Search Results (660)

As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM). Traditional RUL prediction methods primarily fall into two main categories: physics-based and data-driven approaches. Physics-based methods mainly rely on extensive prior knowledge, limiting their scalability, while data-driven methods (including statistical analysis and machine learning) struggle with handling high-dimensional data and suboptimal modeling of multi-scale temporal dependencies. To address these challenges and enhance prediction accuracy and robustness, we propose a novel hybrid deep learning framework (CLSTM-TCN) integrating 2D Convolutional Neural Network (2D-CNN), Long Short-Term Memory (LSTM) network, and Temporal Convolutional Network (TCN) modules. The CLSTM-TCN framework follows a progressive feature refinement logic: 2D-CNN first extracts short-term local features and inter-feature interactions from input data; the LSTM network then models long-term temporal dependencies in time series to strengthen global temporal dynamics representation; and TCN ultimately captures multi-scale temporal features via dilated convolutions, overcoming the limitations of the LSTM network in long-range dependency modeling while enabling parallel computing. Validated on the NASA C-MAPSS data set (focusing on FD001), the CLSTM-TCN model achieves a root mean square error (RMSE) of 13.35 and a score function (score) of 219. Compared to the CNN-LSTM, CNN-TCN, and LSTM-TCN models, it reduces the RMSE by 27.94%, 30.79%, and 30.88%, respectively, and significantly outperforms the traditional single-model methods (e.g., standalone CNN or LSTM network). Notably, the model maintains stability across diverse operational conditions, with RMSE fluctuations capped within 15% for all test cases. Ablation studies confirm the synergistic effect of each module: removing 2D-CNN, LSTM, or TCN leads to an increase in the RMSE and score. This framework effectively handles high-dimensional data and multi-scale temporal dependencies, providing an accurate and robust solution for aeroengine RUL prediction. While current performance is validated under single operating conditions, ongoing efforts to optimize hyperparameter tuning, enhance adaptability to complex operating scenarios, and integrate uncertainty analysis will further strengthen its practical value in aircraft health management. Full article

In this paper, we aim to study the oil–gas two-phase flow characteristics, leakage characteristics, and critical oil sealing characteristics of the bearing cavity sealing system of aero-engine bearings. For this purpose, the unsteady solution models of the conventional bearing cavity sealing system and the graphite with oil-return groove bearing cavity sealing system based on the Euler–Euler two-phase flow method were established. The experimental device for the oil–gas two-phase flow for the bearing cavity was designed and constructed. Thus, the oil–gas two-phase oil sealing characteristics of both systems under different structural and working condition parameters were studied. The results show that the change in the sealing length does not affect the leakage of lubricating oil for the conventional bearing cavity sealing system. It was observed that the higher the rotate speed is, the greater the oil leakage and the greater the critical sealing pressure difference. The graphite with oil-return groove structure can significantly reduce the leakage of lubricating oil and the critical sealing pressure difference. The increase in the length and number of oil-return groove can effectively reduce the leakage of lubricating oil. The width of the oil-return groove has no obvious effect on the sealing and leakage characteristics of the lubricating oil. Full article

High-fidelity replication of compressor exit flow fields is critical for combustor design, yet current simulation facilities lack effective, decoupled control of flow parameters. This study proposes a coordinated optimization strategy combining segmented stationary blade twist with leading-edge baffle configurations. The blades are divided into three spanwise sections with independently optimized twist angles to match airflow deflection. Upstream baffles are redesigned by reducing thickness, shortening horizontal length, and adjusting spanwise position to improve total velocity distribution. The final Plate-T configuration achieves a peak total velocity error of ~3.0% and position error of ~8.5%, while maintaining deflection angle accuracy. Experimental validation confirms improved agreement with compressor outlet flow fields, providing robust support for studies on flame stability, emissions, and combustion performance, as well as guidance for aero-engine experimental facility design. Full article

Modern advanced aero-engine bearing systems typically exhibit structural and loading characteristics with high DN values. The harsh thermal environment and multi-physics loads under operating conditions render the reliability of bearing structural systems particularly sensitive to lubrication efficiency and bearing chamber temperature. This study performs simulation analyses of oil return processes and their influencing factors in an aero-engine main bearing chamber with complex structural features. The results show two primary causes of reduced scavenging performance. On the one hand, the local low-speed region at the inlet of the scavenge pipe causes some oil to fail to enter the scavenge pipe normally. On the other hand, the air in the bearing chamber is disturbed by the rotation of the rotor, which makes oil enter the oil sump with a tendency to return to the oil collection annulus, thereby causing poor oil return. Furthermore, two structural improvements of the oil sump are proposed. These improvements avoid the disruptive effects of circumferential fluid motion in the oil collection annulus on the pressure and velocity distribution within the bearing chamber, thereby improving scavenging performance. Full article

