Search Results (126)

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

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

Failures in gas path components pose significant risks to aeroengine performance and safety. Traditional fault diagnosis methods often require extensive data and struggle with real-time applications. This study addresses these critical limitations in traditional studies through physics-informed modeling and adaptive estimation. A nonlinear component-level model of the JT9D engine is developed through aero-thermodynamic governing equations, enhanced by a dual-loop iterative cycle combining Newton–Raphson steady-state resolution with integration-based dynamic convergence. An augmented state-space model that linearizes nonlinear dynamic models while incorporating gas path health characteristics as control inputs is novelly proposed, supported by similarity-criterion normalization to mitigate matrix ill-conditioning. A hybrid identification algorithm is proposed, synergizing partial derivative analysis with least squares fitting, which uniquely combines non-iterative perturbation advantages with high-precision least squares. This paper proposes a novel enhanced Kalman filter through integral compensation and three-dimensional interpolation, enabling real-time parameter updates across flight envelopes. The experimental results demonstrate a 0.714–2.953% RMSE in fault diagnosis performance, a 3.619% accuracy enhancement over traditional sliding mode observer algorithms, and 2.11 s reduction in settling time, eliminating noise accumulation. The model maintains dynamic trend consistency and steady-state accuracy with errors of 0.482–0.039%. This work shows marked improvements in temporal resolution, diagnostic accuracy, and flight envelope adaptability compared to conventional approaches. Full article

Reliable fault diagnosis in aero-engine bearing systems is essential for maintaining process stability and safety. However, acquiring fault samples in aerospace applications is costly and difficult, resulting in severely limited data for model training. Traditional methods often perform poorly under such constraints, lacking the ability to extract discriminative features or effectively correlate observed signal changes with underlying process faults. To address this challenge, this study presents a process-oriented framework—WSET-CNN-OOA-LSSVM—designed for effective fault recognition in small-sample scenarios. The framework begins with Wavelet Synchroextracting Transform (WSET), enhancing time–frequency resolution and capturing energy-concentrated fault signatures that reflect degradation along the process timeline. A tailored CNN with asymmetric pooling and progressive dropout preserves temporal dynamics while preventing overfitting. To compensate for limited labels, confidence-based pseudo-labeling is employed, guided by Mahalanobis distance and adaptive thresholds to ensure reliability. Classification is finalized using an Osprey Optimization Algorithm (OOA)-enhanced Least Squares SVM, which adapts decision boundaries to reflect subtle process state transitions. Validated on both test bench and real aero-engine data, the framework achieves 93.4% accuracy with only five fault samples per class and 100% in full-scale scenarios, outperforming eight existing methods. Therefore, the experimental results confirm that the proposed framework can effectively overcome the data scarcity challenge in aerospace bearing fault diagnosis, demonstrating its practical viability for few-shot learning applications in industrial condition monitoring. Full article

With the enhancement of thermodynamic cycle parameters and heat dissipation constraints in aero-engines, effective thermal management has become a critical challenge to ensure safe and stable engine operation. This study developed a transient temperature evaluation model applicable to the entire flight envelope, considering fluid–solid coupling heat transfer on both the main flow path and fuel systems. Firstly, the impact of heat transfer on the acceleration and deceleration performance of a low-bypass-ratio turbofan engine was analyzed. The results indicate that, compared to the conventional adiabatic model, the improved model predicts metal components absorb 4.5% of the total combustor energy during cold-state acceleration, leading to a maximum reduction of 1.42 kN in net thrust and an increase in specific fuel consumption by 1.18 g/(kN·s). Subsequently, a systematic evaluation of engine thermal management performance throughout the complete flight mission was conducted, revealing the limitations of the existing thermal management design and proposing targeted optimization strategies, including employing Cooled Cooling Air technology to improve high-pressure turbine blade cooling efficiency, dynamically adjusting low-pressure turbine bleed air to minimize unnecessary losses, optimizing fuel heat sink utilization for enhanced cooling performance, and replacing mechanical pumps with motor pumps for precise fuel supply control. Full article

The component characteristics of an aeroengine below idle speed are fundamental for start-up process simulations. However, due to experimental limitations, these characteristics must be extrapolated from data above idle speed. Existing extrapolation methods often suffer from insufficient utilization of available data, reliance on specific prior conditions, and an inability to capture unique operating modes (e.g., the stirring mode and turbine mode of compressor). To address these limitations, this study proposes a novel curve-and-surface fitting-based extrapolation method. The key innovations include: (1) extrapolating sub-idle characteristics through constrained curve/surface fitting of limited above-idle data, preserving their continuous and smooth nature; (2) transforming discontinuous isentropic efficiency into a continuous specific enthalpy change coefficient (SECC), ensuring physically meaningful extrapolation across all operating modes; (3) applying constraints during fitting to guarantee reasonable and smooth extrapolation results. Validation on a micro-turbojet engine demonstrates that the proposed method requires only conventional performance parameters (corrected flow, pressure/expansion ratio, and isentropic efficiency) above idle speed, yet successfully supports ground-starting simulations under varying inlet conditions. The results confirm that the proposed method not only overcomes the limitations of existing approaches but also demonstrates broader applicability in practical aeroengine simulations. Full article

