Aircraft Engine Research Articles

Simulation Analysis and Experimental Investigation on the Fluid–Structure Interaction Vibration Characteristics of Aircraft Liquid-Filled Pipelines under the Superimposed Impact of External Random Vibration and Internal Pulsating Pressure

This paper investigated the fluid–structure interaction vibration response of an aircraft liquid-filled pipeline under external random vibration and internal pulsating pressure. First, the fluid–structure interaction solution is theoretically analyzed, and the advantages and disadvantages of the direct coupling method and the separation coupling method are compared, with the latter chosen as the simulation analysis method in this study. Second, taking the U-shaped oil pipeline of an aircraft engine as an example, simulation modeling was performed to compare and analyze the fluid–structure interaction vibration response of aircraft liquid-filled pipelines under different working conditions, obtaining the vibration response characteristics of stress danger points under various conditions. Finally, a test bench for an aircraft liquid-filling pipeline was built to explore the influence of external random vibrations with different kurtoses, different pipe wall thicknesses and different working conditions on the vibration response danger points of aircraft liquid-filling pipelines, verifying the simulation conclusions and providing a basis for aircraft liquid-filling pipeline design.

Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.

Combining Machine Learning, Embedded Sensor Networks and Additive Burner Design for Combustor Structural Health Monitoring

Abstract The crystAIr project is about sensitising burners for flame monitoring based on a discrete number of dynamic pressure sensors and a machine learning approach. The context is the development of better aircraft engines and the prospect of technical solutions for hydrogen-fuelled gas turbines. The combination of sensors and appropriate signal processing is designed to mimic a ‘feel’ for the state of the flame. The idea is to monitor the gas turbine's correct operation in real-time and anticipate impending problems such as flame blowout, combustion instability or flashback. Compared to conventional flame monitoring based on signal sampling, thresholding, band-pass analysis and time-lag measurement, this method should be less computationally intensive, more sensitive and more robust to artefacts. Not only should it be more responsive, but it should also anticipate problems based on a lessons-learned approach and outperform the conventional precursors. An unassisted machine learning method is used to achieve this. A custom-built AM burner designed for this experiment provides multiple instrumentation options. A powerful data acquisition system is set up to collect data on multiple channels, over a long time duration and at high speed to collect the learning signal chunks. The focus is on the detection of flames and, more specifically, the moment of their ignition and extinction, the operating conditions that are prone to flashback and the stability of the combustion. Additional measurement techniques are used to refine the learning method. The paper covers all these points and presents the first results.

Microwave reflective properties of amorphous nanogranular composites (CoTaNb)x(MgO)1-x

The most important task of flexible and stretchable electronics is to modify the structure in nanoheterogeneous metal-dielectric media to control the properties of materials and create new small-sized devices based on them. In this paper, we present the results of experimental studies of the structure and microwave reflective properties in the frequency range of 8–12 GHz of amorphous nanogranulated composites (CoTaNb)x(MgO)1−x, 21.73 ≤ x ≤ 73.27 at.% with a thickness of 311–1040 nm. Niobium and tantalum, which are part of the metal alloy, are the most promising metals used in aircraft and rocket engineering, as well as in radio electronics and microprocessor technology, for example, as capacitors. The AFM method revealed a granular structure with a granule size from 70 to 200 nm at a metal alloy content of up to 51.70 at.%. The spectra of the microwave reflectivity as a function of frequency, the dependence of the reflectivity on the metallic phase content and the effective thickness of the composite layer are obtained. It is shown that a significant increase in the microwave reflectivity and its saturation are determined to a greater extent by the metallic phase content than by the effective layer thickness. The results of the experimental effect of magnetic fields with induction up to 0.3 T on the microwave reflectivity of composite films are presented. It is shown that magnetic fields can most effectively change the reflective properties of composites (from 8 % to 29 %) at the extremes of the microwave reflectivity of films with the lowest metallic phase content of 22.61–21.73 at.%. With an increase in the metallic phase content, the maximum change in the microwave reflectivity decreases exponentially.

Method for Remaining Useful Life Prediction of Turbofan Engines Combining Adam Optimization-Based Self-Attention Mechanism with Temporal Convolutional Networks

Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models.

