Aircraft Landing Research Articles

Integrating Electric Vertical Takeoff and Landing Aircraft into Public Airspace: A Scenario Study

The successful deployment of Advanced Air Mobility (AAM) requires the safe integration of electric vertical takeoff and landing (eVTOL) aircraft in non-segregated airspace. However, a lack of practical deployment demonstrations can slow the regulatory progress. This study presents a practical deployment framework for the integration of eVTOL aircraft into public airspace, in accordance with the Federal Aviation Administration (FAA) special airworthiness criteria. This study focused on a corporate use case to provide a comprehensive scenario for enhancing intra-company mobility. The scenario supports early implementation and demonstration goals by removing reliance on air traffic management systems or air traffic controller separation services. The practical deployment framework included facility selection, route planning, terrain mapping, collision avoidance, aircraft selection, infrastructure requirements, vertiport placement, electric chargers, ground handling, operational staff, and cost estimates. Planners can use the framework as a template to inform the implementation of real-world AAM services using eVTOL aircraft.

Performance Evaluation of an H-VTOL Aircraft with Distributed Electric Propulsion and Ducted-Fans Using MIL Simulation

This paper deals with the problem of increasing the energy efficiency of a hybrid vertical take-off and landing aircraft. To this end, an innovative aerial vehicle was developed, featuring a distributed electrical propulsion system with ducted-fan rotors. To compare and analyze the effectiveness of the proposed propulsion system, two configurations with a different number of ducted-fan rotors were examined: a four-rotor configuration and a six-rotor configuration. The mathematical model of the four-rotor configuration was derived using the Newton–Euler formalism, allowing the design and implementation of a control strategy for conducting model-in-the-loop simulations. These simulations enabled the evaluation and analysis of the performance of the proposed propulsion system, where the numerical results demonstrated the functionality of both designs and showed that, during the multirotor flight, the configuration with six rotors increased its energy efficiency by up to 11%, providing higher vertical lift with the same power consumption. This was achieved by distributing its weight among a higher number of engines. The incorporation of two additional ducted fans increased the weight and the drag of the six-rotor configuration, resulting in a low augmentation in power consumption of 1%. Finally, this caused a decrease in airspeed by up to 4% during the cruise speed phase.

Identifying Anomalous Behavior in Aircraft Landing Trajectory Using a Bayesian Autoencoder

Anomalous behavior during the aircraft landing phase can significantly increase the probability of adverse events. Automated anomaly detection during the landing phase can help aviation safety-related organizations to efficiently detect anomalous behavior and consider mitigation strategies. This paper develops a Bayesian autoencoder neural network model to identify anomalous behavior in landing trajectories by reconstructing the flight data because the reconstruction error is larger for anomalous flights. Different loss functions, such as Huber loss, mean squared error loss, and least trimmed squares are investigated to construct the Bayesian autoencoder model; and their performances are compared using different measures: the mean of the reconstruction error, the standard deviation of the reconstruction error, and both the mean and standard deviation of the reconstruction error. Different loss function-based models show differences in performance, depending on which measure is used for anomaly detection; among all the options considered, one of the Huber loss options appears to give the best performance, as indicated by the F1 score. Furthermore, the mean and standard deviation of the reconstruction error for a single flight are used to identify the time of occurrence and the flight parameters related to anomalous behavior.

EVTOL Tilt-Wing Aircraft Design under Uncertainty Using a Multidisciplinary Possibilistic Approach

Recent development in Electric Vertical Take-off and Landing (eVTOL) aircraft makes it a popular design approach for urban air mobility (UAM). When designing these configurations, due to the uncertainty present in semi-empirical estimations, often used for aerodynamic characteristics during the conceptual design phase, results can only be trusted to approximately 80% accuracy. Accordingly, an optimized aircraft using semi-empirical estimations and deterministic multi-disciplinary design optimization (MDO) approaches can be at risk of not being certifiable in the detailed design phase of the life cycle. The focus of this study was to implement a robust and efficient possibility-based design optimization (PBDO) method for the MDO of an eVTOL tilt-wing aircraft in the conceptual design phase, using existing conventional designs as an initial configuration. As implemented, the optimization framework utilizes a deterministic gradient-based optimizer, run sequentially with a possibility assessment algorithm, to select an optimal design. To achieve this, the uncertainties which arise from multi-fidelity calculations, such as semi-empirical methods, are considered and used to modify the final design such that its viability is guaranteed in the detailed design phase. With respect to various requirements, including trim, stability, and control behaviors, the optimized eVTOL tilt-wing aircraft design offers the preferred results which ensure that airworthiness criteria are met whilst complying with predefined constraints. The proposed approach may be used to revise currently available light aircraft and develop eVTOL versions from the original light aircraft. The resulting aircraft is not only an optimized layout but one where the stability of the eVTOL tilt-wing aircraft has been guaranteed.

