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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]

Process capability indices (PCIs) are major tools in geometric dimensioning and tolerancing (GD&T) for quantifying the production quality, monitoring production, or prioritizing projects. Initially, PCIs were constructed for studying each characteristic of the process independently. Then, they have been extended to analyze several dependent characteristics simultaneously. Nowadays, with the increasing complexity of the production parts, for example, in aircraft engines, the conformity of one part may rely on the conformity of hundreds of characteristics. Also, because those characteristics are dependent, it may be misleading to make decisions based only on univariate PCIs. However, classical multivariate PCIs in the literature do not allow treating such amount of data efficiently, unless assuming Gaussian distribution, which may be false. Regarding those issues, we advocate for PCI based on some transformation of the conformity rates. This presents the advantage of being free from distributional assumptions, such as the Gaussian distribution. In addition, it has direct interpretation, allowing it to compare different processes. To estimate the PCIs of parts with hundreds of characteristics, we propose to use Vine Copulas. This is a very flexible class of models, which gives precise estimation even in high dimension. From an industrial perspective, the computation of the estimator can be costly. To answer this point, one explains how to compute a lower bound of the proposed PCI, which is faster to calculate. We illustrate our method adaptability with simulations under Gaussian and non-Gaussian distributions. We apply it to compare the production of fan blades of two different factories. [ABSTRACT FROM AUTHOR]

With the rapid development of sensor and communication technology, multi-sensor data is available to monitor the degradation of complex systems and predict the failure modes. However, two huge challenges remain to be resolved: (i) how to predict the failure modes with limited failure mode labeled systems to alleviate the heavy dependence on expert experience; (ii) how to effectively fuze the useful information from the multi-sensor data to achieve an accurate estimation of the degradation status automatically. To address these issues, we propose a novel semi-supervised prognostic model for the systems with partially observable failure modes, where only a small fraction of the systems in the training set are known for their failure modes. First, we develop a graph-based semi-supervised learning method to extract features characterizing the failure modes. Then, we input these features as well as the multi-sensor streams into an elastic net functional regression model to predict the residual useful lifetime. The proposed model is validated by extensive simulation studies and a case study of aircraft turbofan engines available from the NASA repository. [ABSTRACT FROM AUTHOR]

In aircraft engines applications, the interactions between rare earth silicates (mainly yttrium silicates) used as Environmental Barrier Coatings (EBCs) on turbine blades and liquid calcium-magnesium-aluminosilicates (CMAS) due to the particles ingested by the engines may lead to severe damages at high temperature. Due to the large range of composition of CMAS, simplified model systems must be used to have a good comprehension of the thermochemical phenomena leading to degradation. The interaction between ceramics and CMAS can lead to the precipitation of phases as apatite Ca 2 Y 8 (SiO 4) 6 O 2 and cyclosilicate Ca 3 Y 2 (Si 3 O 9) 2 which might provide protection to the EBC. However, their thermodynamic stability depends on many parameters. In the present work, the influence of alumina Al 2 O 3 addition to the ternary system CaO-SiO 2 -YO 1.5 has been studied in order to verify the equilibrium with the liquid field at 1300 °C. [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]

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]

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]

Wear is progressive deterioration of machine components by the loss of weight and shape results in generation of wear debris. For the analysis of these wear debris, ferrography method is widely used. This method was successfully used to examine the condition monitoring of martial aircraft engines, gearboxes, bearings, heavy earth moving machineries, turbines etc. Ferrographic condition monitoring as a preventive maintenance done by collecting oil sample. Ferrogram's images taken under a microscope for monitoring wear particle contamination of these oil samples. Both qualitative and quantitative analysis of the wear particles was done from these image analyses. Novelty of this research work is use of hot ferrography for getting detailed information about machine health condition. A detailed Excel sheet containing information about the size distribution of wear particles in terms of count, area, perimeter, aspect ratio, and mean diameter was created using data from the Olympus microscope software and a ferrogram. From this data, wear mode, its source, mechanism can be easily detected. [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]

