Flight Parameters Research Articles

Intelligent temporal detection network for boundary-sensitive flight regime recognition

Flight regime recognition is critical in guaranteeing flight safety, making informed maintenance decisions for key components, and evaluating flight quality. However, previous methods take flight data points/segments as inputs to make point-wise/segment-wise prediction, which results in imprecise regime boundary localization, poor recognition accuracy or low efficiency. To this end, an intelligent temporal detection network is proposed from a brand-new perspective of regime detection, which directly handles long flight sequences and concurrently generates multiple regime boxes with corresponding categories. Moreover, specific model structures are designed for flight data, including an adaptive graph embedding to explore spatial relations of multi-modal flight parameters, a multi-scale Transformer encoder to recognize regimes of different durations, and a balanced joint loss to mitigate negative impact on imbalanced flight regimes. To validate the superiority of the proposed method, various parameters of real-world flight sorties are collected. Then flight regime dataset is constructed via manual annotation. Extensive experiments and ablation studies show that our method can achieve accurate and boundary-sensitive regime recognition simultaneously.

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.

Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation.

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.

Field evaluation of a six-rotor unmanned agricultural aerial sprayer: effects of application parameters on spray deposition and control efficacy against rice planthopper.

Unmanned Aerial Spraying System (UASS) has emerged as an advanced, precise, and efficient tool for pesticide application in numerous nations in recent years. Despite this, there is a noticeable gap in research advocating viable, quantifiable methodologies for application parameter optimization. This investigation was primarily oriented toward identifying optimal UASS application parameters. It did so by exploring the effects of varying spray volumes and flight parameters on spray performance in a comprehensive manner, and by assessing the biological potency of aerial insecticide application against Rice Planthopper (RPH) using the optimal parameters, aided by two types of nozzles in rice field settings. Increased spray volume increased the spray deposition. Working height impacted the distribution of spray deposition, with a higher working height leading to superior distribution uniformity. Both spray volume and working height were observed to influence spray deposition and its percentage in tandem. Upon factor analysis, the optimal parameters determined for rice at the heading stage were an application volume of 15.0 L·ha-1 , a working height of 2.0 m, and a driving speed of 5.0 m·s-1 . Under these parameters, the air-induction twin flat fan nozzle IDKT120-015 demonstrated approximately 5% higher spray deposition than the flat fan nozzle SX11001VS, albeit with inferior distribution uniformity. Both nozzle types achieved over 93.0% control efficacy against RPH using triflumezopyrim, persisting for up to 40 days post-treatment. This study furnishes invaluable insights and data for controlling rice planthopper via UASS pesticide application, contributing to the progress of modern intensive and sustainable agriculture. © 2023 Society of Chemical Industry.

Thermo Structural Integrity of a Closure Assembly for The Base Region of a Re-Entry Module

Base heating is an important phenomenon in the design of re-entry capsules. The nature of reverse flow in the base region is largely decided by the conditions prevailing at the end of the fore body. For configuration like the re-entry module, the reverse flow is found to be supersonic near the base. It is therefore necessary to protect the base region as well as the internal components from base heating.
 Suitable Thermal Protection System (TPS) of minimum mass that will protect the base thermally is to be designed for the base heating levels during re-entry. Low density ablative is selected as the TPS material.
 The base region of the re-entry module consists of a closure assembly made up of structural elements which are thermally protected. The structural elements and TPS have different thermo-physical properties. It is necessary to qualify the closure assembly for re-entry loads. However, qualification of closure assembly is a challenging job as there is no facility in the world which can simulate all the time varying flight parameters simultaneously. Hence to study the integrity of the closure assembly during re-entry, detailed temperature mapping of closure assembly was carried out.
 Thermal mapping of the closure assembly was carried out considering both axisymmetric and three-dimensional model using finite element package and the results are compared. Maximum temperatures attained at different locations of the assembly are discussed. Temperature distribution of the closure assembly at critical instants of flight was estimated. Thermo-structural analysis carried out showed the adequacy of the design, which was subsequently demonstrated by a successful re-entry flight.

