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This article focuses on the fixed-time stability of a class of nonlinear systems with uncertain disturbances and time-varying delays. Different from other papers, the second-order sliding mode control algorithm (SOSMCA) is developed in this article to study the fixed-time stability of nonlinear systems for the first time. Additionally, the novel sliding mode surface (SMC) and second-order SMC are presented. And the stability and reachability of the sliding-mode dynamics under a faster settling time are demonstrated. Finally, the applicability and validity of the obtained SOSMCA are demonstrated by a simulation example with the F-404 aircraft engine model. [ABSTRACT FROM AUTHOR]

Carbonaceous particles, such as soot, make up a notable fraction of atmospheric particulate matter and contribute substantially to anthropogenic climate forcing, air pollution, and human health impacts. Thermal–optical analysis (TOA) is one of the most widespread methods used to speciate carbonaceous particles and divides total carbon (TC) into the operationally defined quantities of organic carbon (OC; carbon that has evolved during slow heating in an inert atmosphere) and elemental carbon (EC). While multiple studies have identified fundamental scientific reasons for uncertainty in distinguishing OC and EC, far fewer studies have reported on between-laboratory reproducibility. Moreover, existing reproducibility studies have focused on complex atmospheric samples. The real-time instruments used for regulatory measurements of the mass concentration of aircraft engine non-volatile particulate matter (nvPM) emissions are required to be calibrated to the mass of EC, as determined by TOA of the filter-sampled emissions of a diffusion flame combustion aerosol source (DFCAS). However, significant differences have been observed in the calibration factor for the same instrument based on EC content determined by different calibration laboratories. Here, we report on the reproducibility of TC, EC, and OC quantified using the same TOA protocol, instrument model (Model 5L, Sunset Laboratory), and software settings (auto-split-point: Calc405) across five different laboratories and instrument operators. Six unique data sets were obtained, with one laboratory operating two instruments. All samples were collected downstream of an aircraft engine after treatment with a catalytic stripper to remove volatile organics. Between-laboratory contributions made up a majority of the within-filter uncertainties for EC and TC, even for these relatively well-controlled samples. Overall, expanded (k = 2) uncertainties due to measurement reproducibility correspond to 17 %, 15 %, and 13 % of the nominal values for EC, OC, and TC, respectively, and 7.3 % in the EC / TC ratio. These values are lower than previous studies, including atmospheric samples without volatile organic removal; therefore, they likely represent lower limits for the uncertainties of the TOA method. [ABSTRACT FROM AUTHOR]

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper. In pursuit of more comprehensive data feature extraction and enhanced RUL prognostication precision, the Convolutional Neural Network (CNN) is selected for its capacity to discern high-dimensional features amid the intricate dynamics of the data. Concurrently, the Gated Recurrent Unit (GRU) network is leveraged for its robust capability in extracting temporal features inherent within the data. We combine the two to construct a CNN-GRU hybrid network. Moreover, the integration of data distribution alongside correlation and monotonicity indices is employed to winnow the input of multi-sensor monitoring parameters into the CNN-GRU network. Finally, the engine RULs are predicted by the trained model. In this paper, experiments are conducted on a sub-dataset of the National Aeronautics and Space Administration (NASA) C-MAPSS multi-constraint dataset to validate the effectiveness of the method. Experimental results have demonstrated that this method has high accuracy in RUL prediction tasks, which can powerfully demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Al4SiC4 shows excellent heat resistance, thermal shock resistance, machinability, and oxidation resistance. We focused on Al4SiC4‐based ceramics with SiC as a non‐oxide matrix for ceramic matrix composites for aircraft jet engines. In this study, monolithic Al4SiC4 and Al4SiC4/SiC ceramics were fabricated by hot‐pressing, and a corrosion test against molten calcium‐magnesium‐alumino‐silicate (CMAS) was conducted at 1350°C for 12–100 h in air, and their corrosion behavior was investigated. Scanning electron microscopy and energy‐dispersive X‐ray spectroscopy results revealed that severe damage was not observed at the interface between CMAS and the samples after the CMAS corrosion test. The recession of Al4SiC4‐100, ‐10, and SiC‐100 after corrosion for 100 h was 80–90 µm, and that of Al4SiC4‐50 was the highest of all samples and the value was 130 µm. The dissolution behavior of the oxidation layer into molten CMAS via a corrosion reaction was dependent on the composition of both the sample and the oxidation layer, the thickness, and the microstructure of the oxidation layers. The dominant mechanism of reaction between CMAS and Al4SiC4‐100, ‐90, and ‐50 samples was concluded to be the dissolution of the oxidation products, while in SiC‐100 and Al4SiC4‐10 samples, the dominant reaction was determined to be direct corrosion of the surface with CMAS. [ABSTRACT FROM AUTHOR]

This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed of equal-length segments, enabling the model to capture data patterns across multiple time scales by adjusting segment lengths. Finally, the local attention mechanism enhances feature extraction by accurately identifying and weighting important time segments, thereby strengthening the model's understanding of the local features of the time series, followed by feature fusion. The model demonstrates outstanding performance in time series prediction tasks by effectively capturing sequence information across various time scales. Experimental validation illustrates the superior performance of BiLSTM-MLAM compared to six baseline methods across multiple datasets. When predicting the remaining life of aircraft engines, BiLSTM-MLAM outperforms the best baseline model by 6.66% in RMSE and 11.50% in MAE. In the LTE dataset, it achieves RMSE improvements of 12.77% and MAE enhancements of 3.06%, while in the load dataset, it demonstrates RMSE enhancements of 17.96% and MAE improvements of 30.39%. Additionally, ablation experiments confirm the positive impact of each module on prediction accuracy. Through segment length parameter tuning experiments, combining different segment lengths has resulted in lower prediction errors, affirming the effectiveness of the multi-scale fusion strategy in enhancing prediction accuracy by integrating information from multiple time scales. [ABSTRACT FROM AUTHOR]

