Aircraft Engine Research Articles

Graph constrained empirical wavelet transform and its application in bearing fault diagnosis

The signal decomposition based on frequency domain distribution is a fundamental methodology for mechanical component fault diagnosis. However, existing methods face challenges such as susceptibility to noise interference and limited adaptability. Therefore, this paper proposes the graph constrained empirical wavelet transform (GCEWT) method. This method introduces structured information, such as the interrelationships among different parts of the frequency domain distribution of vibration signals, into the boundary detection process of empirical wavelet transform. The high-dimensional connectivity among different parts of the time-frequency distribution is utilized to construct an adjacency matrix. By constructing an adjacent graph, the proposed method encodes the adjacency relationships among frequency bands to constrain the low-dimensional spatial relationships between them. In conjunction with spectral clustering algorithms, the GCEWT method determines the boundaries for empirical wavelet transformation in the frequency domain. This approach achieves structured and adaptive decomposition of vibration signals from components of critical equipment, facilitating the structured and adaptive extraction of fault features. The effectiveness of the proposed method is validated using vibration data from both wind turbine drivetrain systems and aircraft engines. The experimental results demonstrate that the proposed method yields more reasonable signal decomposition results compared to traditional algorithms. Additionally, the proposed method proves to be more effective in extracting weak fault features of bearings in the presence of noise.

Application of Deep Learning in Predictive Maintenance of Aircraft Engines

The performance and dependability of aircraft engines are vital to the aviation sector, which is a key pillar of the world economy. Ensuring the seamless functioning of these engines is crucial for both economic efficiency and safety. While reactive and preventative maintenance are examples of traditional maintenance systems, they have limits of their own. Preventive maintenance can save costs by avoiding needless inspections and part replacements, whereas reactive maintenance frequently results in unplanned outages. In this regard, deep learning-powered predictive maintenance shows up as a ground-breaking method for raising the dependability and effectiveness of aircraft engines. Predictive maintenance, or PdM, uses a variety of data sources to monitor the state of the equipment and make maintenance recommendations. Instead of using preset plans or scheduling maintenance after a breakdown has occurred, this technique seeks to prevent failures and optimize schedules depending on the actual state of the equipment. Deep learning (DL), a branch of machine learning, is modeling complicated patterns in huge datasets by using multiple-layered artificial neural networks (thus the term "deep"). It is very effective at time-series data analysis, picture identification, and natural language processing, which makes it appropriate for predictive maintenance jobs involving substantial volumes of sensor data.

Accurate thickness evaluation of thermal barrier coatings using microwave resonator sensor

Thermal barrier coatings (TBC) are frequently employed in aircraft engines and turbine blades for protection against high temperatures. Constant exposure to in-service stresses, however, leads to a variety of anomalies in TBC that could prove to be potentially catastrophic for the assemblies they protect. This raises the need for inspection using non-destructive techniques (NDT) to ensure the reliability and structural integrity of the coated assemblies. Thickness evaluation of the topcoat of the TBC is paramount to determining structural integrity of the TBC. Microwaves are particularly suited for such applications since they readily penetrate low loss TBC. However, the performance of previous microwave-based TBC thickness evaluation techniques was limited in accuracy to 15 μm. In this paper, accurate thickness evaluation of thermal barrier coating (TBC) is performed using a microwave resonator sensor operating at a relatively low frequency (<1 GHz). TBC thicknesses as low as 100 μm are evaluated based on shift in resonant frequency of the probe. Overall, practically relevant thicknesses ranging from 100 μm to 600 μm are evaluated herein and multiple trials are performed to ascertain repeatability and assess uncertainty in measurements. For a 250 μm thick TBC sheet, a mean error of 0.9 μm was accomplished which translates to 0.36 % error with a standard deviation of 1.5 μm. Compared to other microwave sensors reported in the literature, a better estimation accuracy is demonstrated in this work, in particular for thicknesses less than <250 μm.