The chemical composition, microstructure and properties of races of a foreign aircraft engine bearing are studied. The material and the heat treatment method are determined, the microstructure of the diffusion layer is analyzed, the thickness of the diffusion and effective layers is calculated, and curves of the distribution of hardness and residual stresses over the diffusion layer depth are plotted. The data obtained can be used for import substitution of heat-resistant bearings for aerospace applications. [ABSTRACT FROM AUTHOR]

Planned maintenance is required by licensed maintenance organizations to detect and prevent performance degradation in aircraft engines. In the literature, engine performance is evaluated with parameters that show engine performance. Fuel flow parameter is one of the important parameters that shows engine performance. In the models developed earlier, no engine performance evaluation was made with the fuel flow parameter at all stages from the take-off to the landing of the aircraft. In this study, fuel flow parameter is estimated with over 99.9% accuracy by using artificial neural network in MATLAB® software. In order to detect the engine performance deterioration of the aircraft, the fuel flow values obtained from the artificial neural network and confidence intervals with 99% confidence level were established. Each value taken from the fuel flow sensor is evaluated by the model in all flight phases. In the model, engine performance is considered normal if the fuel flow value is within the confidence interval, and abnormal (anomaly) if it is outside the confidence interval. An accuracy of over 99.9% was achieved and results of this study showed that fuel flow rate of the engine of interest was within the confidence interval (no performance deterioration). [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]

Purpose: This paper aims to investigate the effect of frictional heat on the wear of high-speed rotary lip seals in engines. Design/methodology/approach: In this research paper, the authors focus on the high-speed rotating lip seal of aircraft engines. Using the hybrid lubrication theory, a thermal-fluid-solid coupled numerical simulation model is established to investigate the influence of parameters such as contact pressure distribution, temperature rise and leakage rate on the sealing performance under different operating conditions. By incorporating the Rhee wear theory and combining simulation results with experimental data, a method for predicting the wear of the rotating seal lip profile is proposed. Experimental validation is conducted using a high-speed rotating test rig. Findings: The results indicate that as the speed increases, the rise in frictional heat leads to a decrease in the sealing performance of the lip seal contact region. The experimental results show a similar trend to the numerical simulation results, and considering the effect of frictional heat, the predicted wear of the lip seal profile aligns more closely with the actual wear curve. This highlights the importance of considering the influence of frictional heat in the analysis of rotating seal mechanisms. Originality/value: This study provides a reference for the prediction of wear profiles of engine high-speed rotary lip seals. [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]

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]

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]

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]

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]

As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge computing resources are exploited to process more of the data locally. This regime of analytics is coined as Federated Data Analytics (FDA). Despite the recent success stories of FDA, most literature focuses exclusively on deep neural networks. In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression. Our treatment is built upon hierarchical modeling that allows borrowing strength across multiple groups. To this end, we propose two federated hierarchical model structures that provide a shared representation across devices to facilitate information sharing. Notably, our proposed frameworks are capable of providing uncertainty quantification, variable selection, hypothesis testing, and fast adaptation to new unseen data. We validate our methods on a range of real-life applications, including condition monitoring for aircraft engines. The results show that our FDA treatment for linear models can serve as a competing benchmark model for the future development of federated algorithms. [ABSTRACT FROM AUTHOR]

Aviation activities have jeopardized the limited energy sources and thereby environmental sustainability due to fossil fuel consumption. Therefore, aircraft engines being the significant emission source have attracted the attention of engineers toward improvement of the design process. In this study, turbojet engine (TE) and its components were examined with exergy, environmental, and sustainability metrics. Then, the effects of several design variables such as turbine inlet temperature (TIT) ranging between 850 K and 1100 K and compressor pressure ratio (CPR) ranging between 3 and 4 on exergetic and environmental parameters were investigated. As a novelty, a new index named specific irreversibility production (SIP) was improved. According to exergy assessment, the wasted exergy ratio (WExR) of the combustor was found as highest changing between 42.46% and 51.89%. Moreover, exergy efficiency of the turbojet engine was determined between 16.42% and 21.92%. According to environmental assessments, the ecological effect factor (EcoEF) of the TE was computed between 4.77 and 6.08 under ranges of different design variables. Finally, the SIP of turbojet was gauged between 0.488 MW/kN and 0.586 MW/kN. Considering these results, the TIT and CPR have high effect on the optimum engine performance and environmental parameters. [ABSTRACT FROM AUTHOR]

