Real Aircraft Research Articles

Dynamic Temporal Denoise Neural Network with Multi-Head Attention for Fault Diagnosis Under Noise Background.

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value.

HEMLAB Algorithm Applied to the High-Lift Japan Aerospace Exploration Agency Standard Model

The high-lift Japan Aerospace Exploration Agency (JAXA) standard model (HL-JSM) has been numerically analyzed in order to further validate the HEMLAB code for realistic aircraft configurations. The numerical algorithm is based on highly efficient edge-based data structure for a vertex-based finite volume algorithm on hybrid meshes. The data pattern is arranged to meet the requirements of a vertex-based finite volume algorithm by considering data access patterns and cache efficiency. A fully implicit version of the numerical algorithm has also been implemented based on the Portable, Extensible Toolkit for Scientific Computation (PETSc) library in order to improve the robustness of the algorithm. The resulting algebraic equations, including the one-equation Spalart–Allmaras, are solved in a monolithic manner using the restricted additive Schwarz preconditioner combined with the flexible generalized minimal residual method [FGMRES(m)] Krylov subspace algorithm. The numerical method is also combined with the metric-based anisotropic mesh refinement library Python Adaptive Mesh Geometry Suite–INRIA (pyAMG) from National Institute for Research in Digital Science and Technology in order to improve the numerical accuracy. The numerical algorithm is initially applied to the two-dimensional L1T2 (National High Lift Programme) high-lift system, and then the calculations around the HL-JSM are carried out at relatively high angles of attack. The numerical results with the anisotropic mesh refinement library pyAMG indicate significant improvements in numerical accuracy.

Optimization of high-speed angular contact ball bearing for aircraft gearbox utilizing an evolutionary multi-objective algorithm, NSGA-III

We develop a program for predicting bearing performance, including the maximum contact stress, bearing power loss, minimum lubricating film thickness, static safety factor, and dynamic life, and an optimal design for high-speed angular contact ball bearings in aircraft gearboxes using optimization algorithms. A program is developed to predict bearing performance by calculating the loads and displacements of individual balls based on the bearing’s kinematic and static equilibrium equations. Next, an optimization program is developed with non-dominated sorting genetic algorithm III (NSGA-III), wherein calculated bearing performance indicators serve as constraints or objective functions. The performance of individuals within the final Pareto front is evaluated using the inter-criteria correlation (CRITIC) and weighted product methods. The optimization performances of NSGA-III and NSGA-II are compared based on their hypervolume indicators. A solution of the kinematic and static equilibrium equations under the load cases and duty cycles of real aircraft gearboxes is obtained, enhancing the prediction accuracy. Furthermore, optimization, including bearing performance metrics rarely considered in previous studies, is performed. Optimal specifications superior to reference bearings employed in aircraft gearboxes are obtained, enhancing all three objective functions or specific objective functions. The optimization performance of NSGA-III is confirmed to surpass that of conventional NSGA-II.

Remaining Useful Life prediction based on physics-informed data augmentation

Current approaches for monitoring machine health (SOH) and effective prognostics depend on the extensive use of complete degradation data trajectories, implying the reliance on data generation techniques that involve functional degradation of the real system until the failure state is reached. These commonly adopted approaches that depend on labeled target data remain operationally and economically nonviable for most industries and safety critical systems. This paper presents novel approaches that alleviate the existing dependence of most prognostics procedures on Remaining Useful Life (RUL) labeled data for training. To this end, firstly, a hybrid data augmentation procedure is proposed that enables the integration of system knowledge available a priori as well as physics of failure, within the training data. Secondly, an unsupervised Health Index (HI) extraction approach is developed, followed by a long-term prediction of this same HI, that leads to an efficient prediction of RUL without labeled data. Finally, a reliability-based assessment is performed to validate the proposed approach. This comprehensive approach (i.e. integrating all the various stages involved in achieving a RUL prediction based on unlabeled data) is tested on a real industrial aircraft system demonstrating the effectiveness of the proposed approach in real industrial context.

