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This article investigates the issue of anti‐modal‐asynchrony sliding mode control (SMC) for discrete semi‐Markov jump systems with bi‐boundary sojourn time. Asynchronous mechanism refers to the mismatch phenomenon between the system mode and the controller mode, in which the modal lag is mode‐dependent to reduce some conservatism compared with traditional mode‐independent lag. In contrast to traditional semi‐Markov jump systems, the sojourn time of each mode in this article is considered to have upper‐and‐lower bounds, representing better characteristics than the upper bound. Based on the semi‐Markov kernel and the Lyapunov function related to the elapsed time of current mode, a sufficient condition is proposed to ensure that the mean‐square stability of the underlying system is achieved under an equivalent SMC mechanism. The conditions for the existence of the designed controller are obtained, which shows that the constructed SMC mechanism is effective against asynchronous mode jumps. An anti‐modal‐asynchrony SMC mechanism is constructed to achieve the reachability of discrete quasi‐sliding mode. Finally, the F‐404 aircraft engine model is given to validate the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

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]

Aircraft encounters with volcanic ash have caused significant damage over the past 40 years, resulting in particular attention being given to the issue. We analyzed the volcanic ash-aircraft encounter database published by the USGS. We added new volcanic eruptions and parameters such as eruption types, and dry–wet. Then, we applied standard and advanced statistical methods. Over 130 encounters have been documented in the mentioned database, with volcanic ash causing severe abrasions to the windshield, airframe, wings, and engine components. In nine cases, aircraft engines failed. We applied the binary regression analysis and some laboratory melting experiments on volcanic ash. Besides phreatomagmatism, we use the term external water in this work to describe meteoric water that enters volcanic plumes through precipitation or melting ice on ice-capped volcanoes. We demonstrated that engine failure occurs when our regression analyses undergo dry-to-wet conditions. In other words, statistically, there is a positive correlation between wet ash encounters with aircraft and engine failure incidents. Moreover, experiments conducted at 900 °C and under 40 bar pressure showed increased sintering in the dry sample, while melting textures were more prevalent in hydrated samples. We concluded that despite the various eruptive dynamics of volcanic ash, the introduction of external water into the volcanic plumes, probably causing instantaneous hydration of volcanic ash, is a common factor in engine failure incidents. Thus, we have identified the reasons behind engine failures during encounters between aircraft and volcanic ash and the specific damage that can occur depending on the type of eruption involved. [ABSTRACT FROM AUTHOR]

Composite fan blades made of CFRP have been developed and investigated to reduce the weight of aircraft engines. Aircraft fan blades are subjected to high-speed impact by foreign objects, such as bird strikes. Because CFRP has lower impact resistance than metal materials, it is necessary to focus on possible failure not only at the impact point but also at the tip and trailing edge of the fan blade. This paper developed a finite element analysis model of fan blade geometry and investigated the dynamic deformation that may induce peripheral fracture when a bird strike occurs on a composite fan blade. Natural vibration analysis and transient response analysis were performed to analyze the vibration behavior at the fan blade periphery. The natural vibration analysis showed that the vibration modes in the out-of-plane direction of the blade are of low order. In the transient response analysis, when a group of particles defined by the equation of state and the SPH method collided, a sudden deformation in the periphery, called a whipping motion, was observed immediately after the impact. The spanwise strain has a peak value at the trailing edge of the fan blade, while the chordwise strain has a peak value at the leading edge of the fan blade. Furthermore, the transient response analysis with the fan blades rotating showed an increase in the peak strain value. A comparison of the impact loads and displacements immediately below the impact indicated that the centrifugal force increased the geometric stiffness, which increased the reaction force due to the fan blade, increasing the peak value. [ABSTRACT FROM AUTHOR]