The Development of an Advanced Air Mobility Flight Testing and Simulation Infrastructure

The emerging field of Advanced Air Mobility (AAM) holds great promise for revolutionizing transportation by enabling the efficient, safe, and sustainable movement of people and goods in urban and regional environments. AAM encompasses a wide range of electric vertical take-off and landing (eVTOL) aircraft and infrastructure that support their operations. In this work, we first present a new airspace structure by considering different layers for standard-performing vehicles (SPVs) and high-performing vehicles (HPVs), new AAM services for accommodating such a structure, and a holistic contingency management concept for a safe and efficient traffic environment. We then identify the requirements and development process of a testing and simulation infrastructure for AAM demonstrations, which specifically aim to explore the decentralized architecture of the proposed concept and its use cases. To demonstrate the full capability of AAM, we develop an infrastructure that includes advanced U-space services, real and simulated platforms that are suitable for future AAM use cases such as air cargo delivery and air taxi operations, and a co-simulation environment that allows all of the AAM elements to interact with each other in harmony. The considered infrastructure is envisioned to be used in AAM integration-related efforts, especially those focusing on U-space service deployment over a complex traffic environment and those analyzing the interaction between the operator, the U-space service provider (USSP), and the air traffic controller (ATC).

Prescribed performance control for uncertain underactuated systems via constraint‐following with application to a PVTOL aircraft

SummaryGuaranteeing prescribed transient and steady state performance (TSSP) for mechanical systems is of great significance to their motion control, motivating the prescribed performance control (PPC). This study investigates the PPC for uncertain underactuated systems. We consider time‐varying (possibly fast), bounded uncertainty including large mismatched portions, however the bound is unknown. The control design is implemented via constraint‐following, that is, rendering the system to follow a series of servo constraints. We hence impose the prescribed TSSP on the constraint‐following error. An approximation‐free adaptive robust PPC based on a state transformation technique is designed, achieving approximate constraint‐following and guaranteeing the prescribed TSSP to be satisfied. It employs an adaptive law to online estimate the unknown bounds for uncertainty compensation. It does not assume sufficiently small mismatched uncertainties, hence admitting large mismatched uncertainties. The stability of the controlled underactuated systems is sustained by rigorous proofs. By the proposed approach, the PPC of a planar vertical take‐off and landing aircraft is explored, which also illustrates the effectiveness of the approach.

In-situ microstructure monitoring during tempering of quenched AISI4340 steels using a high temperature electromagnetic sensor

AISI 4340 steels are used in applications where high strength and toughness are required, for example, in power transmission gears and shafts, aircraft landing gear and other structural components. The required mechanical properties are achieved by hot rolling and quenching and tempering (Q&T) heat treatment. For optimum process efficiency and quality control, it is desirable to monitor the microstructural development during tempering in situ, as the microstructure is directly linked to the mechanical properties. Electromagnetic (EM) sensors have been shown to be sensitive to steel microstructural changes due to the associated changes in magnetic and electrical properties. This study reports on using a high-temperature laboratory EM sensor that operates inside a furnace to assess microstructural changes that occur during tempering at 250°C for AISI4340 steel samples. The sensor operates at low magnetic field and low frequency where the signal is affected by changes in magnetic permeability due to microstructural changes. The microstructure and hardness have been characterised to correlate to the measured EM sensor signal. The results have shown that the EM sensor signal is affected by temperature and microstructural changes on the slow heating to the tempering temperature and then is sensitive to the continuing microstructure changes that occur during the isothermal hold. The results show the potential for in-situ EM sensor monitoring during heat treatment.

