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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]

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]

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]

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]

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]

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]

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]

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]

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]

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 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]

Development of the fast model of phase transformations in the Pyrowear 53 steel was the objective of the paper. An upgrade of the JMAK (Johnson–Mehl–Avrami–Kolmogorov) was proposed. Coefficients in the model were introduced as modified Gauss functions of the temperature. Changes in the carbon and molybdenum concentrations due to precipitation were accounted for. Dilatometric tests were carried out across a wide range of cooling rates. Identification of the model was performed using inverse analysis for the results of these tests. Three austenitization cycles prior to transformations were applied and an influence of the grain size on the nucleation and kinetics of phase transformations was accounted for in the model. All the tests were carried out on the core samples with the nominal chemical composition and on the carburized samples. Numerical tests confirmed the good accuracy of the model for both compositions in the whole investigated range of the grain size. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions and improving revenue in the face of global warming and economic challenges is a growing concern for airlines. This paper addresses the inefficiencies and high costs associated with current aero-engine on-wing washing strategies. To tackle this issue, we propose a reinforcement learning framework consisting of a Similar Sequence Method and a Taylor DQN model. The Similar Sequence Method, comprising a sample library, DTW algorithm, and boundary adjustment, predicts washed aero-engine data for the Taylor DQN model. Leveraging the proposed Taylor neural networks, our model outputs Q-values to make informed washing decisions using data from the Similar Sequence Method. Through simulations, we demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Hybrid metal matrix composites (HMMCs) are a special type of material, possessing combined properties that belong to alloys and metals according to market demands. Therefore, they are used in different areas of industry and the properties of this type of material are useful in engineering applications, e.g., in aircraft engines and electrotechnical parts. The structure of the material requires a number of scientific studies to develop an appropriate processing technology. The paper presents the susceptibility of material from the HMMCs group with the EN AC-44300 (AISi12(Fe)) aluminum alloy matrix with a two-component reinforcement made of alumina particles (AP) and aluminosilicate fibers (AF) to thermal treatment with a laser beam. During this process, laser engraving of the researched material with variable beam power Pav and variable speed of the laser head vl were carried out. A metallographic analysis of the material was carried out. After laser engraving, surface structural changes of the material were determined. The properties of the surface geometric structure of processed material were also examined. Presented studies concern laser engraving on the surface of composite from the HMMC group, which was made by vacuum infiltration. Thanks to this method, it is possible both to produce shaped and precise composite castings with saturated reinforcement and to consequently minimize machining losses. Metal–ceramic composites from the HMMC group are hard-to-machine materials which create problems during machining. The aim of these studies was to develop a laser engraving technology with Al matrix composite with the addition of Al2O3 particles and aluminosilicate fibers, which constitute the reinforcement. The focus was on the selection of engraving parameters (beam power and speed of movement of the laser head). Clear examples of engraving, suitable for macro-assessment, were obtained with minimal change in the initial surface structure of the composite. [ABSTRACT FROM AUTHOR]

Thin-film thermocouples are used as temperature detection components in aircraft engines. However, the preparation of traditional thin-film thermocouples is complex, and their high-temperature stability performance attributes are poor. In this study, polymer-derived ceramic (PDC) technology is introduced to prepare polymer-derived ceramic thin-film thermocouples. Due to the superior high-temperature and adhesion performance characteristics of polymer-derived ceramics and the protective effects of the products of polymer precursors on the thin films, the performance attributes of thin-film thermocouples are greatly improved. After simplifying the preparation process and outputting a large thermoelectric potential, the polymer-derived ceramic thin-film thermocouple exhibits good repeatability, stability, and thermoelectric potential output consistency. At 1200 °C, the peak output is 203 mV, and the average Seebeck coefficient is approximately 182 μV/°C. The temperature measurement error is 0.316%. In the 8-h drift test, the coefficient first increases at 1.26 °C/h and then steadily decreases at −0.63 °C/h. This polymer-derived ceramic thin-film thermocouple has excellent performance and can be applied to the in situ monitoring of thermal components in high-temperature and harsh environments, such as engines. [ABSTRACT FROM AUTHOR]