This comprehensive review examines the role of sustainable aviation fuels (SAFs) in promoting a more environmentally responsible aviation industry. This study explores various types of biofuels, including hydroprocessed esters and fatty acids (HEFAs), Fischer–Tropsch (FT) fuels, alcohol-to-jet (ATJ) fuels, and oil derived from algae. Technological advancements in production and processing have enabled SAF to offer significant reductions in greenhouse gas emissions and other pollutants, contributing to a cleaner environment and better air quality. The review addresses the environmental, economic, and technical benefits of SAF, as well as the challenges associated with their adoption. Lifecycle analyses are used to assess the net environmental benefits of SAF, with a focus on feedstock sustainability, energy efficiency, and potential impacts on biodiversity and land use. Challenges such as economic viability, scalability, and regulatory compliance are discussed, with emphasis on the need for supportive policies and international collaboration to ensure the long-term sustainability of SAF. This study also explores current applications of SAF in commercial airlines and military settings, highlighting successful case studies and regional differences driven by policy frameworks and government incentives. By promoting technological innovation and addressing regulatory and economic barriers, SAF has the potential to play a crucial role in the aviation industry's transition toward sustainability. [ABSTRACT FROM AUTHOR]

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine's piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data. [ABSTRACT FROM AUTHOR]

A time-dependent third-order shear deformation theory (TSDT) approach on the displacements of thick functionally graded material (FGM) conical shells under dynamic thermal vibration is studied. Dynamic equations of motion with TSDT for thick FGM conical shells are applied directly with the partial derivative of variable R* θ in the curve coordinates (x, θ , z) instead of y in the Cartesian coordinates (x, y, z) for thick FGM plates, where R* is the middle-surface radius at any point on conical shells. The generalized differential quadrature (GDQ) numerical method is used to solve the dynamic differential equations in equilibrium matrix forms under thermal loads. It is the novelty of the current study to identify the parametric effects of shear correction coefficient, environment temperature, TSDT model, and FGM power law index on the displacements and stresses in the thick conical shells only subjected to sinusoidal heating loads. The physical parts with values on the length-to-thickness ratio equals 5, and 10 FGMs can be used in an area of an airplane engine that usually operates near more than 1000 K of temperatures when the thermal stress is considered and affected. The important findings of the presented study are listed as follows. The values of normal stress are in decreasing tendencies with time in cases when the coefficient c 1 equals 0.925925/mm2 in TSDT and length-to-thickness ratio equals 5. The shear stress values in x plane z direction on the minor middle-surface radius (r) equals the major middle-surface radius (R) over 8 and length-to-thickness ratio equals to 5 can withstand T = 1000 K of pressure. [ABSTRACT FROM AUTHOR]

Fault detection is an important part of aeroengine health management. Intelligent fault detection methods represented by machine learning have been widely studied. However, most studies assume that training and test data follow the same distribution, which is unrealistic. Due to the degradation of engine performance or change of engine operating environment, the historical operation data of aeroengines are different from the current operation data of the engine. If the engine history operation data are directly used to train a fault detection model, the fault detection of the current engine may lead to low efficiency and affect the reliability of fault detection. In order to overcome this problem, transfer learning is introduced into aircraft engine fault detection in this paper. This paper combines transfer learning with support vector data description (SVDD), a common fault detection algorithm, and proposes SVDD-based transfer learning (SVDD-TL). This algorithm takes the spherical center of the SVDD as the knowledge structure to transfer from the source domain to the target domain, which can improve the detection accuracy of the model in the target domain. A fault detection experiment for an aeroengine was designed. Single and mixed fault data were used in the experiment, and the variation of fault data quantity was considered. Experimental results showed that the proposed method can improve the fault detection accuracy of the model in the target domain and still have good detection performance when the amount of fault data changes. [ABSTRACT FROM AUTHOR]

Thermal boundary conditions of the turbine disk cavity system are of great importance in the design of secondary air systems in aero-engines. This study aims to investigate the complex heat transfer mechanisms of a rotating turbine disk under high-speed conditions. A high-speed rotating free-disk model with Dorfman empirical solutions is developed to evaluate the heat transfer performance considering various factors. Specifically, the influence of compressibility, variable properties, and heat dissipation is determined using theoretical and numerical analyses. In particular, a novel combined solution method is proposed to simplify the complex heat transfer problem. The results indicate that the heat transfer performance of a free disk is primarily influenced by the rotating Mach number, rotating Reynolds number, Rossby number, and wall temperature ratio. The heat transfer temperature and Nusselt number of the free disk are strongly correlated with the rotating Mach number and rotating Reynolds number. Analysis reveals that heat dissipation is a critical factor affecting the accurate evaluation of the heat transfer performance of the turbine disk. Thus, the combined solution method can serve as a reference for future investigations of flow and heat transfer in high-speed rotating turbine disk cavity systems in aero-engines. [ABSTRACT FROM AUTHOR]

Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and monitor aircraft contrails to address the challenges associated with climate change. We propose the SegX-Net architecture, a highly efficient and lightweight model that combines the DeepLabV3+, upgraded, and ResNet-101 architectures to achieve superior segmentation accuracy. We evaluated the performance of our model on a comprehensive dataset from Google research and rigorously measured its efficacy with metrics such as IoU, F1 score, Sensitivity and Dice Coefficient. Our results demonstrate that our enhancements have significantly improved the efficacy of the SegX-Net model, with an outstanding IoU score of 98.86% and an impressive F1 score of 99.47%. These results unequivocally demonstrate the potential of image segmentation methods to effectively address and mitigate the impact of air conflict on global warming. Using our proposed SegX-Net architecture, stakeholders in the aviation industry can confidently monitor and mitigate the impact of aircraft shrinkage on the environment, significantly contributing to the global fight against climate change. [ABSTRACT FROM AUTHOR]

Space-age technology makes it possible to travel faster than the speed of sound from one continent to another one or even to make suborbital flight as a space tourism. The existing technology challenge is to bring this innovative way of travel without damaging the environment to ensure human safety and to preserve environment balance. The sonic boom issue is one of the concerns when flying faster than sound. The issue must be taking into account at the stage of developing environmentally friendly high-speed vehicle. The next generation supersonic transport is fundamentally different from subsonic medium- and long-range aircraft in a breakthrough feature: it ensures a long-term supersonic cruise flight in the range of Mach numbers 1.6–1.8. For a successful implementation of the civil supersonic project at a new stage of technology development, it is necessary to solve a number of scientific and technical problems. In the present paper some of them are under discussion. In particular, the addressed issues are as follows: how to achieve a high level of aerodynamic efficiency at supersonic cruise along with low sonic boom; how to fit into low noise requirements at take-off and landing modes of supersonic aircraft with a moderate-bypass-ratio engine; how to implement a wing with high load factor and fuselage with long nose part to fit into required airframe structural stiffness, etc. Also, new technology approaches in multidisciplinary optimization task to achieve aircraft design goals of low drag, low boom and low noise characteristics are discussed. A few examples of optimal supersonic aircraft configurations along with passenger cabin are given. The paper was presented at the X-th IAA Symposium "Space Flight Safety" in St.-Petersburg on June 2023. • Preliminary aerodynamic design,. • Advanced supersonic transport,. • Sonic boom,. • Multifactor optimization,. • Aerodynamic configuration. [ABSTRACT FROM AUTHOR]

Carbonaceous particles, such as soot, make up a notable fraction of atmospheric particulate matter and contribute substantially to anthropogenic climate forcing, air pollution, and human health. Thermal-optical analysis (TOA) is one of the most widespread methods used to speciate carbonaceous particles and divides total carbon (TC) into the operationally defined quantities of organic carbon (OC; carbon evolved during slow heating in an inert atmosphere) and elemental carbon (EC). While multiple studies have identified fundamental scientific reasons for uncertainty in distinguishing OC and EC, far fewer studies have reported on interlaboratory reproducibility. Moreover, existing reproducibility studies have focused on complex atmospheric samples. The real-time instruments used for regulatory measurements of aircraft engine non-volatile particulate matter (nvPM) mass emissions are required to be calibrated to the mass of EC determined by TOA of the filter-sampled emissions of a diffusion flame source. However, significant differences have been observed in the calibration factor for the same instrument based on EC content determined by different calibration laboratories. Here, we report on the reproducibility of TC, EC, and OC quantified using the same TOA protocol, instrument model (Sunset 5L), and software settings (auto split-point: Calc405) across five different laboratories and instrument operators. Six unique data sets were obtained, with one laboratory operating two instruments. Samples were collected downstream of an aircraft engine after treatment with a catalytic stripper to remove volatiles. We compared laboratory-reported uncertainties with actual variability in the data set, the difference of which (dark uncertainty) was substantial. Interlaboratory (dark) contributions increase uncertainties by a factor of 1.2 – 1.6 relative to the laboratory-reported uncertainties, even for these relatively simple samples (combustion particles downstream of a stripper), resulting in uncertainties of 26 % (k = 2) for EC. Uncertainties were a little larger for EC than for OC. These results indicate that current TOA uncertainties are underestimated and should be adjusted upwards to reflect these interlaboratory differences. [ABSTRACT FROM AUTHOR]

The accurate estimation of the remaining useful life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of the attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention-based hierarchical Transformer (STAR) framework. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrating hierarchical encoder–decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms the current state-of-the-art models for RUL prediction. [ABSTRACT FROM AUTHOR]

As the operational status of aircraft engines evolves, their fault modes also undergo changes. In response to the operational degradation trend of aircraft engines, this paper proposes an aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. The model can be directly applied to raw monitoring data without the need for additional algorithms to extract fault degradation features. It fully leverages the advantages of 1DCNN in extracting local features along the spatial dimension and incorporates CBAM, a channel and spatial attention mechanism. CBAM could assign higher weights to features relevant to fault categories and make the model pay more attention to them. Subsequently, it utilizes BiLSTM to handle nonlinear time feature sequences and bidirectional contextual feature information. Finally, experimental validation is conducted on the publicly available CMAPSS dataset from NASA, categorizing fault modes into three types: faultless, HPC fault (the single fault), and HPC&Fan fault (the mixed fault). Comparative analysis with other models reveals that the proposed model has a higher classification accuracy, which is of practical significance in improving the reliability of aircraft engine operations and for Remaining Useful Life (RUL) prediction. [ABSTRACT FROM AUTHOR]