Aero-engines, as complex systems integrating numerous rotating components and accessory equipment, operate under harsh and demanding conditions. Prolonged use often leads to frequent mechanical wear and surface defects on accessory parts, which significantly compromise the engine’s normal and stable performance. Therefore, accurately and rigorously identifying failure modes is of critical importance. In this study, failure modes are categorized into notches, scuffs, and scratches based on original bearing structure images. The YOLOv8 architecture is adopted as the base framework, and a Dilated Reparameterization Block (DRB) is introduced to enhance the Dilation-Wise Residual (DWR) module. This structure uses a large convolutional kernel to capture fragmented and sparse features in wear images, ensuring a wide receptive field. The concept of structural reparameterization is incorporated into DWR to improve its ability to capture detailed target information. Additionally, the standard convolutional layer in the head of the improved DWR-DRB structure is replaced by Spatial-Depth Convolution (SPD-Conv) to reduce the loss of wear morphology and enhance the accuracy of fault feature extraction. Finally, a fusion structure combining Focaler and MPDIoU is integrated into the loss function to leverage their strengths in handling imbalanced classification and bounding box geometric regression. The proposed method achieves effective recognition and diagnosis of mechanical wear fault patterns. The experimental results demonstrate that, compared to the baseline YOLOv8, the proposed method improves the mAP50 for fault diagnosis and recognition from 85.4% to 91%. Full article

Reliability analysis of aeroengine structures is a critical task in aerospace engineering, but traditional methods often face challenges of low computational efficiency and insufficient accuracy when dealing with complex, high-dimensional, and nonlinear problems. This paper proposes a novel reliability assessment method (AC-Kriging) based on the Actor–Critic network and Kriging surrogate models to address these issues. The Actor network optimizes the sampling strategy for design variables, making sampling more efficient. The Critic network assesses the reliability of these samples to ensure accurate results, while a Kriging surrogate model replaces expensive finite element simulations and cuts computational cost. Three case studies demonstrate that AC-Kriging significantly outperforms traditional methods in both sampling efficiency and reliability estimation accuracy. This research provides an efficient and reliable solution for reliability analysis of aeroengine structures, with important theoretical and engineering application value. Three case studies demonstrate that AC-Kriging significantly outperforms traditional methods in both sampling efficiency and reliability-estimation accuracy, requiring only 52–147 samples to achieve comparable accuracy while maintaining the relative failure probability error within 0.87–7.27%. This research provides an efficient and reliable solution for the reliability analysis of aeroengine structures. Full article

To address the inefficiencies of traditional numerical simulations and the high cost of experimental validation in the aerodynamic–stealth integrated design of S-shaped inlets for aero-engines, this study proposes a novel parameter prediction model based on a fuzzy C-means (FCM) clustering and nonlinear dynamic adaptive particle swarm optimization-enhanced radial basis function neural network (NDAPSO-RBFNN). The FCM algorithm is applied to reduce the feature dimensionality of aerodynamic parameters and determine the optimal hidden layer structure of the RBF network using clustering validity indices. Meanwhile, the NDAPSO algorithm introduces a three-stage adaptive inertia weight mechanism to balance global exploration and local exploitation effectively. Simulation results demonstrate that the proposed model significantly improves training efficiency and generalization capability. Specifically, the model achieves a root mean square error (RMSE) of $3.81\times {10}^{-8}$ on the training set and $8.26\times {10}^{-8}$ on the test set, demonstrating robust predictive accuracy. Furthermore, $98.3\%$ of the predicted values fall within the $y=x\pm 3\beta$ confidence interval ($\beta =1.2\times {10}^{-7}$). Compared with traditional PSO-RBF models, the number of iterations of NDAPSO-RBF network is lower, the single prediction time of NDAPSO-RBF network is shorter, and the number of calls to the standard deviation of the NDAPSO-RBF network is lower. These results indicate that the proposed model not only provides a reliable and efficient surrogate modeling method for complex inlet flow fields but also offers a promising approach for real-time multi-objective aerodynamic–stealth optimization in aerospace applications. Full article