Reconstructing 3D model of accident scene using drone image processing

At the current stage, an investigation technique on the accident takes a longer time and this causes longer traffic congestion. The aim of this study is to reconstruct a 3D model of an accident scene using an unmanned aerial vehicle (UAV). The flight parameters that have been chosen are the circular method, the double grid method, and the single grid method. All these designs can produce a good 3D model to achieve the study’s objective. The methodology in this study is divided into 4 phases which are preliminary work, data acquisition, data processing, and data analysis. The main results of this study are the 3D model of the accident scene, an orthophoto map layout, and an accuracy assessment of a 3D model of reconstructed accident scene. All these parameters will be tested on accuracy based on the root mean square error (RMSE) value, comparing the UAV data and site measurement data. This objective has been tested for 10 different types of processing and different types of flight parameters. The best result among all the methods is the circular method 5 meters with ground control point (GCP) since this method has the least RMSE value which is 0.047 m. UAVs can replace the site measurement to reconstruct the accident scene.

The angle of attack and side-slip angle estimation for civil aircraft

The angle of attack and side-slip angle are two important flight parameters that directly affect the flight safety of civil aircraft. This paper studies the estimation method for the angle of attack and side-slip angle according to air-data system, inertial navigation system, and aircraft aerodynamic data to improve flight safety under the condition of icing, failures, and improper maintenance. Flight tests show that the proposed estimation method is feasible, reliable, and has better accuracy. Furthermore, flight test data analysis shows that the angle of attack estimation error is within 1 degree, and the side-slip angle estimation error is within 1.5 degrees which can meet the requirements of engineering application.

RPAS performance model for fast-time simulation research on integration in non-segregated airspace

PurposeThe purpose of this paper was to elaborate the performance model of the remotely piloted aircraft systems (RPAS) which was destined for simulations of the construction characteristics, airspeeds and trajectory of flight in the controlled, non-segregated airspace according to the standard instrument departure and arrival procedures (SIDs and STARs).Design/methodology/approachThis study used systems engineering approach: decomposition of RPAS performance model into components, relations and its connection with components of controlled the airspace system. Fast-time simulations (FTS) method, which included investigation of many scenarios of the system work, minimizing the number of input variables and low computing power demand, is also used.FindingsPerformance envelope of many fixed-wing RPAS was not published. The representative RPAS geometry configuration was feasible to implement. Power unit model and aerodynamic model needed to be accommodated to RPAS category. The range of aircraft minimum drag coefficient differed in the investigated range of take-off mass and wing loading.Research limitations/implicationsFixed-wing RPAS of small and medium categories cover take-off mass (25–450 kg), wing loading (40–900 N/m2) and power loading (8–40 W/N).Practical implicationsThis is a research on integration of the RPAS in the controlled, non-segregated airspace. The results of the work may be used in broadening the knowledge of the RPAS characteristics from the perspective of operators, designers and air traffic services.Originality/valueThe elaborated performance model of the RPAS used the minimum number of three input variables (take-off mass, wing loading and power loading) in identification of the complete RPAS characteristics, i.e. construction features (aerodynamic, propulsion and loads) and flight parameters (airspeeds and flight trajectory).

Imaging Characteristics of Disturbance Flow Field Surrounding a Hypersonic Target

The disturbance flow field arises naturally with hypersonic target flying in near space. In situations where traditional infrared and radar systems lose effectiveness, space-based optical detection of this surrounding flow can serve as an alternative method for detecting high-speed targets. This paper presents a remote sensing imaging analysis of the disturbance flow field surrounding a hypersnoic target at different flight altitudes and Mach numbers. Utilizing Fourier Optics and Background-Oriented Schlieren, in conjunction with the fourth-order Runge-Kutta ray tracing algorithm, the imaging blurring and imaging deviation of three typical backgrounds under the influence of the disturbance flow field are obtained. Additionally, the study analyzes the influence of flight conditions and parameters of the imaging system on the imaging characteristics, and provides optical design recommendations. The results indicate that the presence of disturbance flow fields leads to varying degrees of visually apparent blurring effects and indiscernible deviation effects on the background images. Furthermore, the profiles of the disturbance flow field are extracted, in agreement with current experimental research. This study verifies the feasibility of space-based optical detection of hypersonic targets through disturbance flow field remote sensing imaging and contributes to the advancement of imaging research in this field.

