Search Results (1,038)

Remaining useful life (RUL) prediction is a core technology in prognostics and health management (PHM), crucial for ensuring the safe and efficient operation of modern industrial systems. Although deep learning methods have shown potential in RUL prediction, they often face two major challenges: an insufficient generalization ability when distribution gaps exist between training data and real-world application scenarios, and the difficulty of comprehensively capturing complex equipment degradation processes with single-modal data. A key challenge in current research is how to effectively fuse multimodal data and leverage transfer learning to address RUL prediction in small-sample and cross-condition scenarios. This paper proposes an innovative deep multimodal fine-tuning regression (DMFR) framework to address these issues. First, the DMFR framework utilizes a Convolutional Neural Network (CNN) and a Transformer Network to extract distinct modal features, thereby achieving a more comprehensive understanding of data degradation patterns. Second, a fusion layer is employed to seamlessly integrate these multimodal features, extracting fused information to identify latent features, which are subsequently utilized in the predictor. Third, a two-stage training algorithm combining supervised pre-training and fine-tuning is proposed to accomplish transfer alignment from the source domain to the target domain. This paper utilized the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbine engine dataset publicly released by NASA to conduct comparative transfer experiments on various RUL prediction methods. The experimental results demonstrate significant performance improvements across all tasks. Full article

Porous media gas bearings utilize gas as a lubricating medium to achieve non-contact support technology. Compared with traditional liquid-lubricated bearings or rolling bearings, they are more efficient and environmentally friendly. With the uniform gas film pressure of gas bearings, the rotating shaft can achieve mechanical motion with low friction, high rotational speed, and long service life. They have significant potential in improving energy efficiency and reducing carbon emissions, enabling oil-free lubrication. By eliminating the friction losses of traditional oil-lubricated bearings, porous media gas bearings can reduce the energy consumption of industrial rotating machinery by 15–25%, directly reducing fossil energy consumption, which is of great significance for promoting carbon neutrality goals. They have excellent prospects for future applications in the civil and military aviation fields. Based on the three-dimensional flow characteristics of the bearing’s fluid domain, this paper considers the influences of the transient flow field in the variable fluid domain of the gas film and the radial pressure gradient of the gas film, establishes a theoretical model and a three-dimensional simulation model for porous media gas bearings, and studies the static–dynamic pressure coupling mechanism of porous media gas bearings. Furthermore, through the trial production of bearings and performance tests, the static characteristics are verified, and the steady-state characteristics are studied through simulation, providing a basis for the application of gas bearings made from porous media materials in the civil and military aviation fields. Full article

With the advancement of intelligent human–computer interaction (IHCI) technology, the accurate recognition of an operator’s intent has become essential for improving the collaborative efficiency in complex tasks. To address the challenges posed by stringent safety requirements and limited data availability in pilot intent recognition within the aviation domain, this paper presents a human intent recognition model based on operational sequence comparison. The model is built based on standard operational sequences and employs multi-dimensional scoring metrics, including operation matching degree, sequence matching degree, and coverage rate, to enable real-time dynamic analysis and intent recognition of flight operations. To evaluate the effectiveness of the model, an experimental platform was developed using Python 3.8 (64-bit) to simulate 46 key buttons in a flight cockpit. Additionally, five categories of typical flight tasks along with three operational test conditions were designed. Data were collected from 10 participants with flight simulation experience to assess the model’s performance in terms of recognition accuracy and robustness under various operational scenarios, including segmented operations, abnormal operations, and special sequence operations. The experimental results demonstrated that both the linear weighting model and the feature hierarchical recognition model enabled all three feature scoring metrics to achieve high intent recognition accuracy. This approach effectively overcomes the limitations of traditional methods in capturing complex temporal relationships while also addressing the challenge of limited availability of annotated data. This paper proposes a novel technical approach for intelligent human–computer interaction systems within the aviation domain, demonstrating substantial theoretical significance and promising application potential. Full article