Aircraft engine icing caused by supercooled large droplets (SLD) poses a significant threat to flight safety. In this paper, the SLD impingement characteristics of the rotating spinner were investigated using FLUENT UDS and the governing equations for water droplet motion were solved based on the Eulerian method. The droplet breakup was simulated using the number density equation, while the droplet rebound and splashing were simulated using a semiempirical model. The effects of rotational speed, droplet diameter, and inflow velocity on the SLD impingement characteristics of the rotating spinner were studied. Some new valuable insights have been found for the SLD impingement. The results indicated that as the rotational speed increases, the local collection efficiency of the rotating spinner decreases. Higher rotational speed results in reduced droplet impingement angle and stronger droplet rebound and splashing. For the droplets with diameters smaller than 111 μm, the local collection efficiency increases with the increase of the droplet diameter. However, when the droplet diameter exceeds 111 μm, the local collection efficiency decreases near the leading edge of the rotating spinner. Additionally, the local collection efficiency decreases as the inflow velocity increases near the leading edge of the rotating spinner. However, higher inflow velocities lead to larger droplet impingement angles, resulting in higher local collection efficiency near the tail of the rotating spinner. The critical impingement angle increases with the increase of the inflow velocity, leading to a more pronounced rebound and splashing of SLD. The research in this paper provides useful help for ice shape prediction and anti-icing system design of rotating spinner in SLD environment. [ABSTRACT FROM AUTHOR]

Predictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. To achieve predictive maintenance, engine sensor data are harnessed to assess the wear and tear of engines. In this research, a Long Short-Term Memory (LSTM) architecture was employed to forecast the remaining lifespan of aircraft engines. The LSTM model was evaluated using the NASA Turbofan Engine Corruption Simulation dataset and its performance was benchmarked against alternative methodologies. The results of these applications demonstrated exceptional outcomes, with the LSTM model achieving the highest classification accuracy at 98.916% and the lowest mean average absolute error at 1.284%. [ABSTRACT FROM AUTHOR]

Thermal barrier coatings (TBCs) are critical for the reliability and lifespan of aircraft engine turbine blades. In this study, a novel quaternary rare-earth oxide (La 2 O 3 , Gd 2 O 3 , Yb 2 O 3 , and Y 2 O 3) co-doped ZrO 2 solid solution, referred to as 1La-xGd-2Yb-3.5YSZ, was synthesized by varying the doping amount of Gd 2 O 3 from 0 to 6 mol. %. The impact of co-doping-induced lattice distortion on the phase composition, high-temperature phase stability, sintering resistance, and mechanical and thermal properties of the solid solution has been explored. It is found that the addition of Gd 2 O 3 stabilizer effectively increased the lattice distortion of the solid solution, which led to improved phase stability and sintering resistance, enhanced mechanical properties, and low thermal conductivity. Our findings highlight the promising properties of the 1La-xGd-2Yb-3.5YSZ solid solution, making it an attractive candidate for TBC materials. [ABSTRACT FROM AUTHOR]

Analyzing the research directions of leading aviation companies, it is evident that biocomponents will soon become a very important addition to the fuel used in turbine aircraft engines. Similarly, intensive efforts are underway to implement this type of solution in the armed forces. Here, the situation is more complex. All military fuels are intended for long-term storage, and bio-additives significantly alter the properties of fuels during this specific storage process. These changes invariably result in the deterioration of fuel quality parameters. The article presents an analysis and conclusions related to biocomponents as additives to F-35 fuel (NATO code-the military equivalent of Jet A-1 fuel). F-35 aviation fuel mixtures with the addition of biocomponents (HVO-Hydrorefined Vegetable Oil) at concentrations of 0–20% (V/V) were independently composed and stored for extended periods (0–6 months). The disadvantages and potential problems of this solution are discussed. The research has demonstrated that using biocomponents in the mixtures significantly alters the course of the distillation curve and increases the fuel’s electrical conductivity. Another adverse effect observed was a significant deterioration in the fuel’s low-temperature properties. The research indicates that using a bio-additive like HVO in F-35 fuel will require addressing many challenges. At the level of laboratory tests, it is to determine the limit value of the share of a biocomponent in a mixture at which the normative values are not yet violated and to confirm or rule out whether the mixtures are suitable for long-term storage. [ABSTRACT FROM AUTHOR]

The operational well-being of aircraft-engine turbine components is paramount for engine safety. Monitoring exhaust gas temperature (EGT) serves as a key indicator of their condition. Real-time and precise EGT prediction in aircraft engines plays a pivotal role in ensuring flight safety and effective engine health management. A deep learning model based on the long short-term memory (LSTM), integrated with the physical topology of the aircraft engine, served as the basic prediction model for EGT. Based on Taylor expansion, error compensation was performed on model errors arising from sensor error and performance degradation, three compensation models were developed: a Base model consistent with the basic prediction model, an LSTM model, and a multilayer perceptron model. Their influence on prediction accuracy was examined. Each compensation model was trained using two distinct fusion methods: a global compensation method (GCM) and a real-time compensation method (RtCM). The research also explored how different fusion methods contributed to enhancing prediction accuracy. The results showed that the basic model used in this study achieved high prediction precision. The addition of the compensation model further improved the prediction precision. The GCM effectively reduced the mean absolute relative error (MARE), while the RtCM effectively reduced the maximum absolute relative error (Emax) without increasing the prediction time. The best model evaluated on the four engine test datasets had a MARE value of 0.166%, Emax value of 2.745% and mean absolute error of 1. 13 ° C , indicating high prediction precision. [ABSTRACT FROM AUTHOR]