High-flow aeroengine transient tests involve strong coupling and external disturbances, which pose significant challenges for intake environment simulation systems (IESSs). This study proposes a compound control scheme that combines fixed-time active disturbance rejection with static decoupling methods. The scheme integrates a fixed-time sliding-mode controller (FT-SMC) and a super-twisting fixed-time extended-state observer (ST-FT-ESO). A decoupling transformation separates pressure and temperature dynamics into two independent loops. The observer estimates system states and total disturbances, including residual coupling, while the controller ensures fixed-time convergence. The method is deployed on a real-time programmable logic controller (PLC) and validated through hardware-in-the-loop (HIL) simulations under representative high-flow scenarios. Compared to conventional linear active disturbance rejection decoupling control (LADRDC), the proposed scheme reduces the absolute integral error (AIE) in pressure and temperature tracking by 71.9% and 77.9%, respectively, and reduces the mean-squared error (MSE) by 46.0% and 41.3%. The settling time improves from over 5 s to under 2 s. These results demonstrate improved tracking accuracy, faster convergence, and enhanced robustness against disturbances. Full article

The exhaust duct of aero-engine exhibits complex vibration response characteristics under the influence of temperature fields and vibration loads. Taking the non-axisymmetric exhaust duct of turboshaft engine as the object of study, a finite element model of the exhaust duct was established using three-dimensional finite element analysis methods to analyze the thermal modal and random vibration response characteristics under axial loading for large thin-walled non-axisymmetric exhaust ducts. The simulation analysis method was validated through thermal vibration experiments on the scaled model. In a thermal environment, the shape of the power spectral density curves for displacement and stress of the exhaust duct remains largely unchanged in the low-frequency range; however, the response frequencies exhibit a significant forward shift. When subjected to Y-axial loading, the amplitude of the X- and Z-direction displacement response at 1st order (12.96 Hz) and the stress response at 6th order (30.92 Hz) significantly increase. Random vibration loads excite multiple modes of the exhaust duct, with lower-order modes being more easily stimulated. When subjected to X- and Z-axial loading, 1st order (12.96 Hz) has the greatest impact on the X- and Z-direction displacement responses, while 2nd order (16.93 Hz) and 13th order (82.79 Hz) frequencies have the greatest impact on the displacement response in the Y-direction and equivalent stress response. When subjected to Y-axial loading, the 5th order (22.35 Hz) and 12th order (81.69 Hz) modes have the most significant effects on the displacement responses in the X, Y, and Z directions and equivalent stress responses. Attention to these orders is essential during the design process, along with implementing certain stiffness reinforcement measures to reduce response amplitudes. Full article

M50 steel is widely used in the manufacturing of high-end bearing components for aero-engine shafts, where an excellent surface performance is required to withstand harsh service conditions. In this study, plasma carburizing at different temperatures varying from 410 to 570 °C was performed on pre-nitrided M50 steel to investigate the influence of the temperature on the structural evolution and mechanical behavior of the self-lubricating functional layer. The microstructure, phase composition, hardness, and wear resistance of the carburized samples were fully characterized using scanning electron microscopy (SEM), Transmission electron microscopy (TEM), Raman spectroscopy, a nano-indenter, and other analytical techniques. The carbon-rich film with nano-domains contains a significant amount of sp³ bonds at low carburizing temperatures, exhibiting a Diamond-like carbon (DLC) film character. With the rise in the carburizing temperature, the initially distinct interface between the carbon-rich film and the compound layer gradually disappears as the nitrides are progressively replaced by carbides; the sp³ bond of the film is decreased, which reduces the hardness and wear resistance. Samples carburized at 490 °C with a homogeneous surface layer consisting of DLC film and a compound layer showed a low friction coefficient (about 0.22) and a 60% reduction in the wear rate compared with the nitrided specimen. The formation of a surface carbon-enriched layer also plays a role in avoiding oxidative wear. Full article