Gas exchange optimization in aircraft engines using sustainable aviation fuel: A design of experiment and genetic algorithm approach

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

Simultaneous improving high temperature oxidation resistance and room temperature toughness of Nb[sbnd]Si based alloy via appropriate Ta content addition strategy

NbSi based alloys are expected to become the application materials for high pressure turbine blades of aircraft engines. This paper investigates the impact of Ta addition on room temperature fracture toughness and isothermal oxidation behavior at 1250 °C of four NbSi based alloys with compositions of Nb-16Si-20Ti-8Zr-4Hf-1.5Al-xTa (x = 0, 1, 2, and 4 at.%) fabricated through vacuum non-consumable arc-melting. The results show that the room temperature fracture toughness and 1250 °C oxidation resistance of the alloys increase first and then decrease with the addition of Ta element. The room temperature fracture toughness of 1Ta alloy is the best, reaching 17.1 MPa·m1/2. Adding a moderate amount of Ta (1 at.%) significantly enhances the alloy's oxidation resistance, but an excessive Ta content diminishes this enhancement. This research has obtained a Nb-16Si-20Ti-8Zr-4Hf-1.5Al-1Ta alloy with high room temperature fracture toughness and excellent high temperature oxidation resistance simultaneously, which provide an ideal path for designing NbSi alloys with superior comprehensive properties.

Few-shot RUL prediction for engines based on CNN-GRU model

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

The relevance of introducing a requirements management system in the production process of the aircraft engine construction industry

It is necessary to have access to documents containing product requirements during the design process. Search and processing of this data slows down the product design significantly. The reduction of time losses calls for the availability of a proper software tool. In this work, programs for the creation of requirements management systems are considered. The proposed concept of the requirements management system will make it possible to increase the efficiency of the product design process. The identified potential of the software allows us to talk about the possibility of introducing this program into the production process of enterprises in the aircraft engine industry.

Volcanic jets to commercial jets: synopsis and diagnosis

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

Fabrication characterization and compression failure analysis of Ni-based alloy ABD-900AM TMPS structures via laser powder bed fusion

The novel high-strength and high-temperature nickel-based alloy developed for additive manufacturing, i.e., ABD-900AM, shows great potential in next-generation aircraft engines. However, there are limited investigations about the printing capability of ABD-900AM with complex structure as well as compression behaviors of this new alloy system. To meet the lightweight requirement, ABD-900AM with triply periodic minimal surface (TPMS) is fabricated by selective laser melting (SLM) in the present work, and the printing quality and compression performance of different TPMS structures are analyzed utilizing both experimental and computational methods. First, six types of TPMS structures are designed, and the external contours fabricated by SLMed ABD-900AM are detected through 3D optical scanning. In general, high fabrication quality is achieved with the proper process parameters, while the positive deviations are observed at the lateral surfaces and edges, and the negative deviations are observed at the upper regions of the cellular structures. Further, compression tests of SLMed ABD-900AM TPMS structures are conducted, and the failure mechanisms are evaluated. The equivalent elastic modulus is ranked as Diamond > Split-P > Primitive > Gyroid > I-WP > Lidinoid. The SLMed ABD-900AM TPMS structures shows advantages compared to these TPMS structures fabricated using typical metal alloys. Finally, the computational models of SLMed ABD-900AM TPMS structures are built up in the commercial software ABAQUS. The stress-strain behaviors and the failure process of different SLMed ABD-900AM TPMS structures are successfully reproduced.

Aircraft engine dust ingestion at global airports

Abstract. Atmospheric mineral dust aerosol constitutes a threat to aircraft engines from deterioration of internal components. Here we fulfil an overdue need to quantify engine dust ingestion at airports worldwide. The vertical distribution of dust is of key importance since ascent/descent rates and engine power both vary with altitude and affect dust ingestion. We use representative jet engine power profile information combined with vertically and seasonally varying dust concentrations to calculate the “dust dose” ingested by an engine over a single ascent or descent. Using the Copernicus Atmosphere Monitoring Service (CAMS) model reanalysis, we calculate climatological and seasonal dust dose at 10 airports for 2003–2019. Dust doses are mostly largest in Northern Hemisphere summer for descent, with the largest at Delhi in June–August (JJA; 6.6 g) followed by Niamey in March–May (MAM; 4.7 g) and Dubai in JJA (4.3 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb, and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP, though large variations in dose magnitude are found, with CAMS producing lower doses by a factor of 1.9 to 2.8, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44 % and 41 % respectively. We suggest that a likely low bias of dust concentration in the CAMS reanalysis should be considered by aviation stakeholders when estimating dust-induced engine wear.