Main shaft bearings determine aircraft engine's reliability and performance benchmark. Corrosion induced by salt water contamination in lubricant oil (SWCLO) is a big threat to bearing especially in coastal aircraft's engine. The recent progress in harm, purification and monitoring of SWCLO mixture contrasts the inadequate understanding about the critical process how droplet in SWCLO mixture is settled and distributed on bearing steel surface before corrosion occurrence. In present work, the complete processes towards reaching equilibrium states of single and multiple droplets were investigated. Hydrodynamic pressure involved model well predicts the overall falling of droplet over several millimeters. Smaller droplet has longer resting while faster spreading processes. Statistically, smaller droplet takes more time to be deposited accompanied by less probability, further smaller droplet keeps resting or drifting. There is a critical droplet size for deposition. Spreading induced coalescing causes droplet reshaping or de-wetting depending on causal and sessile droplet sizes. De-wetting indicates weakened corrosion. Spatial distribution of eventual sessile droplets is determined by deposition and coalescence at early stage of standing. These results are crucial for understanding, predicting and preventing aircraft engine's bearing corrosion from SWCLO mixture. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Propose a Cryogenic Minimum Quantity Lubrication and Ultrasonic Rolling Strengthening hybrid process for blade. • Reveal the formation mechanism of grain refinement on the TC4 blade surface. • Form a 2.1 mm nanocrystalline layer and a 3.7 mm reinforced layer on the blade surface. This study proposes a hybrid process method based on Cryogenic Minimum Quantity Lubrication and Ultrasonic Rolling Strengthening Process (CMQL-URSP) to improve the surface integrity and enhance the mechanical properties of blade surface. First, the principle of the CMQL-URSP hybrid process was described. Second, the TEM experiments were conducted to clarify the influence of the CMQL-URSP hybrid process on aircraft engine blades. Finally, the blade processing experiments were performed to demonstrate the effectiveness of the CMQL-URSP hybrid process on the blade surface micro-morphology, micro-hardness and residual stress. The results indicate that the CMQL-URSP hybrid process effectively overcomes the limitations inherent to the solitary application of URSP. The CMQL process inhibits work hardening, thereby allowing the URSP to achieve better low-plastic surface improvement. Grain refinement governed by the continuous dynamic recrystallization mechanism leads to the formation of nanocrystals, which significantly improves mechanical properties of blade surface. The CMQL-URSP hybrid process results in a reduction in surface roughness from Ra 2.644 μm to Ra 0.055 μm, an increase in surface residual compressive stress from 321 MPa to 657 MPa, and the formation of a reinforced layer with a depth of 3.7 mm on the blade, thereby enhancing the surface integrity of the blade. [ABSTRACT FROM AUTHOR]

• An integrated airframe-propulsion model is developed to simulate internal/external flow. • Effects of aerodynamic and geometric parameters on combustion and performance. • Underlying mechanisms lie in variations of combustion efficiency and total pressure. • Comprehensive understanding of the thrust-drag trade-off in engine and aircraft. The oblique detonation engine (ODE)-based aircraft employs the fast oblique detonation wave (ODW) combustion for airbreathing propulsion, delivering significant technical advantages in structure and efficiency under hypersonic flight conditions. However, achieving overall optimization by harmoniously integrating the ODE internal flows with the aircraft external flows remains a crucial yet unexplored aspect. Utilizing a multi-species reactive Reynolds-Averaged Navier-Stokes (RANS) model along with a detailed hydrogen-air combustion mechanism, an integrated airframe-propulsion ODE aircraft model is developed to investigate the internal/external flow and thrust-drag characteristics of an ODE aircraft operating at a flight Mach number of 10. Simulation results demonstrate the two key aerodynamic and geometric design parameters, i.e., inlet exit Mach numbers Ma 1 and combustor wedge angles θ 1 , exhibit inverted U-shaped curve effects on ODE specific impulse I sp. The optimal configurations with Ma 1 = 5 and θ 1 = 20° are identified as the most effective balance for ODE performance, achieving the maximum I sp of about 1200s. The underlying mechanisms are elucidated from the viewpoints of variations in both combustion efficiency and total pressure. Furthermore, a slightly different inlet exit Mach number of Ma 1 = 5.5 is found to achieve the maximum flight efficiency by reducing the flight drag, indicting the thrust-drag trade-off in ODE aircraft optimal design. This study provides a comprehensive understanding of the significance of balancing propulsion and aerodynamic performance for ODE aircraft to achieve superior overall performance, which holds the key to future engineering applications. [ABSTRACT FROM AUTHOR]

The aim of this research is to analyze the flow generated by the interaction between a simplified linear blade cascade adapted from an industrial aircraft engine demonstrator and an ingested vortex with the aerodynamic characteristics of a ground vortex observed in experiments. Due to the lack of availability of experimental and high-fidelity numerical studies of this phenomenon in the literature, a representative academic test case with a high-fidelity hybrid RANS/LES resolution of turbulence, namely the Zonal Detached Eddy Simulation (ZDES) technique, is performed. After the description of the computational setup and the numerical parameters used, the instantaneous topology of this complex flow is discussed, especially the interaction of the compressor cascade with the dynamics of the vortex. The simulation shows the chopping of the vortex, its destabilization and its dislocation into turbulent structures downstream the blade cascade. Turbulence is finely resolved in the blade wake and in the chopped vortex by ZDES technique at a moderate computational cost thanks to the RANS treatment of attached boundary layers with an applicative value of Reynolds number (5 ⋅ 10 6). A URANS simulation is also performed and allows a good representation of the averaged vortical structures inside the vortex close to the phase-averaged reference simulation. Spectral analyses are performed on unsteady velocity signals from probes located inside and outside the vortex chopped by the blades, highlighting more intense axial velocity fluctuations from turbulent structures inside the transformed vortex than in the wake of the blades, these structures being convected downstream in the direction of the Outlet Guide Vane (OGV). • Problem inspired by the real-life vortex ingestion by an aircraft turbofan engine. • Simplified test case of interaction between a linear blade cascade and a vortex. • High-fidelity simulation of the problem by the ZDES hybrid RANS/LES approach. • Turbulence finely resolved in the transformed vortex at applicative Reynolds number. • Circulation, enstrophy and spectral analysis of the turbulent transformed vortex. [ABSTRACT FROM AUTHOR]