As an indispensable part of the engine manufacturer supply chain, the eco-efficiency of altitude test facility (ATF) operations must improve. Automation is a key enabler in this context since it not only increases precision and reproducibility but also allows for reducing the test time and energy consumption. A suitable controller and reliable validation are crucial to ensure the stability and appropriate response of the control system. Both aspects necessitate a thorough understanding of the control plant, expressed in a numerical model. These models have to be suitable for control system design and validation while covering asymmetric flow phenomena that occur in the pipe system and detailing the nonlinear system dynamics to a high degree of accuracy. One-dimensional network models, state-space models, highly resolving numerical models, and data-driven models are relevant applications for this task. We compare the results of state-of-the-art one-dimensional network models which mainly imply symmetric flow conditions with those of three-dimensional Reynolds-averaged-Navier–Stokes (RANS) simulations which cover asymmetric flow phenomena. The findings show that the assumptions of idealized, axis-symmetric flow in the one-dimensional flow elements do not hold true for the complex flows in an altitude test facility. In a second step, we have compared the results of one-dimensional simulations with in-service measurements taken during ATF test campaigns. Deviations were observed, which become explainable based on the insights gained from the comparison with the RANS simulation. The findings reveal that the one-dimensional simulation-based approach is insufficient to adequately reflect the plant and subsequently for validation due to the observed asymmetric flow phenomena. To overcome this limitation, the application of an empirical first-order transfer function using system identification methods is proposed. Its applicability is successfully demonstrated for the exhaust gas section of the ATF. Subsequently, essential criteria for the design of a suitable control concept for the outlet condition are derived. [ABSTRACT FROM AUTHOR]

A good weight initialization is crucial to accelerate the convergence of the weights in a neural network. However, training a neural network is still time-consuming, despite recent advances in weight initialization approaches. In this paper, we propose a mathematical framework for the weight initialization in the last layer of a neural network. We first derive analytically a tight constraint on the weights that accelerates the convergence of the weights during the back-propagation algorithm. We then use linear regression and Lagrange multipliers to analytically derive the optimal initial weights and initial bias of the last layer, that minimize the initial training loss given the derived tight constraint. We also show that the restrictive assumption of traditional weight initialization algorithms that the expected value of the weights is zero is redundant for our approach. We first apply our proposed weight initialization approach to a Convolutional Neural Network that predicts the Remaining Useful Life of aircraft engines. The initial training and validation loss are relatively small, the weights do not get stuck in a local optimum, and the convergence of the weights is accelerated. We compare our approach with several benchmark strategies. Compared to the best performing state-of-the-art initialization strategy (Kaiming initialization), our approach needs 34% less epochs to reach the same validation loss. We also apply our approach to ResNets for the CIFAR-100 dataset, combined with transfer learning. Here, the initial accuracy is already at least 53%. This gives a faster weight convergence and a higher test accuracy than the benchmark strategies. [ABSTRACT FROM AUTHOR]

TiAl intermediate compound is an important material for high-temperature applications due to its superior creep resistance and oxidation resistance. It is suitable for high-pressure compressors and low-pressure turbine blades of advanced military aircraft engines. TiAl intermediate compound is an excellent substitute for nickel-based superalloys, as it can decrease weight by 40% and greatly enhance aircraft thrust-to-weight ratio. In this paper, the microstructure evolution and the mechanical properties of Ti2AlNb alloy with a 3.0 wt.% W and 0.1 wt.% Y addition obtained by blending elemental ultrafine powders was investigated by XRD, SEM-EDS, and mechanical testing device. The findings show that high relative density of 0.9945, and the excellent mechanical properties of Ti2AlNb–3W–0.1Y alloy can be obtained through isothermal sintering for 3 hour in a furnace with controllable argon atmosphere flow of 200 mL/min at 1,500°C. The alloy's tensile strength, yield strength, and elongation reach 1,030 MPa, 913 MPa, and 15.1% at 700°C, respectively. Meanwhile, the 3 wt.% of element W is added to the alloy to form (TiW)C as the second strengthening phase, which is uniformly distributed in the matrix of Ti2AlNb. The addition of Y element at 0.1 wt.% into the alloy can act as an effective scavenger of oxygen and inhibit the unsatisfactory precipitation of the brittle α2-phase in the Ti2AlNb alloy. Compared to the alloy without additions, the Ti2AlNb alloy with 3 wt.% W and 0.1 wt.% Y demonstrated 13.5% and 19.35% improvements in the fracture resistance at 25°C and 700°C, respectively. The alloy's yield strength was increased as well. The evolution regularity of the main metallography is (Ti2AlNb–TiAl–Ti3Al) → (Ti2AlNb–Ti3Al) → (Ti2AlNb–Ti3Al–(TiW) C) during the isothermal sintering of Ti–22Al–25Nb–3W–0.1Y alloy at 1,500°C. This study provides technical guidance for the preparation of ultrafine TiAl-based alloy powder and high-temperature aerospace applications [ABSTRACT FROM AUTHOR]