Bearing clearance, prevalent in multi-supports rotor systems of aero engines, exerts a significant influence on the dynamics of rotor systems, actuators, and aero engines. The essence of it lies in the complex mechanical effects between the bearing and support. These effects become more complicated when significant relative angular deflections between the bearing and support exist, which is rarely considered in previous studies. In this paper, a model of support structure with bearing clearance considering angular deflections is proposed, and a mechanical model of the multi-supports rotor system with bearing clearance is developed. The dynamic response of the multi-supports rotor system with bearing clearance is investigated by numerical calculation and experimental verification. The results indicate that, in addition to the rotational frequency, remarkable harmonic frequency components occur in the response, which are generated by the relative movement and periodical collision between the bearing and support, and the relative angular deflections between the bearing and support have a significant impact on the amplitude of them; reducing the bearing clearance or increasing the misalignment both leads to a notable increase in the amplitudes of the harmonic frequency components. Full article

The operation range of the adaptive cycle engine (ACE) compression system is constrained by both the compression components and the bypass ducts, resulting in intricate matching mechanisms. Conventional analysis methods struggle to adequately evaluate the feasible operating range or the coupled constraints between components. This study employs an integrated hybrid-dimensional approach, combining zero-dimensional bypass analysis with one-dimensional/quasi-two-dimensional component analysis, to systematically investigate the matching effects of a triple-bypass compression system. The influence of key matching parameters, including the compression component operating points, high-pressure (HP) and low-pressure (LP) shaft speeds, and the core-driven fan stage (CDFS) variable inlet guide vane (VIGV) angles, is investigated. Results indicate that compression component matching primarily influences adjacent downstream bypass ratios, while HP/LP shaft speeds and the CDFS VIGV angle predominantly regulate the first and second bypass ratios. The feasible operating envelope is determined by the superimposed effects of these control parameters. To maximize the total bypass ratio, optimal operation requires increasing the front fan stall margin, elevating LP shaft speed, reducing HP shaft speed, and implementing partial CDFS VIGV closure to enhance pre-swirl. These findings provide critical guidance for control logic refinement and design optimization in advanced variable-cycle compression systems. 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

High-pressure compressor (HPC) blades of aero engines inevitably exhibit various uncertain geometric deviations, which deteriorate engine performance and increase maintenance costs. Although the condition-based maintenance (CBM) strategy is increasingly adopted to reduce costs by tailoring repair actions based on condition monitoring data, maintenance practices often still rely on original equipment manufacturer (OEM) recommendations. To further refine the CBM strategy, this paper proposes an uncertainty quantification method based on the engine performance digital twin (PDT) model to quantify the impact of HPC blade geometric deviations on overall engine performance. The PDT model is developed by coupling computational fluid dynamics simulations with a zero-dimensional performance model using real operating data and is validated for high predictive accuracy. Surrogate models based on support vector regression are employed to efficiently quantify the impact of combined geometric deviations. The results show that combined deviations cause reductions in mass flow, pressure ratio, and efficiency while increasing exhaust gas temperature and specific fuel consumption. The proposed methodology is applied to a CBM scenario to demonstrate its effectiveness. In the real maintenance process, this method enables the prediction of performance after repair, facilitating optimized maintenance strategies. 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

In this study, 10% nitric acid was employed to remove the aluminum coating on the cobalt-based superalloy K6509, with a focus on elucidating the corrosion mechanism and evaluating the effect of ultrasonic on the removal process. The results shows that ultrasonic treatment (40 kHz) significantly improves coating removal efficiency, increasing the maximum corrosion rate by 46.49% from 2.5413 × 10⁻⁷ g·s⁻¹·mm⁻² to 4.7488 × 10⁻⁷ g·s⁻¹·mm⁻² and reducing removal time from 10 min to 6 min. This enhancement is attributed to cavitation effect of ultrasonic bubbles and the shockwave-accelerated ion diffusion, which together facilitate more efficient coating degradation and results in a smoother surface. In terms of corrosion behavior, the difference in phase composition between the outer layer and the interdiffusion zone (IDZ) plays a decisive role. The outer layer is primarily composed of β-(Co,Ni)Al phase, which is thermodynamically less stable in acidic environments and thus readily dissolves in 10% HNO₃. In contrast, the IDZ mainly consists of Cr₂₃C₆, which exhibit high chemical stability and a strong tendency to passivate. These characteristics render the IDZ highly resistant to nitric acid attack, thereby forming a protective barrier that limits acid penetration and helps maintain the integrity of the substrate. 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

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