Commercial Aircraft Research Articles

A Multi-Model Architecture Based on Deep Learning for Longitudinal Available Overload Prediction of Commercial Subsonic Aircraft with Actuator Faults

Assessing the real-time longitudinal available overload onboard under fault conditions offers vital insights for the fault-tolerant reconfiguration and trajectory planning of commercial subsonic aircraft. After actuator failures in a commercial subsonic aircraft, its aerodynamic model undergoes changes. Traditional methods based on analytical models rely on precise aerodynamic models. However, due to the complexities of the flight environment and uncertainties in disturbances, establishing an accurate aerodynamic model after actuator failures is often challenging. Consequently, traditional methods can yield significant errors when evaluating the available overload under actuator faults. To address this, we introduce a multi-model architecture based on deep learning for the longitudinal available overload prediction of a commercial subsonic aircraft with actuator faults. For flight state data under different working conditions and different faults, Spearman correlation coefficient analysis and the gradient boosting decision tree (GBDT) algorithm are used to remove redundant feature parameters, thereby enhancing the training and prediction speed of the model while reducing the risk of overfitting. To meet prediction accuracy and speed demands, we employ the multi-layer perceptron (MLP) deep learning network to fully explore the environmental features, including uncertainties and disturbances, within the flight state, and the mapping relationships between the flight state and the available overload variations. We incorporate the light gradient boosting machine (LightGBM) and the categorical boosting (CatBoost) algorithms to enhance the model’s prediction speed and fuse it with a longitudinal available overload analytical model to elevate the model’s prediction accuracy, thereby achieving the real-time estimation of the commercial subsonic aircraft’s longitudinal available overload with actuator faults. The results demonstrate that the proposed method achieves a higher accuracy than traditional methods, with a relative error of less than 5%.

Simulated contrail-processed aviation soot aerosols are poor ice-nucleating particles at cirrus temperatures

Abstract. Aviation soot surrogates processed in contrails are believed to become potent ice nuclei at cirrus temperatures. This is not verified for real aviation soot, which can have vastly different physico-chemical properties. Here, we sampled soot particles from in-use commercial aircraft engines and quantified the effect of contrail processing on their ice nucleation ability at T< 228 K. We show that aviation soot becomes compacted upon contrail processing, but that does not change their ice nucleation ability in contrast to other soot types. The presence of H2SO4 condensed in soot pores, the highly fused nature of the soot primary particles and their arrangement are what limit the volume of pores generated upon contrail processing, in turn limiting sites for ice nucleation. Furthermore, we hypothesized that contrail-processed aviation soot particles emitted from alternative jet fuel would also be poor ice-nucleating particles if their emission sizes remain small (< 150 nm).

Spot-checking machine learning algorithms for tool wear monitoring in automatic drilling operations in CFRP/Ti6Al4V/Al stacks in the aircraft industry

In aircraft manufacturing, where diverse materials, including Carbon Fiber-Reinforced Plastics (CFRP), aluminum, and titanium alloys, are employed, the assembly process heavily relies on creating thousands of holes. These holes accommodate bolts and rivets, facilitating the secure interlocking of structural components within the aircraft fuselage. The proliferation of sensor systems in this domain has led to a substantial increase in data generation during the hole-making process, offering a compelling opportunity to optimize the production system. In this context, this article is dedicated to harnessing the data collected from the production system of a commercial aircraft to refine the assembly process, with a specific focus on reducing consumable costs. The primary approach involves developing a real-time Tool Wear Monitoring System by comparing the performance of Linear Regression, Lasso Regression, Ridge Regression, k-Nearest Neighbors, Support Vector Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting Machine Learning models. Using a scale of the general drill condition as an outcome, the Gradient Boosting Regressor has shown outstanding results. Notably, the residuals consistently exhibited zero-centered errors in training and test sets. However, it suggests that further enhancements are needed to surpass human-level performance in predicting tool conditions because of the quality and quantity of available data.