Film cooling has been increasingly applied in turbine blade cooling design due to its excellent cooling performance. Although film-cooled blades demonstrate superior cooling effectiveness, the perforation design on blade surfaces compromises structural integrity, making fatigue failure prone to occur at cooling holes. Previous studies by domestic and international scholars have extensively investigated factors influencing film cooling effectiveness, including blowing ratio and hole geometry configurations. However, most research has overlooked the investigation of fatigue life in film-cooled blades. This paper systematically investigates blade fatigue life under various cooling air parameters by analyzing the relationships among cooling effectiveness, stress distribution, and fatigue life. Results indicate that maximum stress concentrations occur at cooling hole locations and near the blade root at trailing edge regions. While cooling holes effectively reduce blade surface temperature, they simultaneously create stress concentration zones around the apertures. Both excessive and insufficient cooling air pressure and temperature reduce thermal fatigue life, with optimal parameters identified as 600 K cooling temperature and 0.75 MPa pressure, achieving a maximum thermal fatigue life of 3400 cycles for this blade configuration. A thermal shock test platform was established to conduct fatigue experiments under selected cooling conditions. Initial fatigue damage traces emerged at cooling holes after 1000 cycles, with progressive damage expansion observed. By 3000 cycles, cooling holes near blade tip regions exhibited the most severe failure, demonstrating near-complete functional degradation. These findings provide critical references for cooling parameter selection in practical aeroengine applications of film-cooled blades. Full article

Jet fuel from direct coal liquefaction (DCL) is an important alternative kerosene and represents a high-performance fuel for specific applications in civil applications. The study on its chemical positions and combustion properties is critical for the development of surrogate models and related combustion reaction mechanisms, which is valuable for promoting its usage in aeroengines. However, research on DCL-derived jet fuel is rather scarce. Herein, this work reports a systematic study on a DCL-derived jet fuel and its blends with traditional RP-3 jet fuel in two different ratios. Specifically, major physicochemical properties related to the aviation fuel airworthiness certification process are measured. Advanced two-dimensional gas chromatography (GC × GC) analysis is used to analyze the detailed chemical compositions on the DCL derived jet fuel and its blend with RP-3, which is then employed for surrogate model development. Moreover, ignition delay times (IDTs) are measured by using a heated shock-tube (ST) facility for the blended fuels over a wide range of conditions. Combustion reaction mechanisms based on the surrogate models are developed to predict the experimental measured IDTs. Finally, sensitivity analysis and rate-of-production analysis are carried out to identify the key chemical kinetics controlling the ignition characteristics. This work extends the understanding of the physicochemical properties and ignition characteristics of alternative jet fuels and should be valuable for the practical usage of DCL derived jet fuels. Full article

With the development of intelligent manufacturing technology, the manufacturing industry is gradually realizing intelligent production. Especially for metal cutting with extremely complex processes, it is of great significance to realize intelligence. Taking the cutting process of aero-engine typical low-rigidity parts as the main line, this article builds an intelligent processing architecture based on a big data platform, which includes customized design of cutting tools, intelligent optimization of cutting parameters, simulation of cutting conditions, and online monitoring and control of cutting processes. At the same time, the realization of related key technologies is explained. Then, this article introduces in detail the intelligent decision-making process based on deep learning, the customized tool design process based on structural features, the simulation process of cutting based on geometric features of parts, as well as the monitoring and control process of Numerical Control (NC) machining based on condition perception. In addition, based on the processing requirements and difficulties of specific parts, formulate a specific intelligent implementation plan under this processing mode. Through the implementation of the above architecture and key technologies, the cutting processing system can automatically optimize the cutting parameters according to real-time working conditions and adjust its own cutting conditions. At the same time, machine tool condition, cutting tool condition, and low-rigidity part condition are real-time monitored to achieve high-precision, efficient, intelligent, and precise cutting of low-rigidity parts. The proposed architecture can provide a reference model for the research and application of intelligent cutting technology for low-rigidity parts. Full article

Active disturbance rejection control (ADRC) emerges as a promising control approach due to its partial model-based characteristics and strong disturbance rejection capabilities. Nevertheless, it is a difficult problem to tune various parameters of ADRC in practical applications. To address the challenge of parameter tuning, this work develops a parameter self-tuning rule based on spatial–temporal scale transformations to simplify the tuning process and enhance its control performance. In particular, based on the transformations of spatial–temporal scale, the parameter tuning relationships for ADRC’s components, including tracking differentiator (TD), extended state observer (ESO) and feedback controller, are provided for a second-order nonlinear system. Numerical simulations show that the proposed method can conveniently and effectively provide a set of well-tuned parameters for ADRC to boost the efficiency of control. Finally, the proposed parameter tuning rule is applied to the intake pressure control of the flight test chamber system, further validating its effectiveness. The results demonstrate that the ADRC with the proposed parameter self-tuning method significantly improves the precision of the intake pressure under different operating conditions, thereby ensuring the reliability of aeroengine flight tests. Full article