Swept blades have been widely used in the transonic fan/compressor of aircraft engines. However, because of the gap in knowledge regarding the change of the inlet flowfield by sweeping, design deviations are often produced. This study used experimental and numerical simulation methods to investigate the inlet circumferential fluctuations (CF) generated by the sweep. The results indicate that the CF source terms induced by the inviscid blade force cause a negative pressure gradient ahead of the leading edge of the forward sweep blade. This pressure gradient results in a spanwise migration of the inlet flow from the hub to the shroud, which redistributes the axial velocity across the entire spanwise range. As a result, there is a reduction in the incidence angle at the tip of the forward-swept blade, whereas an increase is observed at the root. This effect increases with increasing sweep angle. The influence of the CF stress terms starts at approximately 30% of the chord length upstream of the leading edge and the values of the axial component UU and tangential component WW are maximum. CF source terms are physical quantities that quantitatively describe the effect of the sweep, which can help quantify the mechanism of sweep and can play an important role in the design of sweep blades. Swept blades are extensively used in modern transonic fans and compressors to reconcile the conflict between high efficiency, high load, high through-flow, and wide stable operating range. However, there is more qualitative understanding of the mechanism of swept blades and less quantitative design theory. In this work, a method for quantitatively describing the flow mechanism of swept blades was proposed, using a reduced-dimension spanwise force balance analysis of experimental and numerical results. The effect of swept blades on the incoming flowfield is simplified as the action of the circumferential fluctuation source terms. The source terms induce a spanwise migration of the incoming flowfield, which results in a spanwise redistribution of the incidence angle of the swept blades, even under uniform inflow conditions. The circumferential fluctuation source term force can aid in quantitatively understanding the mechanism of swept blades, and enhance the precision and efficiency of swept blade design. [ABSTRACT FROM AUTHOR]

An aero-engine's cost, weight, and complexity can be greatly reduced if fuel is used for lubrication instead of oil. However, there are not many published works about the effect of fuel lubrication on the rolling element bearings' motion. This research analytically investigates the effects of kerosene lubrication on the cage slip, heat generation, and temperature of a cylindrical roller bearing. We developed an in-house program composed of a simplified quasi-static model to predict the cage slip and a thermal network model to predict the bearing's heat generation and temperature. The in-house program was verified with respect to data from previous researches. Four types of lubrication were investigated using the in-house program: under-race lubrication by oil, under-race lubrication by fuel, jet lubrication by oil, and jet lubrication by fuel. We conducted a parametric study by changing the bearing's rotational speed, lubricant flowrate, and inlet lubricant temperature to compare the kerosene lubrication of an imaginary aircraft engine bearing to conventional oil lubrication. [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]

Shape memory effects coupled with superelasticity are the distinctive characteristics of shape memory alloys (SMAs), a type of metal. When these alloys are subject to thermomechanical processing, they have the inherent ability to react to stimuli, such as heat. As a result, these alloys have established their usefulness in a variety of fields and have in recent years been chosen for use in stents, sensors, actuators, and several other forms of life-saving medical equipment. When it comes to the shape memory materials, nickel–titanium (Ni-Ti) alloys are in the forefront and have been chosen for use in a spectrum of demanding applications. As shape memory alloys (SMAs) are chosen for use in critical environments, such as blood streams (arteries and veins), orthodontic applications, orthopedic implants, and high temperature surroundings, such as actuators in aircraft engines, the phenomenon of environment-induced degradation is of both interest and concern. Hence, the environment-induced degradation behavior of the shape memory alloys (SMAs) needs to be studied to find viable ways to improve their resistance to an aggressive environment. The degradation that occurs upon exposure to an aggressive environment is often referred to as corrosion. Environment-induced degradation, or corrosion, being an unavoidable factor, certain techniques can be used for the purpose of enhancing the degradation resistance of shape memory alloys (SMAs). In this paper, we present and discuss the specific role of microstructure and contribution of environment to the degradation behavior of shape memory alloys (SMAs) while concurrently providing methods to resist both the development and growth of the degradation caused by the environment. [ABSTRACT FROM AUTHOR]

Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NO x)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO 2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NO x emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications. [ABSTRACT FROM AUTHOR]

Unique identification of machine parts is critical to production and maintenance, repair and overhaul (MRO) processes in the aerospace industry. Despite recent advances in automating these identification processes, many are still performed manually. This is time-consuming, labour-intensive and prone to error, particularly when dealing with visually similar objects that lack distinctive features or markings or when dealing with parts that lack readable identifiers due to factors such as dirt, wear and discolouration. Automation of these processes has the potential to alleviate these problems. However, due to the high visual similarity of components in the aerospace industry, commonly used object identifiers are not directly transferable to this domain. This work focuses on the challenging component spectrum engine tubes and aims to understand which identification method using only object-inherent properties can be applied to such problems. Therefore, this work investigates and proposes a comprehensive set of methods using 2D image or 3D point cloud data, incorporating digital image processing and deep learning approaches. Each of these methods is implemented to address the identification problem. A comprehensive benchmark problem is presented, consisting of a set of visually similar demonstrator tubes, which lack distinctive visual features or markers and pose a challenge to the different methods. We evaluate the performance of each algorithm to determine its potential applicability to the target domain and problem statement. Our results indicate a clear superiority of 3D approaches over 2D image analysis approaches, with PointNet and point cloud alignment achieving the best results in the benchmark. [ABSTRACT FROM AUTHOR]

This review article presents a summary of currently used and proposed methods of manufacturing fir tree slots of discs in turbine engines. The production of aircraft, including aircraft engines during times of overlapping global economic crises related to the COVID-19 pandemic or the war in Eastern Europe requires a quick response to the changing numbers of passengers and cargo. Similarly, the aviation industry must adapt to these conditions, and thus utilize flexible production methods allowing for a quick change in the design or type of a given part. Due to the constant adoption of new materials for the most critical aero engine parts and the necessity of complying with environmental regulations, it is necessary to search for new methods of manufacturing these parts, including fir tree slots. As an alternative to currently used expensive and energy-intensive broaching, many manufacturers try to implement creep feed grinding CFG or contour milling. However, other manufacturing methods, thus far rarely used for crucial machine parts such as WEDM, ECDM or AWJ, are gaining more and more popularity in the aviation industry. This article presents the advantages and shortcomings of these methods in the context of manufacturing fir tree slots. [ABSTRACT FROM AUTHOR]

The aviation industry generates a wide range of climate pollutants, which often leads to an underestimation of its impact on global warming. Constructing aircraft components using environmentally friendly materials could significantly reduce the environmental burden associated with aviation growth. The objective of this study is to evaluate the Global Warming Potential (GWP) of commonly used alloys in gas turbine engines of aircraft. Material information on aircraft engine components was extracted, and GWP data for each metal was obtained using SimaPro software, and the GWP of components was determined. The study revealed that RENE 15 and GTD111 turbine alloys had the highest GWP impact, ranging from 13.53 to 14.05 kg CO 2 e /kg, while AISI 403 and GTD-450 used in the compressor, M152 used in the turbine, and Cr-Mo-V alloys used in both the turbine and shafts had the lowest GWP effect, ranging from 1.61 to 1.90 kg CO 2e /kg. Moreover, the study evaluated the environmental impact associated with the manufacturing of aircraft engine components. The findings indicated that Ti-based materials had the highest GWP rate, with a value of 3.29 kg CO 2 e /kg, which was almost the sum of all other metals. In particular, Ti-based materials used in fan components were identified as the largest source of environmental impact during aircraft engine production, with a value of 2.02 kg CO 2 e /kg. These results underscore the crucial role of material selection in mitigating environmental impacts during aviation engine component production. The study highlights the need for more effective and efficient approaches to material selection from an environmental standpoint in the aviation industry, especially in the context of reducing global warming potential. The results of this research could inform decision-making related to material selection for aircraft engine components, and help the aviation industry to reduce its overall environmental impact and mitigate the effects of global warming. [ABSTRACT FROM AUTHOR]