An aircraft engine remaining useful life prediction method based on predictive vector angle minimization and feature fusion gate improved transformer model

Remaining useful life (RUL) prediction is crucial for achieving intelligent and predictive maintenance of aircraft engines. In practical applications, advance prediction values smaller than the true values can prevent serious deferred maintenance accidents. Using asymmetric loss functions directly leads to noticeable accuracy degradation, and existing methods often fail to satisfy accuracy and advance prediction requirements. To address this problem, this paper proposes a novel RUL prediction method based on the Prediction Vector Angle (PVA) minimization and Feature Fusion Gate (FFG) improved Transformer network. Specifically, the FFG is proposed to enhance Transformer prediction accuracy by dynamically fusing global and local features. The concept of PVA is first introduced based on the tilting properties of the RUL descent process. The target of the prediction model is cleverly transformed from error minimization to PVA minimization through the cosine similarity loss function. Various experiments on the CMAPSS dataset demonstrate the effectiveness of the proposed method in achieving high accuracy and advanced prediction. Compared to the state-of-the-art method, RMSE is reduced by at least 2.94 % and Score by 7.00 %. Finally, the PVA minimization mechanism significantly improves long short-term memory and convolutional neural network performance. The proposed method is noteworthy for its superiority and applicability.

Tribological performance of ZrO2 nanoparticles as friction and wear reduction additives in aviation lubricant

Abstract High-performance aircraft engines require superior aviation oils to enhance their lubricating performance and prolong service life. Addition of nano-sized ceramic particles has been considered as a useful way to improve the tribological performance of base fluids. Up to now, few previous studies focused on the tribological properties of ZrO2 nanoparticles in aviation oil. The current study dispersed ZrO2 nanoparticles into PAO20 aviation base oil as lubricant additives. A dual-step method comprised of physical blending and ultrasonic dispersing was applied in the preparation of ZrO2 nanofluids. Oleic acid was utilized as surfactant to enhance the stability of ZrO2 nanofluids. Ball-on-plate reciprocating sliding wear tests were conducted to obtain the coefficient of friction and wear volumes. It was found that the PAO20 base oil produced the highest coefficient of friction of 0.278 and wear volumes of 2.305╳10-2 mm3. 5wt% ZrO2 nanofluids with 5wt% oleic acid showed the best lubricating performance. The coefficient of friction was reduced by 31.29%, and wear volume was reduced by 42.95%. In the examination of wear tracks, a physically embedded tribo-layer of ZrO2 nanoparticles and an oleic acid tribo-film with low shearing resistance were formed, which lowered the friction, and protected the mating surfaces against abrasive and adhesive wear. The results obtained in this study have applicable values in the development of high-performance aviation lubricants.

Aerodynamic optimization of double S-duct caret intake by self-adapting non-dominated sorting genetic algorithm

The caret intake could not only provide compressed airflow to the aircraft engine stably but also should have applicable radar stealth performance. Radar stealth means that the echo intensity of the intake should be low when detected by radar, which could be measured by radar cross section (RCS). The airflow distribution of the double S-duct caret intake was numerically optimized to improve aerodynamic performance, with radar stealth performance as constraints. Self-adaptive genetic algorithm and Kriging model were used to improve the optimization efficiency. There could be multiple boundary-layer separations in the S-duct caret intake, resulting in obvious total pressure distortion at the outlet and secondary flow. To suppress these adverse airflow phenomena, the optimization of the double S-duct intake was defined as a problem with two targets and multiple constraints. By defining the probabilities of selection and variation as functions of the iteration number and the fitness, the self-adapting non-dominated sorting genetic algorithm can be applicable for such a problem. Using the Kriging model, which was proven to have satisfactory accuracy in predicting performance parameters, an optimized intake with both excellent aerodynamic and stealth performance was obtained, with a 30% reduction in DC60 value and a 47% reduction in SC60 value compared to the intake before optimization.