Surface roughness effects on transonic aircraft performance: Experimental/numerical comparisons

This work highlights the necessity of taking into account surface roughness when conducting experimental tests, and when using numerical simulations to precisely calculate the turbulent lift and drag of wind-tunnel models or real aircraft in transonic conditions. The present article is a continuation of “Turbulent drag induced by low surface roughness at transonic speeds: Experimental/numerical comparisons,” Physics of Fluids, Vol. 32, 045108 (2020) by Hue and Molton. The outcomes of this former study, which was focused on flat plate samples, are here applied to a three-dimensional aircraft configuration: the Common Research Model used as a reference in the recent international Drag Prediction Workshops. Experimental campaigns have been performed in the largest ONERA wind tunnels S1MA and S2MA involving models with average surface roughness heights Ra close to 0.5 micrometers, wingspans up to 3.5 meters, Mach and Reynolds numbers up to 0.95 and 5 million respectively. Reynolds-averaged Navier–Stokes computations based on the wind-tunnel tests have then been carried out, using the equivalent sand-grain roughness height approach as well as a Musker-type correlation to determine relevant ks values. The results of both the experimental and numerical campaigns have demonstrated that the aerodynamic coefficients of the aircraft can be significantly affected by the surface roughness, even with roughness Reynolds numbers ks+ potentially below the usual threshold values sometimes considered in engineering applications (i.e. in the order of 3.5 to 5). In particular, the surface roughness effects on lift and drag have been studied using far-field analyses to evaluate the responses of friction, viscous pressure, wave and lift-induced drag components. Finally, the numerical studies have been extended to the full-scale geometry in flight conditions in order to assess the roughness effects and potential gains in realistic aircraft operating conditions.

GW-MUSIC focusing algorithm with high accuracy for composite damage diagnosis

The Guided Wave (GW)-based Multiple Signal Classification (MUSIC) method for damage imaging in composite structures offers high-resolution and flexible sensor array placement, demonstrating considerable potential for precise damage localization. However, the application to real aircraft composite structures, characterized by complex structural features, presents substantial challenges. These include complex boundary reflections, pronounced anisotropy, and diminished scattered signals from damage sites. This paper presents a novel single-peak anisotropy-compensated MUSIC method for damage imaging of complex composites. This method, designed for improved localization accuracy, combines a self-excited calibration strategy to mitigate anisotropy with single wave peak focusing to amplify signals and reduce boundary reflections. Experimental validation on a complex composite wing structure demonstrated significant enhancements in localization precision, affirming the method's efficacy.

Active flutter suppression for light sport aircraft by a control surface split

A new method of active flutter suppression is present in the paper and aims at the Light Sport Aircraft category, where it has never been used, designed, or even considered. The novelty of the method lies in splitting the control surface into a part controlled by a pilot with purely mechanical control and into a part controlled by a servo-actuator with a controller. The control law of the actuator is designed to follow the pilot-controlled part of the control surfaces and damp unstable oscillations if they occur. The controller design for flutter suppression is focused on achieving simple solutions. The request for simplicity is important for easy acquisition of airworthiness during a certification process and easy implementation by producers. The contribution of this paper also lies in the analysis of flutter suppression capability based on the varying active control surface span. The results show that it is not necessary to use the entire area of a control surface for active flutter suppression.A mathematical model based on a real aircraft is developed and verified for the simulation of active flutter suppression. In addition, control law design and simulations of the dynamic response are performed. The robustness of the control law and aircraft controllability in the case of active control surface malfunction is investigated.

Intelligent adaptive lighting algorithm: Integrating reinforcement learning and fuzzy logic for personalized interior lighting

The lighting requirements are subjective and one light setting cannot work for all. However, there is little work on developing smart lighting algorithms that can adapt to user preferences. To address this gap, this paper uses fuzzy logic and reinforcement learning to develop an adaptive lighting algorithm. In particular, we develop a baseline fuzzy inference system (FIS) using the domain knowledge, generating light recommendation based on a set of intuitive rules. These rules, derived from existing literature, are based on environmental conditions i.e. daily glare index, and user information including age, activity, and chronotype. Through a feedback mechanism, the user interacts with the algorithm, correcting the algorithm output to their preferences. We interpret these corrections as rewards to a Q-learning algorithm, which tunes the FIS parameters online to match the user preferences. The Q-learning is a model-free learning algorithm that learns to act optimally by interacting with the user and the rewards it receives. This allows the proposed algorithm to work in a model-free manner, effectively handling the uncertainties that might arise from the individualistic preferences of users. To the authors’ best knowledge, this algorithm is pioneering work in designing intelligent algorithms for personalized lighting control, featuring several elements of novelty, including the number of environmental and user-related inputs, the continuous control of light intensity as opposed to common on/off control, and the ability to learn user preferences. The algorithm is implemented in a real aircraft cabin and is evaluated in an extensive user study. The implementation results demonstrate that the developed algorithm possesses the capability to learn user preferences while successfully adapting to a wide range of environmental conditions and user characteristics. This underscores its viability as a potent solution for intelligent light management, featuring advanced learning capabilities.