The trapped vortex combustor (TVC) has shown promise for use in advanced aircraft engines due to its high combustion efficiency, excellent stability, compact geometry, and low pressure loss. However, the mainstream-forced entrainment phenomenon presents a significant challenge to implementing the TVC. This study conducted experimental and numerical investigations to examine the effect of different cavity-air-injection modes on mainstream-forced entrainment. The results indicate that the entrainment of mainstream air into the cavity is greatly affected by the cavity-air-injection mode, subsequently influencing the combustion performance of the TVC. Compared to the front/rear air-injection mode (case 1) or the front air-injection mode (case 3) of the cavity, the rear air-injection mode (case 2) can effectively reduce the entrainment of the main flow into the cavity, creating a stable large-scale single vortex flow structure. Additionally, the fuel distribution in the cavity is somewhat influenced by the cavity's air-injection design. The local equivalence ratio of the cavity in case 1 is higher than that of cases 2 and 3, which benefits lean ignition and blowout performance. Case 1 demonstrates superior ignition and blowout performance compared to case 2, which exhibits the poorest ignition–blowout performance. Cases 1 and 3 share similar ignition processes, although case 3 has a significantly longer ignition time than case 1. In contrast, case 2 experiences ignition with the flame primarily concentrated in the mainstream area due to its air-injection mode. The cavity's air-injection mode leads to a rapid influx of fuel into the mainstream area, resulting in the lowest combustion efficiency observed in case 2. [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]

Volatile Particulate Matter (vPM) emissions are challenging to measure and quantify, since they are not present in the condensed form at the engine exit plane and they evolve to first form in the aircraft plume and then continue to grow and change as they mix and dilute in the ambient atmosphere. To better understand the issues associated with the initial formation and growth of vPM, a modeling study has been undertaken to examine several key parameters that affect the formation and properties of the vPM that is created in the initial cooling and dilution of the aircraft exhaust. A modeling tool (Aerosol Dynamic Simulation Code, ADSC) that was developed and enhanced over a series of past research projects supported by NASA, DoD's SERDP/ESTCP, and FAA was used to perform a parametric analysis of vPM. The parameters of fuel sulfur content (FSC), emitted condensable hydrocarbon (HC) concentrations, and the species profile of the HCs were used to construct a computational matrix that framed a wide range of expected parameter values. This computational matrix was executed for two representative commercial aircraft engines at ground idle and results were obtained for distances of 250 m and 1000 m downstream. From prior results, the most significant vPM emissions occur at the lowest power settings, so an engine power condition of 7% rated thrust was used. A primary goal of the parametric study is to develop an updated vPM modeling methodology and also to help interpret data collected in experimental campaigns. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. The aim is to provide additional understanding on how the vPM properties vary with fuel and engine parameters to increase the utility of vPM predictions. Implications: Volatile Particulate Matter (vPM) is an important contribution to the total PM emitted by aviation engines. While vPM is not currently a part of engine emissions certification regulations, vPM is used in aviation environmental impact assessments and for air quality modeling in and around airports. Current methods in use, such as FOA, were developed before many recent advances in experimental data acquisition and in understanding of vPM processes. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. These estimates can be used to develop inventories and provide a better estimate of total emission for most aviation engines. Its use in international regulatory tools can inform possible future regulatory actions regarding vPM. [ABSTRACT FROM AUTHOR]

Purpose: Climate change significantly impacts global temperatures, posing challenges to various sectors, including aviation. The purpose of this study is to assess the impact of climate change on aircraft engine performance during different flight phases (take-off and cruise) and the environmental consequences. Design/methodology/approach: This study examines the effects of rising temperatures on aircraft engine performance using real-time data from a Boeing 787-8 equipped with GEnx-1B engines, which are collected via Flight Data Recorder of the engines and were analyzed for the take-off and cruise phases on the ground. Exhaust gas temperature (EGT), fuel flow and take-off weights were evaluated. Findings: The analysis revealed a significant increase in EGT at the cruising altitude of 38,000 ft during the summer months compared to expected standard atmospheric values. This increase, averaging over 200 °C, is attributed to global warming. Such elevated temperatures are likely to accelerate the degradation of turbine components, resulting in increased fuel consumption: higher EGT signifies inefficient engine operation, resulting in more fuel burned per unit thrust; early engine aging: elevated temperatures accelerate wear and tear on turbine components, potentially reducing engine lifespan and increasing maintenance costs and enhanced atmospheric pollution: incomplete combustion at high EGTs generates additional emissions, contributing to local air quality concerns. Practical implications: The research findings have practical implications for understanding the potential operational challenges and environmental impacts of climate change on aircraft engine performance. This lets us explore mitigation strategies and adapt operational procedures to ensure sustainable regional aviation practices. Originality/value: This research enhances environmental consequences by assessing the impact of climate change on aircraft performance. [ABSTRACT FROM AUTHOR]