Numerical prediction of passive speed and performance for multistage pump without power drive in natural flow process

With the development of engineering applications and the increase in system complexity, some particular fields, such as liquid rocket engine turbopumps, aircraft engine fuel systems, and marine natural flow cooling systems, are increasingly focusing on the performance characteristics of pumps under natural flow conditions. The pump is in the form of resistance components under natural flow conditions without a power drive. The impeller undergoes passive rotation by the impact of inlet flow. Due to the specificity of its operating conditions and performance indicators, the pump's natural flow performance cannot be evaluated by regular methods. Therefore, this paper proposed a numerical prediction method for pump natural flow performance based on a coupled computational fluid dynamics coupled with six-degrees-of freedom model. The performance of a multistage pump with guide vanes was evaluated under different natural flow conditions, and the accuracy was verified by experimental measurements. The transient variation mode of pump performance parameters with time at the initial stage of natural flow impact was analyzed. The flow field's transient evolution characteristics and the wall shear stress variation during natural flow were investigated. It was found that the impeller's passive rotational speed increases linearly with the natural flow rate, while the hydraulic loss shows an exponentially increasing trend. Meanwhile, the natural flow loss coefficient shows an exponentially decreasing trend and gradually tends to a stable value. The high turbulent kinetic energy inside the impeller is mainly distributed in the flow separation region and large velocity gradients. The distribution of shear stresses is closely related to the flow behavior inside the pump and the geometrical features of the hydraulic components.

Aging hardening behavior and P texture formation of laser repaired GH4169 nickel-based alloy

Laser repair technology based on the DED principle is an advanced green remanufacturing technology that not only repairs the damaged parts but also restores their mechanical properties. Therefore, this technology has been widely used for the repair of key components in aircraft engine. However, due to the fact that the mechanical properties of the repaired zone are usually lower than those of the substrate, this limits the application of laser repair technology in the field of remanufacturing. Therefore, it is crucial to study the microstructure evolution and its influence on mechanical properties of the laser repaired object to improve the mechanical properties of the repaired zone and promote the widespread application of laser repair technology. In this study, the microstructure evolution and its effect on the aging hardening mechanism of laser repaired GH4169 alloy were investigated using SEM, EBSD and TEM techniques. The results show that the hardening mechanisms in the repaired zone are different from those in the substrate. The main hardening mechanisms in the repaired zone are strain hardening, elastic stiffness strengthening and aging hardening, while the hardening mechanisms in the matrix are grain refinement strengthening, aging hardening, strain hardening and texture strengthening. In addition, during the aging period, the texture of the matrix is mainly recrystallized Cube and Goss textures, while the texture of the repaired zone is mainly P texture, and the P grain nucleates and grows in the R-Copper grain.

Entropy generation analysis on hybrid dusty nanofluid flow over a heated stretching sheet: aerospace technology

Over the last few decades, many kinds of advanced substances that are acceptable for specific aerospace applications have been developed. An aircraft engine has a number of duties that are performed by kerosene oil. These responsibilities include oiling, air conditioning, servicing, preventing corrosion and reducing loudness. Maintenance is a matter of extreme importance. Without maintenance, it is only inevitable that any moving component might have the capacity to run away quickly. This project’s efforts are focused on reducing expenditures by extending the usable lifetimes of aviation components. Additionally, the initiative aims to improve fuel efficiency, load capacity and travel distance. The importance of the heat transfer investigation on MHD dusty hybrid nanofluid across a porous stretching sheet with entropy generation and thermal radiation is examined in the current research. We used the self-similarity transformation to transform the partial differential equation into ordinary differential equations. After applying transformations, for graphical purposes we have used the Bvp4c technique in MATLAB solver. The effect of active important parameters affecting the fluid’s capacity to transfer importance is shown in graphs and tables. For higher values of the magnetic field parameter, the velocity outline decreased, while the opposite nature was noticed in the energy profile. Based on our investigation, we found that mixed nanofluids are more effective in transferring energy carriers compared to dusty tiny fluids.

Thermo-elastic vibration analysis and optimization of a nanoresonator composed of a coupled nanotube system for acoustic liner application using the hybrid deep neural network–based white shark algorithm