The robust guaranteed cost composite anti-bump L2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L_2$$\end{document}-gain control for switched systems is investigated in this paper. By constructing a convex combination of positive definite matrices and segmenting the switching interval, a novel multiple convex Lyapunov function suitable for composite anti-bump switching (ABS) control problem is proposed. A new definition of composite ABS performance is presented without reference signals, which restrains the control and rate bumps of the system caused by switchings only at switching instants. Then, imposing a dwell time on the state-dependent switching law, a mixed state-dependent switching scheme is presented based on the multiple convex Lyapunov function. Robust guaranteed cost composite anti-bump control strategy is established for a class of switched systems to overcome the conflicts among guaranteed cost performance, composite bumpless switching performance and L2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L_2$$\end{document}-gain performance. Eventually, an actual aircraft engine model is employed to demonstrate the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction is of great significance for maintaining the reliability and safety of industrial equipment. To address the challenges faced by existing methods in simultaneously extracting local and global degradation information from monitoring data. This paper proposes a Two-Stream Convolution Augmented Transformer (TACT) model based on L 2 regularization constraint. Specifically, we design the parallel multi-scale Convolution Neural Network (CNN) and Transformer module to combine the local modeling ability of CNN and the global modeling ability of Transformer to improve the overall architecture of RUL prediction model. Moreover, the two-stream network based on the parallel structure also realizes the synchronous extraction of different time steps and sensor features in the sequence. Then, in the process of model training, the prediction reliability constraint is fused, the delay prediction constraint term is introduced, and the L 2 regularization loss function is constructed. Finally, extensive experiments on the commercial modular aero-propulsion system simulation (C-MAPSS) show that our model provides competitive performance in terms of Root-Mean-Square Error (RMSE) and Score metrics. Compared to the state-of-the-art method based on Recurrent Neural Network (RNN) or CNN and its variants, Score is reduced by at least 2.71% and RMSE by at least 3.13%. Compared to the Transformer-based improved method, the Score is decreased by at least 4.54% and the RMSE is decreased by at least 2.78%. The effectiveness of the proposed method is demonstrated. • A hybrid end-to-end RUL prediction network based on a tandem combination of CNN and Transformer is proposed. • A two-stream structure is used to extract spatial and temporal features simultaneously. • The MSE loss function incorporating L 2 regularization constraint is proposed. [ABSTRACT FROM AUTHOR]

With uncrewed aircraft systems (UASs) becoming more popular in recent years there is an increasing risk of a midair collision with aircraft for which there are currently no regulations in place. Bird impacts have been thoroughly studied for years and are considered soft body impacts. The bird impact research cannot be directly applied to UAS ingestions since they are hard body impacts. Some recent research has looked into understanding the differences between soft and hard body impacts on aircraft engines. However, this research used primarily unvalidated models for the harsh conditions of an ingestion event, which creates uncertainty in the results. This work intends to fill in this gap in understanding by developing a validated computational model of a UAS for engine ingestion simulations. Part I of this work presented the experimental test plan and experimental results of UAS critical components and full UAS being launched at test articles with representative features of a fan blade. This work discusses the computational modeling completed in LS-DYNA to simulate the physical tests and to update and validate the UAS models. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction is of great significance for maintaining the reliability and safety of industrial equipment. To address the challenges faced by existing methods in simultaneously extracting local and global degradation information from monitoring data. This paper proposes a Two-Stream Convolution Augmented Transformer (TACT) model based on L 2 regularization constraint. Specifically, we design the parallel multi-scale Convolution Neural Network (CNN) and Transformer module to combine the local modeling ability of CNN and the global modeling ability of Transformer to improve the overall architecture of RUL prediction model. Moreover, the two-stream network based on the parallel structure also realizes the synchronous extraction of different time steps and sensor features in the sequence. Then, in the process of model training, the prediction reliability constraint is fused, the delay prediction constraint term is introduced, and the L 2 regularization loss function is constructed. Finally, extensive experiments on the commercial modular aero-propulsion system simulation (C-MAPSS) show that our model provides competitive performance in terms of Root-Mean-Square Error (RMSE) and Score metrics. Compared to the state-of-the-art method based on Recurrent Neural Network (RNN) or CNN and its variants, Score is reduced by at least 2.71% and RMSE by at least 3.13%. Compared to the Transformer-based improved method, the Score is decreased by at least 4.54% and the RMSE is decreased by at least 2.78%. The effectiveness of the proposed method is demonstrated. • A hybrid end-to-end RUL prediction network based on a tandem combination of CNN and Transformer is proposed. • A two-stream structure is used to extract spatial and temporal features simultaneously. • The MSE loss function incorporating L 2 regularization constraint is proposed. [ABSTRACT FROM AUTHOR]