First Results of Airborne GNSS Radio Occultation Sounding From Airbus Commercial Aircraft

AbstractThe lack of high vertical resolution atmospheric thermodynamic structure observations inside or near major weather events impedes our understanding of physical processes and their predictability in numerical weather prediction (NWP) models. Airborne Global Navigation Satellite System (GNSS) radio occultation (airborne radio occultation [ARO]) has proven to be a viable remote sensing option to offer dense soundings near flight tracks. The global fleet of commercial aircraft already equipped with GNSS receivers could be leveraged to produce an unprecedented number of ARO soundings along global flight paths. Eleven cases of atmospheric bending angle and refractivity profiles were successfully retrieved and compared with the colocated European Center for Medium‐Range Weather Forecasting global reanalysis data. Good quality measurements are obtained with median refractivity differences less than 1% in the middle and upper troposphere, between 5.5 and 11.5 km. Given the use of aircraft data (e.g., Aircraft Meteorological DAta Relay) for data assimilation, incorporating ARO profiles would be a valuable addition, further enhancing the accuracy of aviation and weather forecasts.

Impact characteristics of S2-glass fibre/FM94-epoxy composites under high and cryogenic temperatures: Experimental and numerical investigation

The aerospace industry uses glass fibre reinforced polymer (GFRP) composites to manufacture structural and non-structural parts of an aircraft as they possess superior strength to weight ratio and exceptional corrosion resistance. Commercial aircraft operate in a very wide temperature ranges from −54 to 55 °C. Potential GFRP laminates are susceptible to impact during aircraft operation, and the temperature at impact governs the nature of damage and failure mechanisms. As a result, the current study focuses on examining how aeronautical GFRP composites behave in various temperature environments that are encountered during high- and low-altitude operations. Using S2-glass fibre/FM94-epoxy unidirectional prepreg, GFRP plates were created. Drop weight impact tests were conducted at ambient (25 °C), high (50, 75, 100 °C), and low (−25, −55 °C) temperatures, as well as at various impact energies (75, 150, 225 J). The damages were assessed visually, along with their sizes. Each testing scenario's impact parameters, including the impact load, deflection, and energy absorption, were also examined. In Abaqus/Explicit, a coupled temperature-displacement numerical model was created to predict the onset of stress and damage. According to experimental findings, GFRP plates are stiffer and show less apparent damage at cryogenic temperatures (∼15−34 % lower displacement) than they do at other temperatures. Furthermore, it was observed that the matrix softens at high temperatures, showing larger damaged area at entry but with less obvious damage and increasing energy absorption, while semi-perforation occurred under cryogenic temperatures at entry with smaller damaged area. A strong correlation is shown between the experimental and FE data, confirming the capability of FE models to predict impact damage and deflections at different temperatures in the future.

High performance ultrasonic vibration assisted Wire-arc directed energy deposition of Invar alloy

As the utilization of composite materials flourishes in commercial aircraft, the use of Invar alloy with low coefficient of thermal expansion (CTE) for fabricating composite molds has gained prominence. The large-size composite molds can be fabricated by Wire-arc Directed Energy Deposition (Wire-arc DED), due to its high deposition efficiency and ability to form optimized topologies. However, the Wire-arc DED Invar components still exhibit heterogeneous microstructure, low strength and non-tunable CTE. To address these challenges, this study explores the integration of an ultrasonic energy field during the deposition process to systematically investigate its effects on microstructural evolution, mechanical properties and CTE of Invar alloy. The results reveal that the ultrasonic vibration-assisted deposition significantly refines the grain structure, resulting in a decrease of 81.04 % in grain size compared to the reference state. Moreover, the components fabricated by ultrasonic vibration assisted Wire-arc DED exhibit exceptional properties, with a yield strength (YS) of 408 ± 11.37 MPa, ultimate tensile strength (UTS) of 645 ± 7.61 MPa, and elongation (EL) of 31.3 %. Additionally, the correlation between grain size and CTE was established. The effectiveness of introducing an ultrasonic energy field in improving the mechanical properties and modulating the CTE of the components is further validated by rigorous theoretical calculations. This research provides a promising way to fabricate high performance Invar alloy composite molds.

Ground Strength Test Technique of Variable-Camber Wing Leading Edge.

Morphing wing technology is crucial for enhancing the flight performance of aircraft. To address the monitoring challenges of full-scale variable-camber leading edges under flight conditions, this study introduces a ground-based strength testing technique aimed at precisely evaluating the deformation patterns and structural strength during actual operation. Firstly, the motion characteristics of the variable-camber leading edge were analyzed using numerical simulation based on kinematic theory. Secondly, a tracking loading test rig was designed and constructed to simulate the actuated deformation and aerodynamic loads of the leading edge. Next, mechanical boundary numerical simulation was then utilized to predict the motion trajectories of loading points on the upper and lower wing surfaces, and a multi-point coordinated control system was developed to achieve accurate experimental control. Finally, a multi-sensor iterative method was employed to ensure loading precision throughout the testing process. A case study was conducted using a leading edge test piece from a specific commercial aircraft. The results indicated that in the motion test of the variable-camber leading edge, the average error of the deflection angle was 4.59%; in the strength test, the average errors in the magnitude and direction of the applied load were 0.54% and 0.24%, respectively. These findings validate the effectiveness of the proposed technique in simulating the flight conditions of deforming wings and accurately obtaining the leading edge shape change curve, deformation accuracy curve, and strain curves of the upper and lower wing surfaces under deflection angles. Furthermore, this paper compares the deformation accuracy of different testing methods under test conditions, providing scientific evidence and technical support for the testing and evaluation of variable-camber leading edges.