This study explores the potential of nanoresonator systems composed of coupled nanotubes with graphene nanoparticles for enhancing the performance of acoustic liners in aircraft engines. The objective of this study is to develop an analytical model and predict its behavior under various conditions. The acoustic liners consist of perforated metal sheets and honeycomb cavities, which are essential for noise reduction in aircraft engines. The model is tested for variations in natural frequency, mode number (m), size effects (e0a), viscous constant (C), temperature (T), localness factor (L), and stiffness constant (K). A hybrid deep neural network–based white shark algorithm (DNN-WSA) is used to predict and optimize the performance of nanoresonator-coupled nanotube systems. Four theories were compared, such as wave propagation theory, nonlocal elasticity theory, polynomial eigenvalue approach, and governing equations with respect to natural frequencies in nanoresonator-coupled nanotube systems. The wave propagation theory yielded the lowest natural frequency, which was selected for detailed analysis. The optimized values of size effect of 2 nm, temperature of 5 K, and frequency of 1.971975 THz were obtained. When C = 0.3, K = 10, T = 300, and L = 10×e−9, the root mean square error (RMSE) value is 0.8421, which indicates improved predictive performance as it continues to decrease. The study’s findings showed that changes in viscous constants impact natural frequencies, while size effects have a minor influence. Temperature variations also affect natural frequencies, with higher temperatures leading to higher frequencies. The optimized model demonstrates enhanced predictive performance, which contributes to a better understanding of nanoresonator systems and their application in noise reduction for aircraft engines.

Performance and optimization evaluation for integration of sCO2 power system into the aircraft propulsion system

The aviation industry accounts for part of the CO2 emissions contributing to climate change. The industry has established a target to reduce 2050 net aviation carbon emissions by 50 % relative to 2005 levels. With this in mind, waste heat recovery is a key pathway to achieve reduced emissions and improve system efficiency. The waste heat may potentially be converted to electric power using a supercritical CO2 Brayton power cycle. The sCO2 power system offers the advantage of compactness owing to the high working fluid density, which is an important consideration for aircraft performance. The present work focuses on the integration of the sCO2 power system into the aircraft propulsion system and evaluation of its performance. Detailed optimization of the sCO2 waste heat system will be evaluated with a focus on cycle efficiency and net power under different operating conditions, including ground, takeoff, climb, cruise, and landing operations. The study is divided into two parts with two different turbofan engines, one with a nominal thrust of 30 kN and the other with a nominal thrust of 9 kN. The first part shows the effect and operation of the waste heat recovery unit under the different operating conditions. The second part is focused on cycle optimization and performance evaluation. The results demonstrate the potential of waste heat recovery during a range of operational conditions. The sCO2 cycle efficiency can reach between 25 and 39 % (depending on aircraft engine) with net power output in the range of 100 to 260 kW.

Influence of heat treatment on the structure and mechanical properties of pseudo α-titanium alloy in a welded joint

The object of this study was the weld material of a new Ti-6.1Al-6.0Zr-6.3Sn-3.2Mo-1.1Nb-1.2Si alloy, which was obtained by electron beam welding. Electron beam welding (EBW) provides rapid melting and cooling with crystallization under significant temperature gradients. For the pseudo α-titanium alloy, this leads to the appearance of significant residual stresses in the joint and determines the specificity of the dispersion decay of the β-phase. Residual stresses are reduced by preliminary heating, removed by heat treatment. Such processing exerts a critical influence on the formation of the structure and phase composition of the weld and the zone of thermal influence of the electron beam; it can form macro defects, undesirable phases, and structural states. The conditions of heat treatment have been determined to bring the welded joint from complex alloyed pseudo α-titanium alloy to the required level of mechanical characteristics. The structure, phase morphology, elemental composition, and mechanical characteristics of welded joints without additional heat treatment have been compared, with preliminary local heating of 400 °C, with additional post-weld local annealing at ТТ=750 °С, tT=4 minutes and sequential annealing in the furnace at ТТ=850 °C, tT=60 minutes. It has been established that a full cycle of heat treatment of a welded joint provides the highest characteristics of strength and plasticity, but local heat treatment also makes it possible to obtain a defect-free joint with satisfactory characteristics for less responsible products. It is shown how heat treatment of pseudo α-titanium alloy makes it possible to get rid of unwanted phase formations (Zr,Ti)5Si3Al and transform them into (Zr,Ti,Nb,Mo)3SiAl. The results are promising for use in the production of aircraft engine parts

A grey target performance evaluation model for aeroengine pressure test bench

PurposeThis paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.Design/methodology/approachBased on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.FindingsA case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.Originality/valueThe pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

High-Speed Milling of Hardened AMS 6260 Alloy Using Radius End Mill (Effect of Cutting Conditions on Flank Wear and Surface Roughness)

