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.

From advanced research platforms to Unmanned Aerial Vehicles (UAVs), Micro Turbojet Engines (MTEs) have grown to be essential parts in many different kinds of applications. Extensive testing, encompassing analysis, alternative fuel suitability, performance characterization, and control system validation, supports their development. This survey examines the current state of MTE experimentation, along with information on experimental methods, sensor technologies, and new directions in corresponding research. Measurement techniques, alternative fuel effects, characterization of gaseous and particle emissions, and the utilization of experimental data to validate control systems and sophisticated engine models are among the recent advances in test bed design discussed here. Presenting an overall view, the aim is to highlight current challenges and inspire more MTE technology advancement.

Combustion involves the study of multiphysics phenomena that includes fluid and chemical kinetics, chemical reactions and complex nonlinear processes across various time and space scales. Accurate simulation of combustion is essential for designing energy conversion systems. Nonetheless, due to its multiscale, multiphysics nature, simulating these systems at full resolution is typically difficult. The massive and complex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manage the substantial amount of combustion data that is either obtained through experiments or simulations, and thereby can find the hidden patterns underlying these data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, offering promising opportunities to explore its integration with combustion research. This work provides an in-depth overview of machine learning applications in turbulent combustion modeling and presents the application of machine learning models: Decision Trees (DT) and Random Forests (RF), for the spatio-temporal prediction of plasma-assisted ignition kernels, based on the initial degree of ionization, with model validations against DNS data. The results demonstrate that properly trained machine learning models can accurately predict the spatio-temporal ignition kernel profile based on the initial energy deposition and distribution.

Identification of a one-stage axial compressor system is addressed. In particular, we investigate the underlying dynamics of tip air injection and throttle activation to the overall compressor dynamics and the dynamics around the tip of the compressor blades. A proposed subspace system identification algorithm is used to extract three mathematical models: relating the tip air injection to the overall dynamics of the compressor and to the flow dynamics at the tip of the compressor blade and relating the movement of the throttle to the overall compressor dynamics. As the system identification relays on experimental data, concerns about the noise level and unmodeled system dynamics are addressed by experimenting with two model structures. The identification algorithm entails a heuristic optimization that allows for inspection of the results with respect to unmodeled system dynamics. The results of the proposed system identification algorithm show that the assumed model structure for the system identification algorithm takes on an important role in defining the coupling characteristics. A new measure for the flow state in the blade passage is proposed and used in characterizing the dynamics at the tip of the compressor blade, which allows for the inspection of the limits for the utilized actuation.

This study addresses the classic control problem of stabilizing an inverted pendulum on a moving cart, a challenge in control theory and robotics due to its inherent instability and highly nonlinear dynamics. We explore the application of Q-learning, a model-free reinforcement learning algorithm, and its efficacy in deriving an optimal control policy for the system without precise system models. Our approach utilizes Q-learning's capacity for stabilizing a pendulum in an upright position on the top of the horizontally moving cart within a certain boundary. Our strategy adapts to a dynamic environment while showcasing its robustness in developing control policies for complex systems. This research bridges classical control theory with reinforcement learning techniques, contributing to the domain by demonstrating the versatility and potential of machine learning in control tasks.