This study explores the aeroacoustic influence of leading-edge serrations applied to stator blades subjected to turbulent inflow, which is representative of rotor–stator interaction in turbomachinery. A set of serrated geometries—75 mm span, with up to 9 teeth corresponding to 10% chord amplitude—was fabricated via 3D printing and tested experimentally in a dedicated aeroacoustic facility at COMOTI. The turbulent inflow was generated using a passive grid, and far-field acoustic data were acquired using a semicircular microphone array placed in multiple inclined planes covering 15°–90° elevation and 0–180° azimuthal angles. The analysis combined power spectral density and autocorrelation techniques to extract turbulence-related quantities, such as integral length scale and velocity fluctuations. Beamforming methods were applied to reconstruct spatial distributions of sound pressure level (SPL), complemented by polar directivity curves to assess angular effects. Compared to the reference case, configurations with serrations demonstrated broadband noise reductions between 2 and 6 dB in the mid- and high-frequency range (1–4 kHz), with spatial consistency observed across measurement planes. The results extend the existing literature by linking turbulence properties to spatially resolved acoustic maps, offering new insights into the directional effects of serrated stator blades. Full article

Picosecond laser trepanning is a key technology for fabricating film cooling holes in aero-engine turbine blades, overcoming the limitations of conventional machining such as severe tool wear and thermal damage. However, optimizing this advanced process to achieve consistent, high-quality results remains a challenge. This study therefore systematically investigates the influence of key laser parameters (power, scanning speed, defocusing distance, and number of scans) on the geometric quality (diameter, taper, and roundness) of holes trepanned in GH4169 superalloy. The experimental results revealed that laser power and defocusing distance are the dominant factors controlling hole diameter and taper. Furthermore, a critical trade-off was identified concerning the number of scans: while more scans improved exit roundness, they also detrimentally increased entrance diameter and taper due to heat accumulation. Based on these findings, we propose a defect control strategy prioritizing a lower number of scans in the initial phase to effectively suppress molten material formation and preserve surface integrity. This work provides a valuable technological reference and theoretical foundation for the low-damage, high-reliability laser manufacturing of high-performance aerospace components. Full article

TiAl alloy offers advantages including low density, high specific strength and stiffness, and excellent high-temperature creep resistance. It is widely used in the aerospace, automotive, and chemical sectors, as well as in other fields. However, at temperatures of 800 °C and above, it forms a porous oxide film predominantly composed of TiO₂, which fails to provide adequate protection. Applying high-temperature protective coatings is therefore essential. Oxides demonstrating protective efficacy at elevated temperatures include Al₂O₃, Cr₂O₃, and SiO₂. The Pilling–Bedworth Ratio (PBR)—defined as the ratio of the volume of the oxide formed to the volume of the metal consumed—serves as a critical criterion for assessing oxide film integrity. A PBR value greater than 1 but less than 2 indicates superior film integrity and enhanced oxidation resistance. Among common oxides, Al₂O₃ exhibits a PBR value within this optimal range (1−2), rendering aluminum-based compound coatings the most extensively utilized. Aluminum coatings can be applied via methods such as pack cementation, thermal spraying, and hot-dip aluminizing. Pack cementation, being the simplest to operate, is widely employed. In this study, a powder mixture with the composition Al:Al₂O₃:NH₄Cl:CeO₂ = 30:66:3:1 was used to aluminize γ-TiAl intermetallic compound specimens via pack cementation at 600 °C for 5 h. Subsequent isothermal oxidation at 900 °C for 20 h yielded an oxidation kinetic curve adhering to the parabolic rate law. This treatment significantly enhanced the high-temperature oxidation resistance of the γ-TiAl intermetallic compound, thereby broadening its potential application scenarios. Full article

Nickel-based superalloys are widely utilized in critical hot-end components, such as aeroengine turbine blades, owing to their exceptional high-temperature strength, creep resistance, and oxidation resistance. During service, these components are frequently subjected to complex localized loading, leading to non-uniform plastic deformation and microstructure evolution within the material. Combining nanoindentation experiments with the crystal plasticity finite element method (CPFEM), this study systematically investigates the effects of loading rate and crystal orientation on the elastoplastic deformation of DD6 alloy under spherical indenter loading. The results indicate that the maximum indentation depth increases and hardness decreases with prolonged loading time, exhibiting a significant strain rate strengthening effect. The CPFEM model incorporating dislocation density effectively simulates the nonlinear characteristics of the nanoindentation process and elucidates the evolution of dislocation density and slip system strength with indentation depth. At low loading rates, both dislocation density and slip system strength increase with loading time. Significant differences in mechanical behavior are observed across different crystal orientations, which correspond to the extent of lattice rotation during texture evolution. For the [111] orientation, crystal rotation is concentrated and highly regular, while the [001] orientation shows uniform texture evolution. This demonstrates that anisotropy governs the deformation mechanism through differential slip system activation and texture evolution. Full article

The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components. Full article