Air-traffic Flow Prediction with Deep Learning: A Case Study for Diyarbakır Airport

Aviation industry develops rapidly. So the continuous growth of the aviation, accurate predictions play a crucial role in managing air traffic and optimizing airport operations. The prediction process involves various factors such as weather conditions, airport traffic, flight schedules, and historical data. Advanced techniques like machine learning contribute to enhancing the accuracy of predictions. In this context, air traffic data belonging to Diyarbakır province were utilized to predict the number of arrival aircraft to the airport using both traditional Autoregressive (AR) model and deep learning architecture, specifically the stacked Long Short-Term Memory (LSTM) model. The results indicate that the stacked LSTM model outperformed the AR model in terms of air traffic estimation. The AR model had a quite poorly MSE value of 48043.35 and an RMSE value of 219.18, while the stacked LSTM model achieved a significantly higher MSE value of 0.03 and an RMSE value of 0.17. The lower MSE values obtained by the stacked LSTM model indicate its ability to make more accurate predictions compared to the AR model. The stacked LSTM model's predictions were closer to the actual values, resulting in a more realistic estimation of air traffic. Accurate predictions enable efficient resource management, passenger planning, and airport security measures. Continuous efforts in predicting aircraft landings are necessary for the effective functioning of the aviation industry. In this study highlights the importance of predicting the number of aircraft landings at airports.

Effect of Relative Jet Temperature in Supersonic Dual-Impinging Jets

Short takeoff and vertical landing aircraft configurations involve multiple jets that impinge onto the deck surface in tandem and lead to several adverse effects. This study reports on the experimental characterization of supersonic dual-impinging jets by systematically varying their relative jet temperature. A sonic converging and Mach 1.5 converging–diverging (CD) nozzles are employed. The expansion ratio of the converging nozzle is maintained at 0.96, 1.19, and 1.59, and the CD nozzle is operated at a fixed nozzle pressure ratio of 3. The temperature ratio of the jet from the CD nozzle is varied from 1.0, 1.3, and 1.7. For a fixed momentum of the jet pair, an increase in jet temperature intensified the nearfield noise and unsteadiness on the impingement surface. At short impingement heights, resonance in the heated jet was the primary source of unsteadiness. At a fixed impingement height, an increase in jet temperature led to a systematic increase in tonal frequency, while jet instability mode shapes were retained. Furthermore, the mean flowfield of sonic jet and fountain regions remain unaffected, and an increase in supersonic jet velocity is observed. This is also accompanied by an increase in unsteadiness in the fountain upwash.

Linear and Nonlinear Models for Drop Simulation of an Aircraft Landing Gear System with MR Dampers

In this study, our focus is on the drop test simulation of an MR (Magnetorheological) damper-based main landing gear (MRMLG), aiming to explore multi-degree-of-freedom (DOF) dynamic models during aircraft landing. Three different 6-DOF dynamic models are proposed in this work, and their drop performances are compared with results achieved by commercial software. The proposed models include a nonlinear aircraft model (NLAM); a linearized approximated aircraft model (LAAM) linearizing from the nonlinear equations of motion in NLAM; and a fully approximated aircraft model (FAAM) which linearizes the MRMLG’s strut force model. In order to evaluate the drop performance of the aircraft landing gear system with MR dampers, a 7-DOF aircraft model incorporating the nonlinear MRMLG was formulated using RecurDyn. The principal comparative parameters are the coefficient of determination (R2) for the system response of each model with the RecurDyn model and root mean square error (RMSE), which is the ensemble of CG displacement data for each model. In addition, the ensemble of time series data is created for diverse drop scenarios, providing valuable insights into the performance of the proposed drop test models of an aircraft landing gear system featuring MR dampers.

RECOMMENDATIONS FOR THE SELECTION OF MODERN BUILDING MATERIALS FOR USE IN THE RESTORATION OF HARD AIRFIELD SURFACES IN CONDITIONS OF ARMED AGGRESSION