Purpose: The altitude and weather conditions affect directly fuel consumption and engine efficiency of the aircraft engines. The thermo-physical properties of the weather of altitude play a significant role in this process. Unfortunately, engine performance based on altitude conditions also causes waste heat and environmental pollution due to engine entropy generation. However, environmental impact assessment is needed to improve environmental sustainability. This study aimed to analyse the energy and environmental performance of a piston engine based on altitude conditions. Design/methodology/approach: This study is based on the entropy approach, and it aims to assess the environmental impact of the engine. Exergy analysis with together two new indices to evaluate the environmental effects caused by the engine under altitude conditions was used. Findings: The analysis reveals that the exergy efficiency of the piston engine is 23.9% on average for the three referenced altitudes, while the exergy efficiency difference between altitude boundary conditions is 11%. In addition, the entropy production of the engine is on average 10.55 kW/K. In this case, the environmental pollution potential resulting from the entropy production of the engine is on average 3.29 times higher due to reversible conditions, while the improvement rate was found to be 58%. Originality/value: This analysis shows that engine efficiency increases as altitude increases. Similarly, it can also be said that the environmental impacts are reduced and the improvement of the engine has opportunities for operational processes. Besides, in the study, some suggestions for motor performance impact analysis were presented. [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]

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]

One source of modern aircraft engine noise is the fan, which is especially noticeable during takeoff at high angular rotation speeds. In such modes, supersonic flow around the fan blades occurs, which leads to the formation of shock waves that propagate upstream until they exit the engine duct. As a result, specific noise is emitted into the front hemisphere, consisting of a number of harmonics that are multiples of the fan rotation frequency. The paper analyzes this effect using a simple model of the propagation of a system of shock waves. An energy approach is used to demonstrate that a system of shock waves with shocks of equal amplitude attenuates the most rapidly. [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]

The object of research is the impact of airplanes on the environment. Aviation is an area of our life that has been developing faster and faster every year for more than a century. Accordingly, aircraft have a negative impact on the surrounding natural environment, and therefore the assessment of their impact on nature and people is an important and integral component of research in the field of ecology and sustainable development. In particular, the airplanes produced by Boeing are among those that are most widely used in both passenger and cargo air transportation, so a study was conducted on their example. In the study, calculation methods were used to estimate the load on the surrounding natural environment from the operation of Boeing 747 and Boeing 757 aircraft, based on the declared characteristics of emissions from the engines of these aircraft. The study shows that every aircraft has a certain negative impact on the environment due to the emission of pollutants during all phases of flight. However, there are opportunities to minimize this impact by implementing more complete fuel combustion, replacing standard fuels such as aviation kerosene with more modern fuels, reducing the weight of the aircraft, which will lead to lower fuel consumption and, accordingly, lower emissions into the atmosphere. Also effective is the introduction of technologies to reduce the noise load from the operation of aircraft, such as the introduction of noise-protective coverings of runways, new materials for the production of aircraft engines, as well as changing the design of engines and the use of double-circuit scheme. The work assessed the impact of aircraft on the environment and the effectiveness of environmental protection measures using the example of the operation of Boeing 747 and Boeing 757 aircraft, and developed proposals for the implementation of environmental protection and technical measures to reduce this impact. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The most efficient technique that is used for aircraft engine tuning is through mounting the engine on the engine test bench (ETB) to analyze, tune and monitor its variables through the ETB run. It is practically very difficult to unmount the engine from the aircraft and mount it on the ETB for analyzing and estimating a single variable such as fuel consumption or oil temperature as the unmounting process requires huge manpower and machinery. This problem can be resolved if the fuel consumption of an air vehicle is estimated without unmounting the engine from the aircraft through applying data analytics and machine learning models. Therefore, in this paper, the fuel consumption of an aircraft is analyzed and estimated through advanced data science techniques. The dataset went through data analyzing and preprocessing techniques before applying multiple machine learning models such as multiple linear regression (MLR), support vector regression, decision tree regression and deep learning algorithm RNN/LSTM. The performance of algorithms has been evaluated using model evaluation methods such as mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination. The models are evaluated in taxi, cruise and approach flight phases where the LSTM performs excellent among all other algorithms with RMSE 15.1%, 10.5% and 0.9%, respectively. [ABSTRACT FROM AUTHOR]