Purpose: The aviation industry has seen consistent growth over the past few decades. To maintain its sustainability and competitiveness, it is important to have a comprehensive understanding of the environmental impacts across the entire life cycle of the industry, including materials, processes and resources; manufacturing and production; lifetime services; reuse; end-of-life; and recycling. One important component of aircraft engines, integral rotors known as Blisks, are made of high-value metallic alloys that require complex and resource-intensive manufacturing processes. The purpose of this paper is to assess the ecological and economical impacts generated through Blisk production and thereby identify significant 'hot-spots'. Design/methodology/approach: This paper focuses on the methodology and approach for conducting a full-scale Blisk life cycle assessment (LCA) based on ISO 14040/44. Unlike previous papers in the European Aerospace Science Network series, which focused on the first two stages of LCA, this publication delves into the "life cycle impact assessment" and "interpretation" stages, providing an overview of the life cycle inventory modeling, impact category selection and presenting preliminary LCA results for the Blisk manufacturing process chain. Findings: The result shows that the milled titanium Blisk has a lower CO2 footprint than the milled nickel Blisk, which is less than half of the global warming potential (GWP) of the milled nickel Blisk. A main contributor to GWP arises from raw material production. However, no recycling scenarios were included in the analysis, which will be the topic of further investigations. Originality/value: The originality of this work lies in the detailed ecological assessment of the manufacturing for complex engine components and the derivation of hot spots as well as potential improvements in terms of eco-footprint reduction throughout the products cradle-to-gate cycle. The LCA results serve as a basis for future approaches of process chain optimisation, use of "greener" materials and individual process improvements. [ABSTRACT FROM AUTHOR]

The importance of saving energy and materials by lightening structures is constantly increasing. With its powerful software capabilities, Topology Optimization produces solutions for this exact purpose. In addition, thanks to Topology Optimization, more innovative and competitive structures can be produced. The development of additive manufacturing methods has also increased interest in Topology Optimization. In Topology Optimization, volumetric elements that do not carry any load or carry little load are removed from the structure. Thus, lighter, but sufficiently durable structures can be obtained. In this study, the topology optimization of a bracket used as a fastener in a jet engine was carried out using ABAQUS Finite Element software. Required bracket geometry, load conditions, and material information were obtained from an online design competition announced by General Electric. Ti6Al4V alloy was used as the bracket material. At the beginning of the study, static analysis was performed on the original bracket model to obtain the load paths required for topology optimization. As a result of the static analysis, the load paths within the jet engine bracket were determined and topology optimization was applied to the bracket to minimize the mass without reducing the rigidity. As a result of the analysis studies, it has been proven that nearly 80% material savings can be achieved from the bracket thanks to topology optimization. [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]

The performance requirements for extreme pressure and anti-wear properties, as well as oxidative stability, are increasingly stringent for MIL-PRF-23699G Enhanced Ester (EE) for aviation turbine engine lubricants. An oligomerization reaction of pentaerythritol through direct esterification using pentaerythritol and dibasic acid catalyzed by tetrabutyl titanate in xylene solvent, was employed to achieve the low-polymerization reaction of dibasic acid and pentaerythritol. A two-step synthesis process yielded a mixed ester comprising dibasic acid pentaerythritol complex ester and pentaerythritol tetrapentanoate. These mixed esters demonstrate excellent viscosity-temperature performance and good oxidative stability, significantly enhancing the extreme load-carrying properties and anti-wear properties of pentaerythritol tetrapentanoate. Molecular dynamics (MD) calculations indicate that complex esters possess higher adsorption energy and molecular interaction energy, enhancing the possibility of ester molecules adsorbing on metal surfaces and producing stronger oil film that can withstand large loads. The novel 5 mm2/s pentaerythritol ester containing diacid pentaerythritol complex esters exhibits improved viscosity-temperature performance, high-temperature oxidation resistance, and better tribological properties. Therefore, the novel pentaerythritol esters show potential for application in 5 mm2/s EE aircraft engine lubricants. [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]