• aircraft engine remaining useful life prediction method with the real engine flight data • multilayered deep convolutional neural network architecture and a long short-term memory network with regression output • comparison of the various deep neural network training options • analysis of the real turbofan engine operational data as an input data for the deep neural network, in order to train, validate and test machine learning algorithms in predicting engine remaining useful life • case studies for method verification based on the real-life engine operational data not the simulated one Aircraft Engine Remaining Useful Life is a key factor which strongly affects flight operations safety and flight operators business decisions. In the article authors decided to present the concept of engine remaining useful life prediction. Proposed method was created as a result of the analysis of the real turbofan engine operational data collected for a few years which was used as an input data for the deep neural network, in order to train, validate and test machine learning algorithms. Two architectures of deep neural networks were created: multilayered deep convolutional neural networks and a long short-term memory network with regression output. Both neural networks were trained, validated and tested on the same engine data and with a various network training options. Results were compared with the neural network performance metrics and figures presenting network prediction convergence. To present how the real-life engine dataset differs the results from the simulated data, both datasets were validated on the same neural network architectures. The main purpose of this article was to present the idea and method of how the artificial neural networks could be used to predict aircraft remaining useful life indicator on the real-life engine operational data not the simulated one. [ABSTRACT FROM AUTHOR]

[Display omitted] • Simple three-step thermomechanical processing (TMP3) for Alloy 718 achieves microstructural homogeneity regardless of billet heterogeneities. • TMP3 provides optimum δ-phase fraction, solute distribution, and dislocation density for subsequent direct ageing. • While the optimised fraction of δ-phase reduces γʹʹ-precipitation overall, the higher dislocation density promotes formation of coarser γʹʹ. • TMP3 promotes optimum sizes and fractions of γ′′ and γ′ Co-precipitates leading to enhanced strengthening and mechanical properties. • TMP3 is scalable for industrial forging of Alloy 718, enabling high-performance turbine disks for next-generation, fuel-efficient aircrafts. Alloy 718 is widely used in critical temperature components of modern aircraft engines and gas turbines. However, its industrial-scale forging faces challenges around heterogeneous microstructures and properties in the final product. This has been attributed to inherent heterogeneous microstructures of the billet starting materials and/or the heterogeneous nature of deformation during hot forging itself, leading to heterogeneities and inferior mechanical performance during service. To overcome these challenges, a three-step TMP approach, denoted simply as TMP3, is introduced to unlock effective microstructure and homogeneity control, irrespective of the given billet microstructure. Using electron and atom probe microscopy, the through-process microstructure evolution is revealed, highlighting dependencies of homogeneity and superior properties on various dynamic recrystallization mechanisms and the δ-phase dissolution. The process affects the dislocation density, δ-phase characteristics, and solute distribution in the matrix γ-phase. This facilitates Nb redistribution, resulting in fractions and morphologies of γʹ and γ" Co-precipitates during subsequent direct ageing. The final samples have a hardness of ∼ 500 HV, a ∼ 5 % improvement over previous methods, providing a reliable proxy for high-temperature yield strength, independent of the billet position. Our TMP3 approach can be scaled-up and will enable manufacturing of high-performance Alloy 718 parts ready for next generation aircraft engines. [ABSTRACT FROM AUTHOR]

The health of the aircraft engines is of great concern. And it is a key task to predict the remaining useful life (RUL) of the aircraft engines accurately. However, there are still challenges in RUL prediction, such as the flaw in incomplete sensor signals acquired, difficulty in determining the importance of sensor signals, and neglect of the key time points with significant performance degradation information in the sensor signals when performing RUL prediction. To tackle these challenges, a dual attention framework named Channel Attention & Temporal Attention based Temporal Convolutional Network (CATA-TCN) is proposed for the RUL prediction of the aircraft engines. Specifically, channel attention is integrated into TCN to focus on sensor signals with critical impact on RUL prediction and suppressing unimportant ones in long-term horizon. Next, the processed sensor signals are fed into temporal attention module, which enhances the impact of the key time points and generates the critical degradation features. Finally, the CATA-TCN outputs the predicted RUL by performing non-linear mapping on the extracted features. Turbofan engine degradation simulation data set (C-MAPSS dataset) and real flight data are used to validate the CATA-TCN framework. The experimental results show that the proposed method is significantly more accurate on overall prediction performance (Score and RMSE) than other state-of-the-art methods, especially under changeable operation conditions and complex fault modes. [ABSTRACT FROM AUTHOR]

In reliability-based topology optimization (RBTO), both quantification and maximization of structure reliability considering uncertainty of various quantities are essential. In this study, A novel gradient online learning and prediction (GoLap) method is proposed to accelerate the double-looped RBTO and reduce total computational time by using a one-hidden-layer neural networks (NN) and gradient and non-gradient based reliability quantification. The total computational time saving is achieved by (i) skipping the finite element analysis (FEA) runs and sensitivity analyses and (ii) updating the design and random variables using the gradients predicted by the NN model in selected inner and outer loops. 2D and 3D numerical examples, including an aircraft engine pylon, are presented to demonstrate the total computational time saving of up to 84.4%, and the prediction accuracy of the reliability of optimum structures for the GoLap implemented RBTO methods. [ABSTRACT FROM AUTHOR]

