A Review on Theoretical and Numerical Research of Axial Compressor Surge

Zhao, Hongliang; Du, Jie; Zhang, Wenqiang; Zhang, Hongwu; Nie, Chaoqun

doi:10.1007/s11630-022-1682-x

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…This phenomenon, surge, is the reversal of flow and complete break down of continuous flow in the compressor. Surge induces mechanical problems due to the high fluctuations of flow that causes changes in the direction of thrust force in the rotor section (Boyce, 2012b;Zhao et al, 2023).…”

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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“…The smaller the surge margin, the higher the performance. This reasoning has motivated wide investigations of compressor instabilities such as classical surge (CS), deep surge (DS), and rotating stall (RS) [2][3][4][5][6]. The detection of the instabilities and their active control are essential tasks in order to ensure safety and engine performance [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

AI-Based Detection of Surge and Rotating Stall in Axial Compressors via Dynamic Model Parameter Estimation

Zanotti,

Ceschini,

Ferlauto

2024

Fluids

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Compressors are an essential component of aircraft engines. Their design and operation must be extremely reliable as engine safety and performance depend greatly on these elements. Axial compressors exhibit instabilities, such as surge or rotating stall, in a region close to the peak of their performance curves. These fluid dynamic instabilities can cause drops in efficiency, stress on the blades, fatigue, and even failures. Compressors are handled therefore by operating with a safety margin far from the surge line. Moreover, models able to predict onset instabilities and to reproduce them are of great interest. A dynamic system able to describe successfully both surge and rotating stall is the model presented by Moore and Greitzer That model has also been used for developing control laws of the compressor dynamics. The present work aims at developing an artificial neural network (ANN) approach able to predict either the permanence of the system in stable working condition or the onset instabilities from a time sequence of the compressor dynamics. Different solutions were tried to find the most suitable model for identifying the system, as well as the effects of the duration of the time sequence on the accuracy of the predicted compressor working conditions. The network was further tried for sequences with different initial values in order to perform a system analysis that included multiple variations from the initial database. The results show how it is possible to identify with high accuracy both rotating stall and surge with the ANN approach. Moreover, the presence of an underlying fluid dynamic model shares some similarities with physically informed AI procedures.

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Enhancing Axial Compressor Stall Warning with Time-Frequency Generative Adversarial Networks: A Novel Approach in Advanced Signal Processing

Qiu,

Wu,

Guo

et al. 2024

2024 36th Chinese Control and Decision Conference (CCDC)

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A Review on Theoretical and Numerical Research of Axial Compressor Surge

Cited by 7 publications

References 42 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

AI-Based Detection of Surge and Rotating Stall in Axial Compressors via Dynamic Model Parameter Estimation

Enhancing Axial Compressor Stall Warning with Time-Frequency Generative Adversarial Networks: A Novel Approach in Advanced Signal Processing

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