Estimating the remaining useful life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine's condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time-series sensor measurements serves as the input, initially processed by the bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods. [ABSTRACT FROM AUTHOR]

Physical and mathematical numerical models have been developed to predict the effective acoustic properties of sound-absorbing honeycomb structures at sound pressure levels of 100 and 130 dB with normal sound wave incidence. The sound absorption coefficients and patterns of acoustic interactions of cells installed at the end of a cylindrical duct with normal sound wave incidence on them were studied by numerical mathematical and physical modeling. The sound absorption efficiency of single and groups of resonators of various shapes and sizes is estimated, and unique combinations of cells in groups are identified, taking into account their acoustic interactions. Representative samples of fragments of sound-absorbing structures were 3D-printed; laboratory tests of the samples were carried out with an interferometer with a normal sound wave incidence on the cells at a sound pressure level of 130 dB. [ABSTRACT FROM AUTHOR]

In this study, we present a comprehensive approach for predicting the remaining useful life (RUL) of aircraft engines, incorporating advanced feature engineering, dimensionality reduction, feature selection techniques, and machine learning models. The process begins with a rolling time series window, followed by the extraction of a multitude of statistical features, and the application of principal component analysis for dimensionality reduction. We utilize a variety of feature selection methods, such as Genetic Algorithm, Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator Regression, and Feature Importances from a Random Forest model. As a significant contribution, we introduce the novel aggregated feature importances with cross-validation (AFICv) technique, which ranks features based on their mean importance. We establish a selection criterion that retains features with a cumulative mean sum equal to 70%, thereby reducing the complexity of machine learning models and enhancing their generalizability. Four machine learning regression models—Natural and Extreme Gradient Boosting, Random Forest, and Multi-Layer Perceptron—were employed to evaluate the effectiveness of the selected features. The performance of our proposed method is assessed by the evaluation metrics Root Mean Square Error (RMSE) and R2 Score, and also considered within-interval percentages and relative accuracy metrics. Importantly, a novel PCA interpretability was introduced to provide real-world context and enhance the utility of our findings for domain experts. Our results indicate that the proposed AFICv technique efficiently achieves competitive performance across the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) sub-datasets using a significantly smaller subset of features, thus contributing to a more effective and interpretable RUL prediction methodology for aircraft engines. [ABSTRACT FROM AUTHOR]

A method is proposed to determine uncontrolled areas of aircraft engine parts in penetrant testing, taking into account the specifics of applying developers of different types (all forms according to ISO 3452-3). The main technological factors affecting the testability of surfaces of parts of complex geometry are presented. A method has been experimentally tested, which makes it possible to determine the uncontrolled zones of engine parts in penetrant testing, due to the specifics of applying different forms of developers. It has been established that when carrying out penetrant testing of parts, especially with complex configurations/geometry, the existing technologies for applying developers may not ensure its high-quality application to all controlled surfaces of parts, as was previously assumed based on the results of an expert assessment. It has been experimentally proven that such structural elements of parts as holes are controlled to a depth much less than the diameter. [ABSTRACT FROM AUTHOR]