As a crucial component of aircraft engine combustors, the external-mixing prefilming atomizer exhibits significant advantages in enhancing combustion efficiency and reducing pollutant emissions. However, the intricate relationship between spray characteristics, droplet breakup mechanisms, flow fields, and geometric parameters of the external-mixing prefilming atomizer remains poorly understood. This paper presents a novel external-mixing prefilming atomizer, aiming to address this knowledge gap. Additionally, particle image velocimetry (PIV), high-speed Schlieren photography, and particle image analysis (PIA) measuring systems were employed to gain insights into the spray characteristics and droplet breakup mechanism of the external-mixing prefilming atomizer under various swirl flow fields. The obtained results reveal the formation of two distinct regions, namely a low-density area and a high-density area. Notably, the number of droplets in the low-density area, located within 0–2 cm from the central axis, is significantly lower compared to the high-density area situated further away from the central axis. The breakup of droplets in the low-density region is primarily attributed to the inner swirl airflow, while the outer swirl airflow induces droplet breakup in the high-density region. As the Reynolds number of the incoming flow increases, the air-liquid ratio (ALR) and Weber number also increase, thereby enhancing the fuel breakup effect. The Schlieren results reveal a two-stage spray development process. The initial stage is characterized by the dominant momentum of the dense spray, with the spray tip penetration (STP) exhibiting a relatively slow increase. The aerodynamic force of airflow plays a crucial role in governing the breakup and transport of droplets, leading to a linear variation of STP over time. As the swirl number increases, there is an enhanced momentum exchange between droplets and airflow, resulting in accelerated spray penetration and droplet breakup. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction of aircraft engines is significant in the health monitoring, operation, and maintenance of aircraft. Capturing more comprehensive device degradation trends at different time scales and extracting long-term dependencies effectively among elements in long time series are two challenges in the field of aircraft engine RUL estimation. To address the aforementioned challenges, this paper proposes a novel multiscale Hourglass-Transformer (MHT) aircraft engine RUL prognostics. Specifically, an hourglass-shaped multiscale feature extractor (HME) is designed based on one-dimensional convolutional neural network, which can scale the time sequence into multi-time scales for feature fusion. Then, a transformer network is employed to further extract features from the fused feature map and output the RUL. To enhance inter-scale data attention, a pyramid self-attention mechanism is employed in both the encoder and decoder. Finally, the superiority and effectiveness of this approach are verified on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Furthermore, the robustness and generalization capability of this method are further validated on New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset. • A novel multiscale Hourglass-Transformer is developed for aircraft engine RUL prognostics. • An hourglass-shaped multiscale feature extractor is designed based on one-dimensional convolutional neural network. • A transformer network is employed to further extract features. • Pyramid self-attention mechanism is adopted to reduce space complexity. [ABSTRACT FROM AUTHOR]

The Prognostics Health Management (PHM) of modern equipment typically employs Remaining Useful Life (RUL) prediction to assess health status. Existing mainstream RUL prediction methods have improved prediction performance by adopting data-driven approaches based on deep learning. However, most methods overlook the impact of noise interference caused by the redundancy of sensor data across channels, leading to a decrease in RUL prediction performance. Furthermore, these methods lack transparency and interpretability, which are crucial for maintenance personnel in accurately diagnosing the degradation process of equipment RUL. Therefore, we propose a Factorized temporal-channel fusion and Feature fusion based Variational Encoding (FFVE) for interpretable RUL prediction. By utilizing the factorization operation, we construct the Factorized Temporal-Channel Fusion (FTCF) block to learn temporal and channel dependencies, thereby reducing redundancy between channels. Through the feature fusion operations which mix original information with extracted feature information, we augment the original information that is lost during the deep network learning process, consequently avoiding performance degradation caused by increasing network depth. Through the above encoding process, sensor data is effectively compressed into a 3D latent space for predicting and interpreting the equipment degradation process. Extensive experiments were conducted on two datasets of the C-MAPSS turbofan aircraft engine and the NASA lithium-ion battery. The results demonstrate that our method outperforms state-of-the-art methods in terms of prediction accuracy. Additionally, our study provides a novel visual means of assessing turbofan engine health status, elucidating the engine RUL degradation process learned by our method. [ABSTRACT FROM AUTHOR]

The strength of wet granular ice layers is an important quantity for modelling the behaviour of ice accretions encountered in aircraft engine icing since it determines the dynamics of the collision of ice crystals, their penetration into the layer, breakup and the characteristics of the ejected material. In the present study, impact tests of 3.18 mm nylon spheres are conducted on wet granular ice layers for a dynamic strength characterization. An experimental setup and methodology are designed to measure the crater depth for impact velocities ranging from 5 ms − 1 to 90 ms − 1. Experiments are conducted using both ice layers generated by ice crystal accretion on a solid substrate inside an icing wind tunnel and artificial ice layers prepared in a laboratory environment. By fitting the Poncelet penetration equation to the experimental data, two dimensionless parameters are derived that characterize the strength of the investigated ice layers in relation to the layer inertial resistance and apparent yield strength. A key finding is that strong connections between individual ice particles exist for ice layer generated in the wind tunnel, originating from re-freezing of liquid water near the particle contacts. Only by re-enforcing the ice particle connections in ice layers prepared in the laboratory, comparable strength values are obtained. The presented methodology enables measurement of ice layer strength and comparison at different facilities as well as conducting advanced experiments with realistic ice layers in a laboratory environment. • Ice layer impact experiments conducted in icing wind tunnel and icing laboratory • Deeper impact craters in ice layers generated at positive wet-bulb temperatures • Connection strength between ice crystals significantly affects ice layer strength • Representative granular ice layers can be replicated in a laboratory environment [ABSTRACT FROM AUTHOR]