Stall events in axial compressor systems have been a limiting factor for efficiency of such systems and a source of safety concerns. The detection of the onset of stall, and in many cases the precursor of the onset of stall, have been of interest in the axial compressor community for many decades. As such, development of algorithms along with active control could lower cost, reduce emissions, improve safety, and increase market competitiveness. To gain an understanding of these stall phenomena, past and current research has focused on modeling axial compressors as dynamic systems, with a focus on obtaining descriptive formulations of the physical aspects of stall. Some of these approaches allow for active control measures that extend the stall margin of the compressor system to increase safety and efficacy. This paper reviews the major contributions in these listed pursuits and presents the latest methods and algorithms for stall precursor detection in low-speed axial compressors. In particular, a review is presented in the types and characteristics of stalls, the major mathematical models used to describe these systems, influences of physical attributes such as tip clearance, guide vanes, and groove casing—operating as passive control elements—but also active control utilities such as air injection are discussed along with a detailed review of existing stall precursor detection algorithms. In addition, a forward-looking projection is presented that includes the use of machine learning algorithms to further the understanding and the capability of stall precursor detection.

Operational safety and fuel efficiency are critical, yet often conflicting, objectives in modern civil get engine designs. Optimal efficiency operating conditions are typically close to unsafe regions, such as compressor stalls, which can cause severe engine damage. Consequently, engines are generally operated below peak efficiency to maintain a sufficient stall margin. Reducing this margin through active control requires stall precursor detection and mitigation mechanisms. While several algorithms have shown promising results in predicting modal stalls, predicting spike stalls remains a challenge due to their rapid onset, leaving little time for corrective actions. This study addresses this gap by proposing a method to identify spike stall precursors based on the changing dynamics within a compressor blade passage. An autoregressive time series model is utilized to capture these dynamics and its changes are related to the flow condition within the blade passage. The autoregressive model is adaptively extracted from measured pressure data from a one-stage axial compressor test stand. The corresponding eigenvalues of the model are monitored by utilizing an outlier detection mechanism that uses pressure reading statistics. Outliers are proposed to be associated with spike stall precursors. The model order, which defines the number of relevant eigenvalues, is determined using three information criteria: the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Conditional Model Estimator (CME). For prediction, an outlier detection algorithm based on the Generalized Extreme Studentized Deviate (GESD) Test is introduced. The proposed method is experimentally validated on a single-stage low-speed axial compressor. Results demonstrate consistent stall precursor detection, with future application for timely control interventions to prevent spike stall inception.

In this work, we investigate the optimal control problem of regulating the airspeed and altitude of an airplane solely by using the elevator angle. The proposed Linear Quadratic Controller (LQR) is optimized using a Genetic Algorithm with the objective of finding the optimal weighting matrices of the optimal controller cost function. In comparison to the controllers published in other literatures, the proposed control scheme yields improved response times and magnitude.