Strategies for Increasing Passenger Aircraft Capacity: A Comprehensive Review

This review explores various strategies to increase the passenger and cargo capacity of aircraft while maintaining safety and efficiency. The study focuses on four main areas of modification: wing structures, center of gravity management, landing gear upgrades, and engine enhancements. We examine the challenges associated with increasing aircraft capacity, including structural limitations, performance impacts, and fuel efficiency concerns. The paper provides insights into potential design modifications that could allow for increased load-carrying capacity in commercial aircraft, potentially leading to improved operational efficiency and profitability in the aviation industry.

4D printed NiTi variable-geometry inlet for aero engines

ABSTRACT In modern aerospace engineering, the demand for innovative and adaptable solutions is paramount. This study focuses on enhancing aircraft engine components, specifically variable-geometry inlets, to meet evolving aviation technology requirements. We utilise custom Ni51Ti alloy powder to create 4D printed variable-geometry inlets. A comprehensive examination of thermal history and residual stress reveals differences in the behaviour of NiTi alloys at various points of the 4D printed inlet. After post-treatment, the printed inlet demonstrates stable two-way shape memory effects, undergoing adaptive deformation with temperature changes, mimicking the operational conditions of a commercial aircraft. Differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) tests confirm that the 4D printed NiTi alloys maintain very stable phase transformation temperatures and two-way shape memory performance. Fluid dynamics simulations further reveal that the variable-geometry inlet design is potentially more efficient in certain operational scenarios with a total pressure recovery factor of 0.9919.

Adaptive control method for morphing trailing-edge wing based on deep supervision network and reinforcement learning

The morphing trailing-edge wing can adaptively change its shape according to the different flight conditions, improving cruising efficiency. The control of the morphing trailing edge is a high-dimensional variable and nonlinear problem, facing the difficulty of the high training cost and the difficulty in real-time continuous control. To overcome these difficulties, a novel adaptive control method based on the couple of Deep Supervision Net (DSN) and Deep Reinforcement Learning (DRL) is proposed to solve a control problem of the morphing trailing-edge wing. DSN uses distributed loads to improve aerodynamic prediction accuracy compared to the traditional surrogate models, while DRL conducts real-time control and improves the performance at non-design points compared to the traditional multi-design points optimization. After control, the lift-to-drag ratio at the start point can be increased by 1.36%, and can be increased by 2.61% at the end point tested on a commercial wide-body aircraft model.

Aviation noise in the United States: The current state of FAA research on community response

While today's civilian aircraft fleet is quieter than at any time in the history of jet-powered flight, the Federal Aviation Administration (FAA) continues to receive complaints about aircraft noise affecting communities across the United States. Additionally, the FAA's Neighborhood Environmental Survey (NES) has shown that community response to aviation noise in the United States has changed and the dose-response relationship between noise and annoyance such as the one represented by the Shultz Curve is no longer representative of communities' lived experiences. This paper will examine the current aviation noise environment in the United States and how the nature of aviation noise and the characteristics of communities that raise significant noise concerns in the United States have changed since the start of widespread use of commercial jet-powered aircraft in the 1960s. This paper will also explore the changing nature of community response to aviation noise based on the FAA's latest research findings on aviation noise and annoyance as detailed in follow-on analyses to the NES.

Data‐based nonlinear learning control for aircraft trajectory tracking via Gaussian process regression

AbstractIn this article, a new data‐based iterative learning control (ILC) algorithm is proposed via Gaussian process regression (GPR) to accomplish the trajectory tracking objective of aircraft subject to completely unknown dynamics and strong nonlinearities. The nonlinear system input–output relationship of the unknown aircraft is formulated through GPR by leveraging historical data, based on which an optimal ILC framework is established. The monotonic convergence analysis of the GPR‐based ILC is explored such that high‐precision tracking tasks can be accomplished without prior model knowledge. Simulation tests are further conducted on a commercial aircraft performing a continuous climb operation to illustrate the effectiveness of the GPR‐based ILC approach.