It is usually assumed that faulty bearings produce second-order cyclostationary (CS2) signals, and thus the natural process for their diagnostic analysis involves first the removal of first-order cyclostationary (CS1) components, such as from gears, followed by amplitude demodulation of an 'informative' frequency band, and subsequent envelope analysis, in which the spectrum of the (squared) envelope is inspected for signs of a fault, typically manifesting as discrete frequency components at or near one of the expected fault frequencies, the latter calculated from basic kinematic relationships. However, recent theoretical research, supported by empirical evidence from the aero industry, has shown that bearing signals are in fact not purely CS2, but rather exhibit both CS1 and CS2 properties, dominant in the lower and higher frequency ranges, respectively, with the 'crossover' between these ranges proportional to machine speed. Thus, for high-speed bearings, where the frequency range available for analysis may span only a handful of fault frequency harmonics, the relative importance of CS1 signal content is far greater than for lower speed applications, and it can no longer be assumed that the envelope spectrum is the optimal diagnostic tool. These and other issues associated with high-speed bearing diagnostics are discussed in this paper, using as an example the aircraft engine data provided by Safran for the diagnostic contest at the 2015 Surveillance 8 Conference in Roanne, France. The objective of the paper is to guide the reader in the analysis of high-speed bearing signals, pointing out in particular the ways in which the required analysis techniques differ from the 'conventional wisdom' of the diagnostics field, most of which is based on more common, lower speed applications. [ABSTRACT FROM AUTHOR]

The structural components prone to erosion damage often exhibit irregular and complex shapes. Predicting erosion rates for such irregular geometries is a field that needs more attention, since it can be used to evaluate the performance of the component. This study aims to develop an evaluation method for predicting erosion rates of components with complex geometry. Standard erosion tests were performed on small plates to investigate the effects of different impact angles and particle velocities on the erosion rate of the target material. Based on these test results, the Newton iteration method was employed to determine the parameters of the Tabakoff and Grant erosion model. Moreover, these parameters were used in computational fluid dynamics simulation of components with complex geometries. Besides, a model was developed to convert the erosion rate density into erosion rate in numerical simulation, allowing quantitative comparison of simulation results with experimental data. The calibration of the erosion model parameters was conducted by using 1060 aluminum alloy specimens. The predicted erosion rate changes with impact angle showed consistency good agreement with the testing data. Subsequently, the erosion rate of a simplified similar structure for aircraft engine blade made of 1060 aluminum alloy was predicted and verified. • Determined optimal Tabakoff and Grant erosion model parameters for 1060Al by standard erosion tests. • Quantitative evaluation of erosion rates for components with complex geometry. • A model for conversion of erosion rate density to erosion rate was proposed. • Effectively predicted erosion rates for a simplified structure of aircraft engine blade. [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]

• 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]

Robotic machining systems have been widely implemented in the assembly sites of large components of aircraft, such as wings, aircraft engine rooms, and wing boxes. Milling is the first step in aircraft assembly. It is considered one of the most significant processes because the quality of the subsequent drilling, broaching, and riveting steps depend strongly on the milling accuracy. However, the chatter phenomenon may occur during the milling process because of the low rigidity of the components of the robotic milling system (i.e., robots, shape-preserving holders, and rod parts). This may result in milling failure or even fracture of the robotic milling system. This paper presents a feasible spindle speed interval identification method for large aeronautical component milling systems to eliminate the chatter phenomenon. It is based on the chatter stability model and the analysis results of natural frequency and harmonic response. Firstly, the natural frequencies and harmonics of the main components of the robot milling system are analyzed, and the spindle speed that the milling system needs to avoid is obtained. Then, a flutter stability model considering the instantaneous cutting thickness is established, from which the critical cutting depth corresponding to the spindle speed can be obtained. Finally, the spindle speed interval of the robotic milling system could be optimized based on the results obtained from the chatter stability model and the analysis result of the natural frequency and harmonic response of the milling system. The effectiveness of the proposed spindle speed interval identification method is validated through time-domain simulation and experimental results of the large aeronautical component milling system. [ABSTRACT FROM AUTHOR]

