Pakatchian

Ziamolki²,

Nazari³

2023

Front. Energy Res.

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Identification and classification of operating flow regimes and prediction of stall in a contra-rotating axial fan using machine learning

2022

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Prediction of stall before it occurs, or detection of stall is crucial for smooth and lasting operation of fans and compressors. In order to predict the stall, it is necessary to distinguish the operational and stall regions based on certain parameters. Also, it is important to observe the variation of those parameters as the fan transitions towards stall. Experiments were performed on a contra-rotating fan setup under clean inflow conditions, and unsteady pressure data were recorded using seven high-response sensors circumferentially arranged on the casing, near the first rotor leading edge. Windowed Fourier analysis was performed on the pressure data, to identify different regions, as the fan transits from the operational to stall region. Four statistical parameters were identified to characterise the pressure data and reduce the number of data points. K-means clustering was used on these four parameters to algorithmically mark different regions of operation. Results obtained from both the analyses are in agreement with each other, and three distinct regions have been identified. Between the no-activity and stall regions, there is a transition region that spans for a short duration of time characterised by intermittent variation of abstract parameters and excitations of Fourier frequencies. The results were validated with five datasets obtained from similar experiments at different times. All five experiments showed similar trends. Neural Network models were trained on the clustered data to predict the operating region of the machine. These models can be used to develop control systems that can prevent the stalling of the machine.

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Algorithm of axial compressor stall warning based on BP neural network and fuzzy logic

Qiu

Jia-li

2022

MATEC Web Conf.

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Stall warning of axial compressor is very challenging and the existing warning margin is not enough. A algorithm based on BP neural network fusion fuzzy logic is proposed. Firstly, BP neural network is used for training recognition, next the identification results are fused with fuzzy logic reasoning to form the result judgment of time sequence, finally the stall early warning of axial compressor is realized. The simulation results of the experimental data show that the stall data at all speeds are at least 0.1s in advance of the early warning. Compared with other methods, this method has a better surge early warning margin performance and engineering practicability.

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

Cited by 7 publications

References 6 publications

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

Identification and classification of operating flow regimes and prediction of stall in a contra-rotating axial fan using machine learning

Algorithm of axial compressor stall warning based on BP neural network and fuzzy logic

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