A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path electrostatic signal noise, which challenges the gas-path component condition monitoring and feature extraction techniques. Firstly, the integration of CEEMDAN addresses modal aliasing and intermittent signal challenges, while the proposed low-pass filtering method autonomously selects valuable signal components. Additionally, the application of the WT in the unselected components enhances the extraction of useful information, presenting a unique and advanced approach to electrostatic signal denoising. Moreover, the proposed method is applied to simulated signals with different input signal-to-noise ratios and experimental fault electrostatic signals of a micro-turbojet engine. The comparison with several traditional approaches in a denoising test for the simulated signals and experimental signals reveals that the proposed method performs better in extracting the effective components of the signal and eliminating noise.

The combustion chamber is a critical component of turbojet engines, and airflow distribution plays an essential role in ensuring flame stability and optimizing combustion efficiency. This study investigates a miniature annular combustion chamber by employing SolidWorks 2022 software to model an evaporative tube combustion chamber. A dedicated combustion test platform was constructed for the proposed miniature combustion chamber. By adjusting the air and fuel flow ratios entering the evaporative tube, the temperature at the flame tube outlet was measured, and the combustion efficiency was subsequently calculated. In addition, numerical analysis was conducted using ANSYS/CFX software to simulate the flow field in the combustion chamber. The following conclusions were drawn from an analysis of the variations in the flow field and temperature field during the simulation process: When the flow rates in the ignition and dilution zones of the miniature annular combustion chamber remained constant, modifying the air-fuel flow ratio within the evaporative tube significantly enhanced the combustion characteristics within the chamber. Specifically, the combustion efficiency is closely related to the ratio of the air mass flow rate to the fuel mass flow rate within the evaporation tube. The highest combustion efficiency was achieved when the ratio fell within the range of 4.20 to 4.96. Furthermore, the area-averaged velocity at the combustion chamber outlet was independent of the air-fuel flow ratio but exhibited a positive correlation with the fuel flow entering the combustion chamber.

A path tracking controller based on active disturbance rejection control(ADRC) theory is presented in this paper to solve path tracking problem in inverse vehicle handling dynamics. The basic idea behind the work is to design an active disturbance rejection controller according to yaw rate and lateral displacement during a vehicle travels along a prescribed path to generate an expected trajectory which guarantees minimum clearance to the prescribed path. Aiming at this purpose, using preview follower theory, a linear extended state observer based on lateral displacement is designed. Considering yaw angle of vehicle, a non-linear combination function combined error of lateral displacement as well as error of yaw angle is designed according to monotone bounded hyperbolic of tangent function. Finally, a real vehicle test is executed to verify the rationality of the path tracking controller. At the same time, according to characteristics of pavement file in Carsim, a 3-D virtual pavement model is established and ride comfort simulation of random pavement is carried out in the software model. The results show that the minimum lateral position error of the generated path tracking trajectory can be good indicators of successful solving of the path tracking problem in inverse vehicle handling dynamics for ADRC. More precisely, there is higher calculation accuracy for the algorithm of the ADRC to solve the path tracking problem. The study can help drivers easily identify safe lane-keeping trajectories and area.

To address the strong nonlinearity and time-delay characteristics of micro turbojet engines (MTEs) under dynamic operating conditions, this paper proposes an identification modeling approach that employs the Aquila Optimizer (AO) to jointly optimize the number of hidden layer nodes, learning rate, and input/feedback delay orders of a NARX neural network. Based on a self-developed test bench, five-dimensional input data (including fuel flow rate and inlet temperature) and three-dimensional output data (including rotational speed, thrust, and exhaust gas temperature (EGT)) covering the entire acceleration and deceleration processes were collected. Experimental results demonstrate that, during the relatively stable acceleration phase, the AO-NARX model achieves extremely high fitting accuracy, with mean squared errors (MSEs) for rotational speed, thrust, and EGT predictions as low as 0.689 × 10 ^-5 , 1.054 × 10 ^-5 , and 0.740 × 10 ^-5 , respectively, and coefficients of determination (R ² ) exceeding 0.999 for all. Compared with PSO-NARX, SSA-NARX, and GA-NARX models, the AO-NARX reduces the MSE for thrust prediction by more than 60% and the MSE for EGT prediction by more than 80%. In the deceleration phase characterized by intense nonlinear fluctuations, the AO-NARX model exhibits remarkable residual suppression capability, maintaining the mean relative error (MRE) for thrust within 5.25% and significantly outperforming the overshoot phenomena observed in the comparison models; the EGT prediction MSE is reduced by 73.0% relative to GA-NARX, demonstrating its quantitative performance in suppressing transient errors and capturing thermal inertia delays.