AMS 6260 alloy is a nickel-chromium-molybdenum steel containing Ni, Cr, Mo, with Fe as the main component, and is one of AMS (aerospace materials specifications) in the United States. This material has high hardenability and high toughness. For this reason, it is used as a material for parts that require high toughness, such as gears in aircraft engines. However, while AMS 6260 alloy after heat-treatment has high hardness, it significantly reduced machinability. Therefore, it is extremely difficult to achieve high efficiency and high accuracy in the cutting process of AMS 6260 after heat-treatment. In this paper, the experimental milling of the hardened AMS 6260 alloy after heat-treatment is conducted under some cutting conditions using a coated cemented carbide radius end mill, and the effect of cutting conditions on the flank wear and surface roughness is investigated. As a result, optimal cutting conditions on the tool wear and surface roughness were clarified.

Attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability

This study addresses the problem of attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability. Unlike traditional Markov/semi-Markov models that rely on transition probabilities, a novel switching rule is introduced that focuses on sojourn probability information associated with the target mode and sojourn time, which are easier to obtain than the well-known transition probabilities. Considering the vulnerability of stochastic switching systems to cyber-attacks, where the system state becomes unobservable and difficult to manage, a compensation-based output feedback controller framework is proposed. Using the Lyapunov method and stochastic analysis, sufficient conditions are provided to ensure system stability. Finally, the effectiveness and applicability of the developed approach are demonstrated using an F-404 aircraft engine system model.

Optimal ultra-broadband sound-absorption performance design for coiled up space structures with nonlinear robustness

In this paper, based on a high- sound-pressure microperforated plate model, a nonlinear sound-absorption model for multi-unit couplings based on coiled-up space structures is proposed. The sound absorption performance and relative impedance of two-unit coupled structures (TUCSs) were studied. The results show that the TUCS sound-absorption performance, which is good at low sound pressures, decreases significantly as the incident sound pressure increases owing to impedance mismatch. Furthermore, the influence of parameters such as aperture size, plate thickness, perforation rate, and equivalent length on the unit’s structural sound-absorption performance was studied. By employing the particle swarm optimization algorithm, we optimized the parameters of an eight-unit coupled structure (EUCS) using the proposed model. The optimization results reveal the nonlinear robust sound-absorption characteristics of the structure, which means the EUCS can maintain a stable and good sound-absorption performance when the incident sound pressure level and frequency are within 125–155 dB and 400–3000 Hz, respectively. Experimental assessments of the EUCS sound-absorption performance within the 300–1900 Hz range confirmed the accuracy of the proposed model and the efficacy of the optimized sound-absorption capabilities of the structure. Consequently, the proposed model and sound-absorption structure demonstrated potential applications in the acoustic lining design of aircraft engines.

Dynamic force identification considering modeling errors using modal expansion method and relevant vector regression algorithm

Accurate identification and estimation of external forces on launch vehicles, aircraft, and other in-service engineering structures plays a vital role in structural design and health monitoring. Considering structural modeling errors, this paper develops a novel force identification method based on modal expansion and relevant vector regression (RVR). Initially, the incomplete and noisy experimental modal data are used to modify the stiffness and mass matrices of the finite element (FE) model, reducing the impact of modeling errors on force identification accuracy. Subsequently, to account for noise disturbances and potential information about unknown initial conditions in the measured acceleration responses, the external forces and the unknown initial conditions are respectively expanded using trigonometric functions and the mode shapes based on the function fitting technique. On this basis, the force identification equation is established for the updated model. The RVR algorithm is integrated into modal expansion and force identification. Firstly, the mode shapes of the FE model are utilized to expand the incomplete and noisy experimental mode shapes to their full degree of freedom, significantly enhancing the accuracy of the model updating. Secondly, by combining the measured acceleration responses, the base coefficients can be solved sparsely, effectively identifying the dynamic forces even when unknown initial conditions are present.

Assessing the impact of climate change on aircraft engine performance during different flight phases (take-off and cruise) and the environmental consequences

PurposeClimate 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/approachThis 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.FindingsThe 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 implicationsThe 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/valueThis research enhances environmental consequences by assessing the impact of climate change on aircraft performance.