Application research on improved genetic algorithm and active disturbance rejection control on quadcopters

For nonlinear, strongly coupled, underdriven quadcopters in the context of modeling complexity and demanding performance requirements for the controller, this paper proposes a strategy based on an improved genetic algorithm to optimize the active disturbance rejection control (ADRC) controller. To make the quadcopter continue to fly stably in a complex environment, the dynamics model of the quadcopter was firstly established, the mathematical model was simplified according to the real world, and the ADRC controller of the quadcopter was designed. Given a large number of ADRC controller parameters, the difficulty of manual tuning and obtaining the optimal control effect, and the shortcomings of the genetic algorithm in solving the problem of local optimal and precocious convergence, a control strategy based on improved genetic algorithm to optimize ADRC’s parameters is proposed to improve the genetic diversity in the population and enhance the adaptability of individuals to the environment, ITAE (Integral-of-Time-multiple Absolute Error) evaluation index is selected as the fitness value. Finally, the model of the control system is built according to the real aircraft. The application results prove that the altitude, attitude of the quadcopter are controlled stably, and it is verified that the control strategy based on the improved genetic algorithm optimizing ADRC has faster rapidity, stronger tracking performance, and robustness in altitude, attitude control of the quadcopter, which has greater practical application value.

SslTransT: Self-supervised pre-training visual object tracking with Transformers

Transformer-based visual object tracking surpasses conventional CNN-based counterparts in superior performance but comes with additional computational overhead. Existing Transformer-based trackers rely on large-scale annotated data and longer training periods. To address this issue, we introduce a self-supervised pretext task, named target localization, which randomly crops the target and then pastes it onto various background images. The copy-paste-transform data augmentation strategy can composite sufficient training data and facilitate routine training. In addition, freezing the CNN backbone during pre-training and randomly adjusting template and search area factors further lead to faster training convergence. Extensive experiments both on public tracking benchmarks and real aircraft flight test videos demonstrate that our proposed tracker SslTransT significantly outperforms the baseline performance while requiring only half the training time. Furthermore, we apply SslTransT to a 6D pose measurement system based on vision and laser ranging, achieving excellent tracking results while running in real-time.

Assessing the Impact of Metrics on the Choice of Prognostic Methodologies

Over the past years, advanced prognostic models and approaches have been developed. Most existing approaches are tailored to one specific system and cannot adaptively be used on different systems. This can lead to years of research and expertise being put into implementing prognostic models without the capacity to predict system failures, either because of a lack of data or data quality or because failure behavior cannot be captured by data-driven models. In addition, prognostic models are often evaluated using metrics only related to the correctness of predictions, preventing meaningful evaluation of operational performance. This paper makes use of a framework that can automatically choose prognostic settings based on specific system data. It simultaneously optimizes the choice of methodologies using metrics that capture multiple aspects of prediction quality. We apply this framework to both a simulated data set and a real aircraft data set to characterize the impact of metrics on the choice of prognostic methodologies. The results show that the choice of optimization metric greatly impacts the output of the generic prognostic framework and the overall performance. In addition, a definition for data suitability is provided and assessed on the aircraft system data sets.

