In order to reduce NO_x emissions, modern gas turbines are often equipped with lean-burn combustion systems, where the high-velocity fuel-lean conditions that limit NO_x formation in combustors also inhibit the ability of ignition, high altitude relight, and lean combustion stability. To face these issues, internally staged scheme of fuel injection is proposed. Primary and main fuel staging enable fuel distribution control, and multi-injections of main fuel lead to a fast and efficient mixing. A fuel-staged low emission combustor in the framework of lean-burn combustion is developed in the present study, i.e., the central pilot stage for low power conditions is swirl-cup prefilming atomization, the main stage is jet-in-crossflow multi-injection, and a combination of primary and main stage injection is provided for higher power output conditions. In lean-burn combustors, the swirling main air naturally tends to entrain the pilot flame and quench it at low power conditions, which is adverse to the operability specifications, such as ignition, lean blow-out (LBO), and high-altitude relight. In order to investigate the effects of the main swirl angle on combustion performances, the ignition and LBO performances were evaluated in a single dome rectangular combustor. Furthermore, the spray patterns and flow field are characterized by kerosene-planar laser induced fluorescence and particle image velocimetry (PIV) to provide insight into spray and combustion performances. Flow–flow interactions between pilot and main air streams, spray–flow interactions between pilot spray and main air streams, and flame–flow interactions between pilot flame and main air streams are comprehensively analyzed. The entrainment of recirculating main air streams on pilot air streams enhances with the increase of main swirl angle, because of the upward motion and increasing width of main recirculation zone. A small part of droplets are entrained by the recirculating main air streams at periphery of combustor and a majority of droplets concentrate near the centerline of combustor, making that entrainment of recirculating main air streams on pilot spray and quenching effects of recirculating main air streams on pilot flame are slight, and the extinguishing effects can be ignored. The contributions of main swirl strength to improvement of ignition and LBO performances are due to enhancement of air/fuel mixing by strengthening turbulence level in pilot zone.

In this paper, fixed-time consensus tracking for multiagent systems (MASs) with dynamics in the form of strict feedback affine nonlinearity is addressed. A fixed-time antidisturbance consensus tracking protocol is proposed, which consists of a distributed fixed-time observer, a fixed-time disturbance observer, a nonsmooth antidisturbance backstepping controller, and the fixed-time stability analysis is conducted by using the Lyapunov theory correspondingly. This paper includes three main improvements. First, a distributed fixed-time observer is developed for each follower to obtain an estimate of the leader's output by utilizing the topology of the communication network. Second, a fixed-time disturbance observer is given to estimate the lumped disturbances for feedforward compensation. Finally, a nonsmooth antidisturbance backstepping tracking controller with feedforward compensation for lumped disturbances is designed. In order to mitigate the “explosion of complexity” in the traditional backstepping approach, we have implemented a modified nonsmooth command filter to enhance the performance of the closed-loop system. The simulation results show that the proposed method is effective.

Modern electronic power systems rely heavily on analog circuits. The accurate detection of analog circuit faults, especially soft faults, is of great significance for the maintenance and inspection of electronic systems. This paper proposes the application of the Boruta feature selection method to the field of the soft fault diagnosis of analog circuits to screen out low-dimensional and efficient feature components from the high-dimensional time-domain statistical features and frequency-domain features of circuit responses. Then, the feature components are used as the input to train the LightGBM classification model, and the Bayesian optimization method is introduced to optimize the model’s hyperparameters. Finally, the trained fault diagnosis model is verified in two typical experimental circuits, and satisfactory accuracy is obtained.

Accurate prediction of thermoacoustic instability is a prerequisite for thermoacoustic control to avoid the damage of combustion chamber, however, this problem has not been completely solved yet. This paper proposes a data-driven method based on the Elman neural network (ENN) to predict the value of acoustic pressure of combustion instability. As a comparison, a model based on support vector machine (SVM) was built. It is proved that ENN has better prediction performance with a certain predicted time horizon compared to the SVM method. What is more, the prediction model based on ENN can adapt to time-varying characteristics of the transition scenario which is characterized by amplitude modulation, multiple frequencies, and irregular bursts. ENN model still maintains enough prediction accuracy for various input training sets, indicating that ENN can fully mine the features of data and has a strong feature extraction ability in combustion oscillation prediction. Hence, it is demonstrated that ENN is a promising prediction tool for thermoacoustic instability under various combustion conditions. These findings are of great significance for the accurate prediction and control of thermoacoustic instability.