Jet engines, wind turbines, and AI: community reactions to new sounds

When jet engine-powered commercial aircraft started flying in and out of airports in the mid-20th century, surrounding communities-often in rural areas-were exposed to unprecedented sound levels. A straight line can be drawn from the reactions of those communities to today's Federal Aviation Administration (FAA) airport acoustical requirements. Community reactions to utility-scale wind energy facilities in the late 20th and early 21st centuries led to a bevy of local legislation around the United States, some of which has been insightful and helpful. More recently, society's ever-increasing demand for bits and bytes is introducing suburban areas to data centers-campuses of multi-story buildings requiring access to high volumes of cool air, fast Internet connections, and megawatts of electricity. Community reactions to the resulting environmental sound levels are again driving regulation and legislation. In all these cases, community reactions have driven the narrative. Instead of responding to community reactions, the noise control engineering community and their clients could be more proactive-the professionals could lead the narrative. Ideas about how to achieve this level of proactivity are discussed.

How well can persistent contrails be predicted? An update

Abstract. The total aviation effective radiative forcing is dominated by non-CO2 effects. The largest contributors to the non-CO2 effects are contrails and contrail cirrus. There is the possibility of reducing the climate effect of aviation by avoiding flying through ice-supersaturated regions (ISSRs), where contrails can last for hours (so-called persistent contrails). Therefore, a precise prediction of the specific location and time of these regions is needed. But a prediction of the frequency and degree of ice supersaturation (ISS) on cruise altitudes is currently very challenging and associated with great uncertainties because of the strong variability in the water vapour field, the low number of humidity measurements at the air traffic altitude, and the oversimplified parameterisations of cloud physics in weather models. Since ISS is more common in some dynamical regimes than in others, the aim of this study is to find variables/proxies that are related to the formation of ISSRs and to use these in a regression method to predict persistent contrails. To find the best-suited proxies for regressions, we use various methods of information theory. These include the log-likelihood ratios, known from Bayes' theorem, a modified form of the Kullback–Leibler divergence, and mutual information. The variables (the relative humidity with respect to ice, RHiERA5; the temperature, T; the vertical velocity, ω; the divergence, DIV; the relative vorticity, ζ; the potential vorticity, PV; the normalised geopotential height, Z; and the local lapse rate, γ) come from ERA5, and RHiM/I, which we assume as the truth, comes from MOZAIC/IAGOS (Measurement of Ozone and Water Vapour on Airbus In-service Aircraft/In-service Aircraft for a Global Observing System; commercial aircraft measurements). It turns out that RHiERA5 is the most important predictor of ice supersaturation, in spite of its weaknesses, and all other variables do not help much to achieve better results. Without RHiERA5, a regression to predict ISSRs is not successful. Certain modifications of RHiERA5 before the regression (as suggested in recent papers) do not lead to improvements of ISSR prediction. Applying a sensitivity study with artificially modified RHiERA5 distributions points to the origin of the problems with the regression: the conditional distributions of RHiERA5 (conditioned on ISS and non-ISS, from RHiM/I) overlap too heavily in the range of 70 %–100 %, so for any case in that range, it is not clear whether it belongs to an ISSR or not. Evidently, this renders the prediction of contrail persistence very difficult.

The applicability of Zermelo's equation to indirect and direct optimization of commercial aircraft flights

This paper focuses on the applicability of Zermelo's equation within the context of 3D commercial aircraft trajectory optimization problems. The associated optimal control problem includes two singular controls, specifically aerodynamic path angle and throttle setting, and one regular control, specifically heading angle. Using Pontryagin's maximum principle and the direct adjoining method, we show that the optimal heading angle is defined by Zermelo's equation. The significance of the presented analysis is that Zermelo's equation holds for 3D commercial aircraft flights, even in the presence of standard state-inequality constraints and a more general objective function. With the help of Zermelo's equation, the control problem is initially analyzed for a 3D time-optimal climb-phase scenario, which is solved by an indirect approach. Through the analysis of the switching function, we demonstrate the dependency of the optimal aerodynamic path angle on the optimal heading angle. Next, by tackling a 3D time-fuel-optimal free-routing flight, solved by a direct approach, we show that Zermelo's equation can help reduce the overall dimension of the associated nonlinear programming. To successfully handle the initial guess in both indirect and direct problems, it is proposed a diminutive nonlinear programming technique as a fast and robust initializer. This simple-yet-effective initializer provides sufficient information about the optimal controls and state dynamics required for initializing a direct optimization, as well as the optimal switching times and co-states required for initializing an indirect optimization.