Whirl Flutter Predictions for Distributed Propulsion Tiltrotor Configurations

A family of generic hingeless hub distributed propulsion tiltrotor configurations was modeled in RCAS and studied for whirl flutter along with a baseline conventional configuration for comparison. A sensitivity study was carried out using the baseline model to provide a fundamental understanding. The objective was to examine whirl flutter instability for a range of design variables to provide guidelines to designers. Wing torsion and rotor flap stiffness significantly impacted the instability speed. Reducing wing beam stiffness interestingly stabilized the system due to lower participation of wing torsion motion in the beam mode. Various distributed propulsion tiltrotor configurations were analyzed. Replacing the large rotor at the wing tip with two small rotors of half the disk area (same disk loading) greatly stabilized the system and pushed the instability speed by more than 100%. Placing these rotors at the wing tip in a coaxial configuration also improved stability, but not as much as distributing them over the wing. It appears that distributed propulsion tiltrotor configurations can be expected to exhibit better stability characteristics than traditional tiltrotor aircraft; however, analysis for a real aircraft is necessary.

Stress Distribution Analysis using ANSYS of the Nose Landing Gear of STOL Aircrafts Considering Runway Gradient: A Simulation Study

Landing gear failures are a major cause of aircraft failures because they are an integral part of the aircraft's development. According to the Federal Aviation Administration (FAA), 50% of all aircraft failures occur during take-offs and landing. The landing gear of a short take-offs and landing (STOL) aircraft has had its static and dynamic forces analyzed analytically and numerically. SOLIWORKS is used to model the landing gear, and ANSYS is used to perform the numerical simulation results. Following it, the model is examined for stress and deformation with the boundary conditions and computed loads into account. This study uses structural finite element analysis (FEA) to analyze the nose gear's stress behavior and displacement during landing, as well as to assess the effect of the runway gradient. Utilizing a comprehensive numerical simulation, the landing gear of a real material Twin-Otter STOL aircraft has been demonstrated to be subjected to a dynamic force of 3653.26N, a pneumatic pressure of 1.59MPa, a bead pressure of 7.97MPa, and a vertical force of 1672.31lbs on level runways, or runways with no gradient. These findings will increase the level of confidence of aircraft manufacturers to make necessary appropriate design of STOL aircraft’s landing gear. It may can decrease the aircraft accidents and increase human life safety ultimately saving valuable time and resources too.

Probabilistic aeroelastic analysis of high-fidelity composite aircraft wing with manufacturing variability

Safety margins of aerospace structures can be improved through altering the laminate parameters of composite materials to increase flutter and divergence velocities. Existing work demonstrates the impact of material uncertainties on low-fidelity structural models that are not sufficient to represent realistic aircraft designs. A gap exists in quantifying laminate parameter uncertainties on aeroelasticity for high-fidelity three-dimensional composite structures in realistic tailored designs. This paper puts forward an efficient methodology for uncertainty quantification on the aeroelastic characteristics of three-dimensional composite structures using FE-based parametric composite models and advanced Kriging surrogate models. The methodology is tested on both low and high fidelity case studies to represent the composite wing structure. Similarities between the case studies are observed in the coefficient of variance of all hard flutter modes being within 0.15-1.4% of each other. The difference was found for divergence and soft flutter velocities where the coefficient of variance could be over ten times higher in the high fidelity case. Global sensitivity results revealed similar physical behavior cases can be produced from both studies at early design stages.

Large‐scale homo‐ and heterogeneous parallel paradigm design based on CFD application PHengLEI

SummaryThe development of computational fluid dynamics (CFD) highly depends on high‐performance computers. Computer hardware has evolved rapidly, yet scalable CFD parallel software remains scarce. In this article, we design a highly scalable CFD parallel paradigm for both homogeneous and heterogeneous supercomputers. The paradigm achieves the separation of communication and computation and automatically adapts to various solvers and hardware environments, thus reducing programming difficulties and increasing automatic parallelization. Meanwhile, the number of communications is greatly reduced and the scalability of the program is improved through implementing centralized communication and two‐level partitioning techniques. Complex flow problems for real aircraft were then computed on different hardware platforms with a grid size of ten billion. The homogeneous computer hardware includes Intel Xeon Gold 6258R and Phytium 2000+ processors, and the heterogeneous computer platforms include NVIDIA Tesla V100 and SW26010 processors. High parallel efficiency was obtained on all computer platforms, verifying that the paradigm has good automatic parallelization, scalability, and stability. The paradigm in this article has an important reference value for CFD massively parallel computing and can promote the development and application of CFD technology.