In the course of hostilities (especially of high intensity), the Armed Forces of Ukraine will inevitably suffer certain losses of airfields. As a result of the bombing, the hard airfield surfaces were damaged, which hinders the reliable operation of aircraft. As a result, hard airfield surfaces needed restoration, and for this, materials with very strict requirements must be used. In recent years, modern modified structural materials have been offered in Ukraine, which make it possible to significantly reduce the recovery time: if the work is properly organized, the time can be no more than 7–10 days, provided that the required strength values are met characteristics – in compression, and in tension they are fully achieved. The consequence is a slight interruption of flights at the airfield, which is very relevant in the conditions of hostilities.
 The main factors affecting the destruction of hard airfield surfaces in peacetime are natural influences, as well as loads during take-offs and landings of aircraft. Among the operational factors, mechanical effects, as well as the force and the temperature effects of the gas jet of aircraft jet engines should be singled out. If any damage to the runway surface, measures must be taken to eliminate it immediately, otherwise there is a risk of accidental damage and closure of the runway for long-term repairs. But in case of detection of malfunctions that threaten the safety of flights, they are eliminated immediately before the start of flights. This is a very important point in the operation of hard airfield surfaces: it determines the need to use such materials for the restoration of runways, which in the shortest possible time acquire the necessary values of the operational characteristics of hard concrete surfaces.
 A wide variety of materials can be used for repair work, but if there are two limitations – the period before the start of operation and the duration of the work, then it is most appropriate to use modern materials. Such repair materials must meet the requirement: the strength of the materials must provide a load-bearing capacity no less than the existing airfield pavements and meet the requirements.

A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction.

The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed. First, an MCA (multiple correlation analysis) method is proposed to select key features. Second, a heterogeneous multilearner integration framework is proposed, which makes use of different base learners. Third, an MLPSA (multilayer perceptron with self-attention) model is proposed to adaptively capture the data distribution and adjust the weights of each base learner. Finally, the excellent prediction performance of the proposed MCA-MLPSA is validated by a series of experiments on the landing gear data.

Assessing wind field characteristics along the airport runway glide slope: an explainable boosting machine-assisted wind tunnel study

Aircraft landings are especially perilous when the wind is gusty near airport runways. For this reason, an aircraft may deviate from its glide slope, miss its approach, or even crash in the worst cases. In the study, we used the state-of-the-art glass-box model, the Explainable Boosting Machine (EBM), to estimate the variation in headwind speed and turbulence intensity along the airport runway glide slope and to interpret the various contributing factors. To begin, the wind field characteristics were examined by developing a scaled-down model of Hong Kong International Airport (HKIA) runway as well as and the surrounding buildings and complex terrain in the TJ-3 atmospheric boundary layer wind tunnel. The placement of probes along the glide slope of the model runway aided in the measurement of wind field characteristics at different locations in the presence and absence of surrounding buildings. Next, the experimental data was used to train the EBM model in conjunction with Bayesian optimization approach. The counterpart black box models (extreme gradient boosting, random forest, extra tree and adaptive boosting) as well as other glass box models (linear regression and decision tree) were compared with the outcomes of the EBM model. Based on the holdout testing data, the EBM model revealed superior performance for both variation in headwind speed and turbulence intensity in terms of mean absolute error, mean squared error, root mean squared error and R-square values. To further evaluate the impact of different factors on the wind field characteristics along the airport runway glide slope, the EBM model allows for a full interpretation of the contribution of individual and pairwise interactions of factors to the prediction results from both a global and a local perspective.

Data-Driven Diffraction Loss Estimation for Future Intelligent Transportation Systems in 6G Networks

The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users’ locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems.

Flight Control Method Using Neural Network in Prediction for Suppressing Ship Airwake Impact in Carrier Landing

Ship airwake is an important factor affecting the accurate carrier landing of carrier-based aircraft. To suppress the effects from airwake, this paper presents a flight control method using neural network prediction. Different from most studies focusing on improving the adaptability of the flight control methods, the emphasis of the research is placed on modelling the effects of the ship airwake in the carrier landing environment pertinently and reflecting the effects in the control model. From a large amount of carrier landing flight data, the impact of airwake on the landing control can be extracted and summarized into models based on neural network algorithm. Then the airwake impact in the next period of time can be predicted. A flight control law is designed combining the predicting model of airwake impact with model predictive control. The simulation demonstrated that the presented method can reduce the trajectory deviation influenced by airwake and decrease the touchdown dispersions.