Haynes 188 alloy with a combination of strength and ductility is of significance in aircraft engines and recent development in additive manufacturing significantly increases potential applications of this material. However, understanding the microstructure and tensile properties of Laser Powder Bed Fusion (LPBF) Haynes 188 alloy is limited. Here, a crack-free Haynes 188 alloy is successfully fabricated. The LPBF specimens are observed in two different directions: perpendicular and parallel to the building direction, referred to as "LPBF-H" and "LPBF-V" sections, respectively. To investigate the anisotropy of LPBF specimens, tensile tests were conducted on LPBF Haynes 188 alloy with different observed sections. The vertical sections show lower yield strength than the horizontal sections due largely to an elongated columnar structure. The effects of solute, grain size and dislocation on the strengthening mechanism are discussed thoroughly. The synergy effects of the grain size and the dislocation increase the strength of the LPBF alloys compared to wrought and casting equivalents. For LPBF specimens, the dislocation cellular structure is particularly investigated. To better understand the plasticity, the fracture morphology is also analyzed. The developed understanding on the microstructure and mechanical properties of Haynes 188 alloys in this work could also be applicable to other Co-based superalloys. • A Haynes 188 alloy with superior mechanical properties is successfully fabricated using the LPBF process. • LPBF Haynes 188 alloys show better mechanical strength compared to wrought and cast specimens. • The strengthening mechanism of LPBF Haynes 188 is disscussed thoroughly. [ABSTRACT FROM AUTHOR]

• Demonstrates payload range capability of a modern aircraft retrofitted with Ammonia. • Ammonia enables smaller and compact turbofan cores with engine weight reduction. • Ammonia exists as subcooled liquid under the assessed engine operating points. • Fuel conditioning is a major design driver for Ammonia operated turbofan engines. • Impacts of Ammonia as a Hydrogen carrier for civil aviation on engine and aircraft. The 2050 net zero targets for aviation to decarbonize the industry means that solutions need to be delivered that can help achieve those targets. Transitioning to zero carbon aviation fuel is an effective solution to achieve those targets. This research article aims to highlight the potential design and performance implications of using Ammonia as a zero-carbon fuel for civil aviation through a retrofit case study conducted for an Airbus A350-1000 equivalent aircraft. The impacts on both turbofan design and aircraft payload-range capability are presented. A feasibility study of using Ammonia as a Hydrogen carrier for civil aviation is also presented. The turbofan design impacts, and payload range capability are assessed using Cranfield University's in-house gas turbine performance tool TURBOMATCH and NASA FLOPS respectively. A 3-point turbofan cycle design strategy is utilized for redesigning turbofan engine cycles using Ammonia as a fuel. Ammonia fuel conditioning assessment is made using REFPROP to investigate its impact on turbofan design. Utilizing pure Ammonia as an aircraft fuel can provide significant turbofan redesign opportunities. Fuel conditioning assessment revealed that for a 430 kN thrust class engine, 2.1 MW of thermal power is required to condition Ammonia fuel at take-off. As a result, various strategies to condition the fuel and its significant impact on turbofan design are presented indicating fuel conditioning as a major design driver for Ammonia fuelled turbofan engines in the future. Although upon initial preliminary assessment, Ammonia utilized as a Hydrogen carrier showcased potential by providing additional mission range capability when compared to a pure Ammonia burning aircraft, the significant thermal energy required to crack (decompose) Ammonia into Hydrogen highlighted the challenges at aircraft mission level and Hydrogen turbofan design implications. It is found that energy requirement (power) to crack Ammonia into Hydrogen are significant which is approximately an order of magnitude higher than Ammonia fuel conditioning itself. [ABSTRACT FROM AUTHOR]