The elastic ring squeeze film damper (ERSFD), due to its compact structure and excellent mechanical properties, has been increasingly applied in various types of combination bearings for aero-engines. During operation, the force state of the elastic ring varies with different precession angles of the journal, leading to changes in the stiffness of the elastic ring. This study, based on a bidirectional fluid–structure interaction (FSI) theory, analyzes the deformation and stiffness of the elastic ring under different contact conditions. The time-varying stiffness curve of the elastic ring is obtained, and the influence of various parameters on its time-varying stiffness characteristics is further investigated. An equivalent stiffness method for the elastic ring is proposed, which improves accuracy by more than 3% at low speeds compared to traditional methods. Using this equivalent method, the effects of parameters such as the number of ring protrusions, protrusion width, protrusion angle, elastic ring thickness, and oil film eccentricity on the pressure distribution of the inner and outer oil films are analyzed. The results indicate that an increase in the number of elastic rings, protrusion width, axial length, and ring thickness leads to a rise in stiffness, with the number of protrusions having the strongest effect and the axial length having the weakest effect. Additionally, as the number of protrusions, protrusion width, and protrusion angle increase, both the damping and stiffness of the inner and outer oil films decrease by approximately 10%, with a more significant impact on the outer oil film than on the inner oil film. When the axial length and oil film eccentricity increase, both the damping and stiffness of the inner and outer oil films also increase, with the inner oil film being highly sensitive to eccentricity. However, excessive eccentricity enhances the nonlinearity of the oil film. The findings of this study provide a theoretical foundation for the design, application, and maintenance of combination bearings incorporating elastic ring squeeze film dampers. Full article

Elastic rings are extensively utilized in aero-engine rotor systems owing to their compact size and ease of assembly, where they play a critical role in vibration suppression during engine operation. The dynamic behavior of elastic rings is governed by their structural parameters, with stiffness being a pivotal factor influencing the rotor system’s performance. This study employs finite element methods to investigate the effects of elastic ring structural parameters, particularly the geometric features of bosses and internal/external assembly clearances, on stiffness nonlinearity, with a focus on its mechanisms and contributing factors. The results reveal that stiffness nonlinearity emerges when the whirling radius exceeds a critical threshold. Specifically, increasing the boss width, reducing the boss height, or augmenting the number of bosses all attenuate stiffness nonlinearity under identical whirling radii. Furthermore, external clearances exhibit a stronger capability to suppress stiffness nonlinearity compared to internal clearances. Engineering insights suggest that maintaining a small clearance fit during assembly effectively mitigates stiffness nonlinearity, thereby enhancing the rotor’s dynamic performance. This study elucidates the stiffness nonlinearity behavior of elastic rings in practical applications and provides actionable guidance for their design and operational optimization in rotor systems. Full article

The integration of more-electric technologies into aero-engines has revolutionized their multi-power architectures, substantially improving system maintainability and operational reliability. This advancement has established more-electric systems as a cornerstone of modern aerospace electrification research. Concurrently, liquid hydrogen (LH₂) emerges as a transformative solution for next-generation power generation systems, particularly in enabling the transition from 100 kW to megawatt-class propulsion systems. Beyond its superior energy density, LH₂ demonstrates dual functionality in thermal management: it serves as both an efficient coolant for power electronics (e.g., controllers) and a cryogenic source for superconducting motor applications. This study systematically investigates the electrification pathway for LH₂-fueled aero-engine multi-electric systems. First, we delineate the technical framework, elucidating its architectural characteristics and associated challenges. Subsequently, we conduct a comprehensive analysis of three critical subsystems including LH₂ storage and delivery systems, cryogenic cooling systems for superconducting motors, and Thermal management systems for high-power electronics. Finally, we synthesize current research progress and propose strategic directions to accelerate the development of LH₂-powered more-electric aero-engines, addressing both technical bottlenecks and future implementation scenarios. Full article

As the core of an aircraft’s power system, the stability and reliability of an aero-engine’s performance are crucial to flight safety. Addressing the issues of complex wiring and poor flexibility in traditional wired testing systems, this paper designs and implements a wireless transmission aero-engine pressure measurement system based on FPGA. By integrating front-end memory and a back-end test bench and utilizing LoRa wireless communication technology and the Reed–Solomon (RS) forward error correction (FEC) algorithm, the system significantly enhances the reliability and anti-interference capability of data transmission. Test results demonstrate that the system can monitor and record engine pressure parameters in real time in complex environments, with a notable reduction in bit error rate and packet loss rate, especially under strong interference conditions. This system resolves wiring challenges, enhances the real-time performance of monitoring links and the stability of data storage, and is characterized by high precision, high reliability, and automation. It is suitable for complex and harsh working environments and has broad application prospects in the aviation and military sectors. Full article