Wind Tunnel Testing and Analysis of a Rigid, Variable Speed Rotor for eVTOL Applications

Many electric vertical take-off and landing (eVTOL) aircraft intended for the urban air mobility (UAM) market are currently being designed with multirotor configurations using variable speed fixed-pitch, rigid rotors for lift. These types of rotors, which are similar in construction to general aviation airplane propellers, are simpler than helicopter rotors and have no moving parts in the rotating frame. This paper discusses wind-tunnel testing of a full-scale, UAM multirotor size, fixed-pitch, rigid rotor with a focus on vibratory blade loads and on the ability to predict these loads with comprehensive analysis. Test results show that vibratory loads are very high, with peak-to-peak magnitudes up to three times greater than the steady component. Correlation of test data to comprehensive analysis using geometrically exact composite beam structural elements and dynamic inflow wake modeling captures the trends in the steady and vibratory loads though it underpredicts the magnitudes; an expected result with the level of wake modeling fidelity used. The paper also discusses the physical sources of the observed vibratory loads and suggests potential options for mitigating their magnitude.

Joby Completes Submission of Stage Three Certification Plans to the FAA

On July 06, 2023, Joby Aviation, Inc., a company developing electric vertical take-off and landing (eVTOL) aircraft for commercial passenger service, announced it had submitted all of its Certification Plans to the Federal Aviation Administration (“FAA”), marking further progress toward completing the third of five stages required to certify its aircraft for commercial use.

A fuzzy control method with the optimized intelligent weight function for directional control during aircraft autoland rollout

Aircraft landing rollout process is a complex and high non-linearity process. The mutual effects of tire dynamics, aerodynamics and multi-body dynamics should be taken into considerations. It is difficult to be accurately expressed in mathematics. Aiming at obtaining a good directional control capability during aircraft autoland rollout, a comprehensive simulation system was constructed including all those three aspects above, and a fuzzy control method with intelligent weight function was adopted which is appropriate for systems difficult to be modeled exactly. In the paper’s work, the intelligent weight function was optimized by the power function for the first time. By adjusting the exponential k, the fuzzy control rules can be conveniently and reasonably optimized. Through a set of simulations without crosswind, the optimal k was obtained. Finally, the simulation tests under conditions of 20 kn crosswind with ±5° original drift angles were conducted with the system configured by the optimal k. Accroding to the results, the optimized fuzzy controller can give a good directional control performance.

Environmental inefficiencies for arrival flights at European airports.

In this paper, we analyze two months of trajectory data for aircraft landing in five major European airports. Based on open ADS-B data from the OpenSky Network and open performance models, we enrich all trajectories with automatically detected procedure information, fuel consumption, and emissions for supported aircraft types. To assess the inefficiencies associated with holding patterns, point merges, and continuous descent operations across different airports, we propose methodologies to quantify and compare these environmental inefficiencies. Holding patterns are found to have a higher negative impact on the environment than point merge and continuous descent operations. Furthermore, the paper provides recommendations for procedure evaluations of future airports, which could help policymakers and relevant stakeholders to evaluate the environmental performances of arrival procedures based on open data and open models.

Position and Attitude Tracking Finite-Time Adaptive Control for a VTOL Aircraft using Global Fast Terminal Sliding Mode Control

In this work, the position and attitude tracking finite-time adaptive control problem of a type of vertical take-off and landing (VTOL) aircraft system is studied. Here, the dynamic of the VTOL aircraft is subjected to external disturbances and unknown nonlinearities. Firstly, radial basis function neural networks are introduced to approximate these unknown nonlinearities, and adaptive weight update laws are proposed to replace unknown ideal weights. Secondly, for the errors generated in the approximation process and the external disturbances of the aircraft system, adaptive parameter update laws are presented. After that, based on the designed global fast terminal sliding mode control functions and adaptive update laws, we present the position tracking control laws and the roll angle control law. Then, based on this, the adaptive global fast terminal sliding control laws for the given aircraft system are finally obtained. Meanwhile, the stability of the aircraft control system is proven by using Lyapunov stability theory and designed adaptive control laws. It is not only ensured that the outputs of the aircraft system can track the given trajectories, but also ensured that the tracking errors can converge to approximately zero within a finite time. Finally, the validity of the designed adaptive control laws is verified through three numerical examples. It can be obtained that the finite-time tracking problems of the given aircraft system can be achieved at 18.8766 s and 14.6340 s under the given parameters. The results are consistent with the theoretical analysis. In addition, under the control laws proposed in this work, the aircraft system can achieve tracking after 9.443 s and 9.674 s and the tracking errors are basically close to zero, which is significantly superior to other control methods considered in this work.