Exploring factors influencing aviation MRO services with blockchain technology in Taiwan

PurposeBlockchain is the fastest-growing technology currently being used in the aviation industry, especially in aviation maintenance, repair and overhaul (MRO) services. This study aims to create an analytic framework to assess the main factors and subfactors that have significantly influence the blockchain used in aviation MRO services. A mixed-methods approach is used to gain a comprehensive understanding of how blockchain is being adopted in aircraft maintenance facilities, Semi-structured interviews and questionnaires are used to gather data. The questionnaire is focused on the present state of the MRO industry.Design/methodology/approachBased on the literature review, a framework including four factors and 12 subfactors is developed, and the analytic hierarchy process (AHP) is then established. This study explores how these factors influence the implementation of blockchain in aviation MRO services. The five aviation MRO services providers in Taiwan, namely, “Evergreen Aviation Technologies Corporation,” “Taiwan Aircraft Maintenance and Engineering Co., Ltd.,” “Air Asia Company Ltd.,” “Aerospace Industrial Development Corp.” and “GE Evergreen Engine Services Corporation” are considered; furthermore, 55 experts working in these organizations were invited to evaluate the relative importance criteria in the AHP framework.FindingsThe results indicate that “inventory management” is the most important criterion, followed by “provisioning, procurement and sales” and “maintenance planning.” In addition, the three most important subfactors are “parts interchangeability,” “customer stock” and “SPEC2K interface for ATA SPEC 2000.”Originality/valueAsia is ranked as the second most important aviation MRO service market in the world. Taiwan has the shortest flight hours in the western Pacific region, the seven major foreign cities in this area. Aviation MRO service providers located in Taiwan are the best choices for aircraft MRO in the Asia-Pacific region, indicating that Taiwan serves as a promising market development evaluation model for blockchain aviation MRO services. The results offer a comprehensive overview of the relative importance of different criteria for MRO services that use blockchains. In addition, the findings present the market potential for key players in the aviation industry, including aircraft engineers, airline companies, aircraft component manufacturers and aviation MRO service providers.

THE AIRCRAFT ENGINE PROPELLER MAIN CHARACTERISTICS DETERMINATION IMPROVED METHOD

An improved method of the fixed and variable pitch propeller aerodynamic characteristics determining that based on the vortex theory is presented. It is shown that the design of propellers has it`s peculiarities, it does not take into account a number of factors that affect the propeller operation to one or another degree. This actualized the special purpose unmanned aerial vehicles engines propellers aerodynamic characteristics calculating methodology improvement. Since the use of the propeller profiles performances in the design requires the air compressibility correction introduction already after the selection of the propeller, that complicates the design, therefore the mathematical model of the variable-pitch propeller working process has been improved based on the profile model with the correct consideration of the Mach and Reynolds numbers influence on the aerodynamic characteristics of the air screw. This advantage of the developed airfoil model allows the design and verification of a subsonic propeller for ground and flight operations. Methods of design and verification calculations of propeller blades are presented. Verification of the improved methodology was carried out by comparing calculated data with known experimental data. The reliability of the improved aircraft engine propeller main performances determination methodology is substantiated by the correct application of the numerical and experimental methods of aerodynamics, the consistency of theoretical provisions with experimental data. The satisfactory coincidence of the calculated data obtained by the improved method of the engine propeller main performances determination allowed us to draw a conclusion concerning its efficiency. The improved technique for the main performances of an aircraft engine propeller determination does not require knowledge on the propeller profiles aerodynamic characteristics from which the blade is drawn, since these characteristics are calculated based on known geometric data, taking into account the Mach and Reynolds numbers. This allows you to design and build a subsonic propeller of both fixed pitch and variable pitch.

Prediction of Aerodynamic Forces at the Tip of the Compressor Blades Based on Multi-scale 1DCNN Combined with CBAM

The compressor is a crucial component of aircraft engines, and the blades are the critical factor affecting the performance of the compressor. Based on multi-scale one-dimensional convolution neural network (1DCNN) with Convolutional Block Attention Module (CBAM), a data-driven model is proposed for predicting the aerodynamic characteristics of the blade tips. The model is trained using the Adam with decoupled weight decay (AdamW) optimizer and a staged learning rate scheduling strategy. Due to the distinct aerodynamic pressure distributions on the suction and pressure sides, separate models are constructed in order to reveal the aerodynamic performance of the blade tips accurately. During the model validation, Root Mean Square Error (RMSE) and the coefficient of determination (R2) are taking as evaluation criterions, where high reliability is demonstrated compared to Computational Fluid Dynamics (CFD) results.