Design and aircraft‐in‐the‐loop validation of structured‐H∞ self‐scheduled fault‐tolerant controllers

AbstractThis paper presents the design of a self‐scheduled fault‐tolerant controller for the lateral/directional motion of MuPAL‐ research aircraft using a polynomial‐scheduled structured H control. The controller is designed to be tolerant against loss‐of‐efficiency faults in the aileron and rudder, but based on industrial best practices it is scheduled with respect to an overall fault level instead of with respect to the individual faults. The performance and robustness of the resulting controller is verified first using frequency and time domain analysis, and subsequently it is validated in the Aircraft‐In‐the‐Loop configuration of MuPAL‐, where the real aircraft is operated in research Fly‐By‐Wire mode by the pilot on‐ground while coupled to an emulation computer that simulates the aircraft flying motion. The results show good behavior of the controlled aircraft across the defined fault scenarios.

Optimized fuel values for emission reduction

Aviation is an integral and vital part of modern society. The growth over the last decades has consequences for greenhouse gas emissions and reducing this through efficiency within the same framework is difficult. Precision flight planning is crucial for reliably and optimized real environment aircraft operation. The presented study gives an overview of the status of the legal requirements for flight planning under the current fuel requirements of the European Union Aviation Safety Agency (EASA) and the emerging opportunities for fuel savings. As part of the larger study, planned and actual fuel figures of an international cargo airline were statistically analyzed. The overall analysis showed that there was no significant deviation between planned and consumed fuel. Based on the results, an adjustment of the planned alternative fuel quantity can be considered within the framework of an individual fuel plan. The possible savings potential using the example of Destination Alternate Airport fuel is presented.

Research on Scenario Modeling for V-Tail Fixed-Wing UAV Dynamic Obstacle Avoidance

With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.

Visualising flight regimes using self-organising maps

AbstractThe purpose of this paper is to group the flight data phases based on the sensor readings that are most distinctive and to create a representation of the higher-dimensional input space as a two-dimensional cluster map. The research design includes a self-organising map framework that provides spatially organised representations of flight signal features and abstractions. Flight data are mapped on a topology-preserving organisation that describes the similarity of their content. The findings reveal that there is a significant correlation between monitored flight data signals and given flight data phases. In addition, the clusters of flight regimes can be determined and observed on the maps. This suggests that further flight data processing schemes can use the same data marking and mapping themes regarding flight phases when working on a regime basis. The contribution of the research is the grouping of real data flows produced by in-flight sensors for aircraft monitoring purposes, thus visualising the evolution of the signal monitored on a real aircraft.

The use of the interactive DA-42T aircraft cockpit mockup in the process-oriented training

When conducting training sessions at an aviation university, it is advisable to demonstrate samples of aeronautical equipment, individual elements of systems and accessories, or use specialized stands and posters. However, when studying modern types of training aircraft, the cockpits of which contain multifunction displays, complications arise when absorbing training resources. When studying multi-function display cockpits, it is necessary to use interactive training devices in which displays must operate under power and possess the required capabilities. Training on hardware is impracticable in some cases, and unadvisable in others. The use of the integrated simulator for training in various disciplines is limited as the simulator is primarily designed to impart primary aircraft control skills rather than for classroom training. The article deals with the issue of upgrading pilot-trainees training skills during the familiarization with the cockpit equipment using an interactive DA-42T aircraft cockpit mockup with all the controls and indications (except for the control stick and pedals), including two primary multi-function displays and one backup, during the process-oriented training of the university. The operation of multi-function indicators is implemented in the form of special devices with a display and a push-button frame connected to a specially developed program on a PC that simulates the aircraft data system operation. For this purpose, to be based on information from the Aircraft Flight Manual, the information frames, shown on multi-function displays in the DA-42T aircraft, are reproduced. The content of the developed frames duplicates completely the DA-42T aircraft indication, contributing to improving academic study and acquiring practical skills to work on the integrated simulator as well as in a real aircraft. The physical cockpit mockup controls also correspond, in terms of the appearance and location, to the controls in the real DA-42T aircraft cockpit. The procedure to develop simulators of multi-function displays and the DA-42T aircraft cockpit mockup is described. The capabilities to use simulators of multi-function displays and the DA-42T cockpit mockup in the process-oriented training to upgrade skills are described. The conducted research results are described; the advantages of using the interactive cockpit mockup in the process-oriented training are presented.