A review of anti-surge control systems of compressors and advanced fault-tolerant control techniques for integration perspective

Alsuwian, Turki; Amin, Arslan Ahmed; Iqbal, Muhammad Sajid; Maqsood, Muhammad Taimoor

doi:10.1016/j.heliyon.2023.e19557

Cited by 8 publications

(1 citation statement)

References 75 publications

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“…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]. As RS and DS instabilities are nonlinear phenomena, one of the most effective approaches to their control is the model-based predictive control, which in turn requires an underlying reduced order model that predicts the system evolution within a given time interval.…”

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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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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Control strategy for the dynamic shutdown of the charging process of an advanced adiabatic compressed air energy storage

Wang,

Qin,

Jia

et al. 2024

Journal of Energy Storage

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Aviation fuel pump health state assessment based on evidential reasoning and random forests

Zhang,

Chen,

et al. 2024

Electronics Letters

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As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

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A review of anti-surge control systems of compressors and advanced fault-tolerant control techniques for integration perspective

Cited by 8 publications

References 75 publications

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

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

Control strategy for the dynamic shutdown of the charging process of an advanced adiabatic compressed air energy storage

Aviation fuel pump health state assessment based on evidential reasoning and random forests

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