Background-Oriented Schlieren For The Detection Of Thermoacoustic Oscillations In Flames

Thermoacoustic oscillations are well known to combustion engineers. They are not only a cause of concern, but also give hope to be able to operate aircraft engines with hydrogen, due to flame stabilization and significant reduction of NOx emissions when thermoacoustically excited. The aim of this work is to investigate the potential of using a heterodyne background-oriented schlieren (HBOS) technique to detect thermoacoustic oscillations in the density field of an acoustically excited flame. It also seeks to address the issue of accuracy due to the small amplitude of thermoacoustic oscillations compared to turbulent density fluctuations in a flame. The experiment uses an unconfined swirl-stabilized methane flame and investigates thermoacoustic oscillations at about 3.4 kW power at ambient conditions excited by a siren at 225 Hz. The HBOS technique recently presented by the authors uses a carrier fringe system in background-oriented schlieren recordings, with subsequent analysis using evaluation techniques known from holographic interferometry. These fast algorithms reduce turbulence noise by phase averaging a large number of images. To derive local data from the line-of-sight projections, an inverse Abel transform is applied. Laser interferometric vibrometry is used to calibrate to the observed oscillations. A comparison with data from chemiluminescence is also presented to better demonstrate the application of this efficient technique to the detection of heat release oscillations. Future tasks in this project will deal with full-scale test benches and machine learning tools to address the limited observations in them.

Process simulation of laser shock-based disassembly of adhesively bonded composite/metallic structural parts: a numerical upscaling

ABSTRACT In previous works, both experimental and numerical investigations have shown the potential of the laser shock technique for disassembling adhesively bonded composite/metal coupons. This study extends this concept to upscale the method, focusing on the full disassembly of a foreign object damage (FOD) panel designed to replicate an aircraft engine fan blade. Utilizing LS-Dyna explicit FE software, the numerical simulation incorporates various material models: a progressive damage model for the 3D CFRP substrate, Johnson–Cook plasticity model combined with Grüneisen equation of state for the titanium layer, and a cohesive zone model with a bilinear traction separation law for the adhesive layer. Investigating the FOD panel’s inclined geometry, a preliminary analysis examines the impact of inclination angles on shock wave propagation and back face velocity, revealing minimal alterations. Initial simulations are conducted to determine double pulse delay times and evaluate spot location effects. Automation of the multi-step process simulation is achieved through a Python script. After approximately 30 shots, complete disassembly of the FOD strip is achieved, with observed damage primarily limited to matrix cracking in the composite substrate. Numerical findings support the efficacy of the double-shot laser shock method for disassembling adhesively bonded metallic/CFRP components, contingent upon appropriate experimental arrangements.

Lean Blowout Proximity Sensing in a Low Emission Liquid-Fueled Combustor

ABSTRACT Advanced aircraft engine combustor designs, intended for achieving low NOx emissions, necessitate lean combustion with small operating margins above the LBO boundary, posing a high risk of exhibiting lean blowout (LBO) problems. Employing active LBO controllers receiving input from LBO proximity sensors can allow for narrower margins, thereby yielding multiple performance gains. Although several previous studies investigated LBO proximity sensing, they predominantly focused on premixed gas-fueled combustors. The application of proximity sensing approaches to liquid-fueled combustor designs, relevant to aircraft engines have not received adequate attention. Therefore, this study investigates LBO proximity sensing in a low emission lean direct injection (LDI) liquid-fueled combustor design developed by NASA. The combustor operates at atmospheric pressure with preheated air. The proximity sensing is examined by analyzing both OH* and CH* chemiluminescence signals, with a focus on assessing their relative performances. The signal analysis approaches include spectral, partial extinction event (LBO precursor) detection, signal RMS, and the CH*/OH* ratio, with an emphasis on the first two methods. Both steady-state and transient operating conditions have been examined. The combustor did not exhibit large-scale flame partial extinction and re-ignition events near LBO, as commonly observed in gas-fueled combustors. However, an increase in flame unsteadiness and partial extinction events with very subtle signatures in optical signals, have been observed. Low-pass filtering has been observed to considerably enhance the precursor signatures enabling their improved detection. With a reduction in LBO margin, an increase in signal low-frequency power, RMS, and precursor event occurrence rate have been observed, which were utilized for providing LBO proximity measures. A key finding from this study is that CH* signals provide significantly superior performance compared to OH* for the proximity sensing in all the approaches, and the potential reasons are discussed.