Under non-stationary condition, estimating the instantaneous frequency (IF) of shaft from vibration signal is an ideal way for gearbox fault monitoring without tachometer. Among them, the method based on harmonic phase demodulation has always occupied a dominant position. In the low signal-to-noise ratio (SNR) condition, the noisy harmonic components may cause greater IF estimation error. In this work, an improved approach for estimating the IF of gearbox reference shaft is proposed based on the short time Fourier transform spectrogram. The theoretical derivation proves that the high-order meshing harmonic can reduce the IF estimation error of gearbox reference shaft. By constructing the spectrogram weight factor, an improved maximum tracking algorithm is proposed to redefine the IF estimation range. On this basis, the robust IF estimation of high-order meshing harmonic with low SNR is realized. Finally, it is equivalent to the estimated IF of gearbox reference shaft. The proposed strategy is compared with the two advanced IF estimation methods. The civil aircraft engine dataset and the wind turbine gearbox dataset provided in the Safran contest demonstrate that the proposed strategy works. In the case of heavy load and large speed change, the proposed method has extremely low consumption time while maintains good estimation accuracy. [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]

Herein, the real-time structural composition, physicochemical properties and oxidation behavior of ester aviation lubricating oil (ALO) in a complete oil change cycle is investigated deeply based on a certain in-service aeroengine. Theoretical calculations and gas chromatograph-mass spectrometer results reveal that the ester group (109.54 kcal mol−1), C–O bond (109.55 kcal mol−1) and C–C bond (123.38 kcal mol−1) between α-C and β-C in 1-decanoyl-3-nonanoyl-propyl-2-dodecanoate (base oil of ester ALO) breaks orderly at high temperature due to their lower bond energy. The further oxidation, cyclization, esterification and polymerization occurs among the formed intermediates and forms coking precursor via unsaturated double ester, unsaturated single ester and ternary ester. Based on these results, a possible oxidation and coking mechanism of ester ALO is proposed. • The physicochemical properties of ester ALO in a complete oil change cycle is investigated. • Oxidation and coking mechanism of ester ALO are proposed based on a certain in-service aeroengine. • Tribological performance of ester ALO with different service time is evaluated. • Composition and property of coking particles are discussed. [ABSTRACT FROM AUTHOR]

This paper reviews several decades' worth of research on the topic of slat noise arising from high-lift systems of commercial aircraft. A high-lift system is commonly used for providing additional lift by deploying the leading-edge slat(s) and trailing-edge flap(s) during the takeoff and landing phases of flight. Slat noise can be one of the main sources of airframe noise, along with the deployed landing gear, and airframe noise can be dominant during the approach phase when aircraft engine noise is reduced. This review synthesizes historical investigations related to the generation/radiation of slat noise, including both experimental and numerical approaches. Characteristics of noise and corresponding generation mechanisms have been well investigated, and numerical tools have been developed to predict noise levels. Scaling laws to map the results from models to real aircraft are proposed based on the combined analytical and experimental investigations. Noise-reduction technologies are also discussed. [ABSTRACT FROM AUTHOR]

• Pylon–rotor interaction noise in low transonic speed of fan is elucidated. • CFD and duct acoustics are used to investigate the generation and propagation. • Periodic shock motion gives a prominent concentrated dipole source on a blade surface. • Advent of extra cut-on modes with low circumferential wave numbers contributes to the noise increase. Experimental, computational fluid dynamics (CFD), and duct-acoustics theoretical studies were conducted to investigate the mechanism of upstream noise propagation from fan stages with and without a downstream cylinder which simulates the presence of an engine pylon or bifurcation. Model fan experimental results obtained using an anechoic test facility show that the fan tone noise level increases sharply when the rotational speed exceeds a critical speed point, which is much lower when a downstream cylinder is present than when there is no cylinder. Distinctive features of the sound source due to pylon–rotor interaction (PRI), i.e. the unsteady aerodynamic blade loading resulting from interaction with the back-pressure distortion, are found by CFD. Characteristic modal structures of PRI noise were made clear by modal analysis of CFD results. The pressure fields upstream of the fan rotor blades and the rotor blade surface pressures obtained from three-dimensional unsteady CFD indicate that shock wave position and strength fluctuations caused by interaction with back-pressure distortion bring about highly peaked unsteady pressure amplitudes at the shock foot on the blade suction surface. The sharp increase in the PRI tone noise level in the low transonic speed range is ascribable to the advent of a large number of cut-on duct acoustic modes with high modal pressure amplitudes, which are absent when no cylinder is present. Comprehensive experimental, CFD, and duct-acoustics theoretical studies indicate that CFD reasonably simulates the experimental sound field. [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]