Penerapan Strategic Approach Pada Kegiatan Company Visit PT. Garuda Maintenance Facility Aero Asia Tbk

PT Garuda Maintenance Facility Aero Asia Tbk (GMF) is a company engaged in aircraft maintenance, repair and overhaul (MRO). GMF has a business unit consisting of comprehensive aircraft maintenance, such as commercial aircraft maintenance, military aircraft maintenance and industrial services. At this company, the author took part in practical work activities in the Corporate Communications & CSR unit which is under the auspices of the Corporate Secretary & Legal service which carries out the public relations function at GMF. In this report, the author will explain the application of 4 strategic approaches to company visits held for GMF stakeholders using interview, observation and literature study methods. The stakeholder aspects that will be explained from the perspective of Grunig's 4 Linkage Model are functional linkage, enabling linkage, normative linkage, and diffused linkage. PT GMF approaches stakeholders using 4 strategic approaches, namely assertive, defensive, collaborative and responsive.

Nenadzirana praktična analiza ponašanja aviona A320 i B738 provedena u međunarodnoj zračnoj luci Sultan Hasanuddin

The purpose of this research was to look at the behavior of two well-known commercial aircraft types in Indonesia (the A320 and the B738) during the take-off phase. This was done to provide new information in the field of aviation, particularly flight safety. Observations were made at Sultan Hasanuddin International Airport by observing aircraft ADS-B data, which defines the behavior of the flight pattern. This ADS-B data is the subject of data analysis, which will subsequently be taught to the machine (computer) so that it can recognize the pattern and construct clusters. The purpose of this study is to utilize unsupervised learning, specifically K-Means clustering, to categorize and identify patterns in unlabeled ADS-B data obtained from AERO-TRACK. To prepare the raw data and create a dataset, data analysis techniques were employed. The machine learning model generates three distinct clusters: cluster 1 represents aircraft take-off on two-thirds of the runway, cluster 2 represents aircraft take-off on the entire runway, and cluster 3 represents aircraft take-off on one-third of the runway. The elbow method is utilized to analyze and interpret the three clusters produced by the model. An interesting observation is that the B738 aircraft dominate in all three clusters, while the A320 aircraft dominate in clusters 1 and 3. Notably, in cluster 2, there is a significant number of commercial planes taking off, accounting for 145 out of 628 flights. Based on the observed data spanning 91 days (September 26 to December 26, 2022), there is a 23% probability of runway excursion (overshooting the runway) in this cluster. Additionally, the research reveals that A320 aircraft demonstrate a safe zone take-off rate of 87%, whereas the B738 aircraft demonstrate a rate of 70.5%. These findings, derived from the analysis of ADS-B data such as GPS-Altitude and Coordinate, are intended to serve as valuable knowledge for aviation authorities, aviation users, and other stakeholders in the aviation industry.

Study on the wake vortex behavior behind a commercial aircraft during take-off and landing

Study on the wake vortex behavior behind a commercial aircraft during take-off and landing

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.

Synthesis, characterization, and CO sensing properties of NiO thin film for commercial aircraft applications

Abstract The detection of combustion gases such as carbon monoxide (CO) and fuel leaks is a concern in regard to aeronautical safety and has led to the development of semiconductor sensors. The focus of this work is the detection of CO within commercial aircraft using a thin film of nickel oxide (NiO) as the sensing element. The NiO phase was synthesized using the sol-gel method using a modification of the procedures proposed by N. Talebian and B. Pejova. Microstructural characterization of the NiO thin film was performed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM). Optical analysis was carried out using diffuse reflectance (UV-VIS) spectroscopy. The results showed that the NiO thin film was homogeneous and composed of nodular nanoparticles with size between 10 and 15 nm and thickness of 75 nm. The band gap value was 3.3 eV, which is consistent with p-type semiconductors. The sensitivity of the devices was determined by means of the static environment method. The best operation temperature was 200°C for the device with 100-µm spacing between tracks, which gave a response time of 165 seconds and recovery time of 212 seconds according the results of a flow-through method. The device with this track spacing is ideal for CO detection at 50 ppm, at which the first symptoms of intoxication appear in humans inside commercial aircraft.