Experimental Investigation of the Sensitivity of Forced Response to Cold Streaks in an Axial Turbine

In turbomachinery, geometric variances of the blades, due to manufacturing tolerances, deterioration over a lifetime, or blade repair, can influence overall aerodynamic performance as well as aeroelastic behaviour. In cooled turbine blades, such deviations may lead to streaks of high or low temperature. It has already been shown that hot streaks from the combustors lead to inhomogeneity in the flow path, resulting in increased blade dynamic stress. However, not only hot streaks but also cold streaks occur in modern aircraft engines due to deterioration-induced widening of cooling holes. This work investigates this effect in an experimental setup of a five-stage axial turbine. Cooling air is injected through the vane row of the fourth stage at midspan, and the vibration amplitudes of the blades in rotor stage five are measured with a tip-timing system. The highest injected mass flow rate is 2% of the total mass flow rate for a low-load operating point. The global turbine parameters change between the reference case without cooling air and the cold streak case. This change in operating conditions is compensated such that the corrected operating point is held constant throughout the measurements. It is shown that the cold streak is deflected in the direction of the hub and detected at 40% channel height behind the stator vane of the fifth stage. The averaged vibration amplitude over all blades increases by 20% for the cold streak case compared to the reference during low-load operating of the axial turbine. For operating points with higher loads, however, no increase in averaged vibration amplitude exceeding the measurement uncertainties is observed because the relative cooling mass flow rate is too low. It is shown that the cold streak only influences the pressure side and leads to a widening of the wake deficit. This is identified as the reason for the increased forcing on the blade. The conclusion is that an accurate prediction of the blade’s lifetime requires consideration of the cooling air within the design process and estimation of changes in cooling air mass flow rate throughout the blade’s lifetime.

Microstructure and properties of in situ TiC-reinforced Ti750 composite prepared by SPS

Purpose Titanium alloys and composites have proven to contain desirable properties for use at elevated temperatures. One such material is the Ti750 composite, which can be used at temperatures up to 750°C for a brief period. This paper aims the microstructure, phase compositions, apparent porosity and hardness of both sintered and heat-treated TiC reinforced Ti750 composites for consideration in aircraft engine design. Design/methodology/approach The fabrication of TiC-reinforced Ti750 composites was achieved through spark plasma sintering (SPS). To analyze the microstructure and X-ray diffraction, a scanning electron microscope (SEM) with model number S-3400N and a D8 advance model machine were used, respectively. The microhardness of the samples was measured using a Vickers hardness tester with model HV-1000. The research incorporated three solid solution treatments: 975°C/3 h/AC, 1,010°C/3 h/AC and 1,025°C/3 h/AC, along with a solid-solution aging treatment at 1,010°C/3 h/AC + 750°C/8 h/AC. Additionally, oxidation analysis was conducted on the samples. Findings The microstructures contained enhanced TiC and Ti5Si3 phases in the near a-Ti matrix. The microhardness of the sintered composite was over twice that of the matrix alloy, and its porosity was reduced by about 0.35%. The sample treated at 1,010°C/3 h/AC had the highest enhanced peaks and microhardness of 1,277.1 HV. After oxidation at 800°C for 100 h, the accumulated weight of the solid solution composite at 1,010 °C/3 h/AC was the lowest (3.0 mg.cm-2). The surface microstructure contained oxides of TiO2 and a spalling white area containing a small amount of Al2O3 and SiO2. Originality/value There is limited research on Ti-Al-Sn-Zr-Mo-Si-based TMCs using a combination of the SPS method. This study used SiCp as a reinforcement for the Ti750 matrix alloy. The consolidation of SiCp and Ti750 powders using the SPS method, heat treatment of the resulting TiC reinforced Ti750 composites and study of the microstructure and properties of the composites are not found in literature or under consideration for publication in any media.

Parameterization of H2SO4 and organic contributions to volatile PM in aircraft plumes at ground idle

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

A Time Series Prediction-Based Method for Rotating Machinery Detection and Severity Assessment

Monitoring the condition of rotating machinery is critical in aerospace applications like aircraft engines and helicopter rotors. Faults in these components can lead to catastrophic outcomes, making early detection essential. This paper proposes a novel approach using vibration signals and time series prediction methods for fault detection in rotating aerospace machinery. By extracting relevant features from vibration signals and using prediction models, fault severity can be effectively quantified. Our experimental results show that the proposed method has potential in early fault detection and is applicable to various types of bearing faults and the different statuses of these faults under complex running conditions, achieving very good generalization ability.