Shock Induced Flow-Separation in Hypersonic Intakes at Off-Design Conditions

High-speed turbulent flow in hypersonic intakes encounters flow-separation due to shockboundary layer interaction. Presence of separation bubble changes the pressure distribution over the surface which can lead to deterioration of aerodynamic performance of intake and thus jeopardize the stability of vehicle. In this study, we employ the physics-based shock-unsteadiness k-e model to compute separation bubble size in a mixed compression two-ramp intake. The design point Mach number of 6 at 30 km altitude with zero angle of attack is considered. Changes in separation bubble length is analyzed at different off-design conditions by varying the flight Mach number, angle of attack and cruise altitude. The changes observed in the separation length by variation of above flight parameters is studied in terms of the physically relevant canonical parameters of upstream Mach number, Reynolds number Red and the ratio of wall to recovery temperature. CFD simulations are performed for the entire range of canonical and flight parameters. Wall-to-recovery-temperature ratio is found to be an important governing parameter for separation length studies. The largest shock-induced separation is observed for the case of off-design flight Mach number of 8. The corresponding effect on the lift and drag generated by the hypersonic intake are quantified.

Aerodynamic and structural characteristics of helicopter rotor in circling flight

To investigate the distinct properties of the helicopter rotors during circling flight, the aerodynamic and dynamic models for the main rotor are established considering the trim conditions and the flight parameters of helicopters. The free wake method is introduced to compute the unsteady aerodynamic loads of the rotor characterized by distortions of rotor wakes, and the modal superposition method is used to predict the overall structural loads of the rotor. The effectiveness of the aerodynamic and the structural methods is verified by comparison with the experimental results, whereby the influences of circling direction, radius, and velocity are evaluated in both aerodynamic and dynamic aspects. The results demonstrate that the circling condition makes a great difference to the performance of rotor vortex, as well as the unsteady aerodynamic loads. With the decrease of the circling radius or the increment of the circling velocity, the thrust of the main rotor increases apparently to balance the inertial force. Meanwhile, the harmonics of aerodynamic loads in rotor disc change severely and an evident aerodynamic load shock appears at high-order components, which further causes a shift-of-peak-phase bending moment in the flap dimension. Moreover, the advancing side of blade experiences second blade/vortex interaction, whose intensity has a distinct enhancement as the circling radius decreases with the motion of vortexes.

WI-FI COMMUNICATION SYSTEM FOR A FIXED-WING TWIN-ENGINE AIRPLANE UAV

Unmanned Aerial Vehicle (UAV) systems have automated pilots that can be configured to suit various mission requirements. In this study, we assess, develop, and evaluate a bidirectional Wi-Fi communication link for an automated piloting system, utilizing the Cube Orange architecture that is mounted on a fixed-wing twin-engine airplane UAV. This Wi-Fi link enables seamless data exchange between the UAV and the ground control station. Using this bidirectional communication link, the user gains access to telemetry data, providing real-time insights into the UAV's flight parameters. Additionally, the user can send commands to the UAV, ensuring dynamic mission control and adaptability, while, the Wi-Fi link facilitates the retrieval of live video data from the UAV's onboard camera, enabling display on the command station screen or recording for future analysis and utilization. This comprehensive Wi-Fi communication system enhances the overall functionality and efficiency of the UAV's automated piloting, enabling smooth and reliable operations for various mission scenarios.

Research of Flight Safety and Technical Evaluation

Flight safety is the foundation of survival and development of civil aviation transport industry. Aircraft flight safety can be checked by aircraft flight parameters. We discuss a flight technology evaluation method based on flight parameter data. Firstly, data preprocessing is carried out, and the data is reduced by PCA dimension reduction. Then, BP neural network and decision tree model are established to train and test the data set. Neural network algorithm and machine learning algorithm are used for training. The results show that the BP neural network verification set is more accurate. The comparison table between the predicted results and the real results is shown in Table 3. Among the 15 samples, 14 are correct. The prediction accuracy reaches 93%.

ENSURING SPEED STABILITY OF THE UNMANNED AERIAL VEHICLE IN DIF-FERENT FLIGHT MODES

The publication deals with the issue of ensuring the stability of an unmanned aerial vehicle flight by speed. An analysis of the physical essence of the process of ensuring the unmanned aerial vehicle stability by speed is presented. Attention is paid to the flight modes in which the operator (external pilot) may encounter difficulties in the practical piloting of the unmanned aerial vehicle. The possible causes of abnormal situations when flying the unmanned aerial vehicle at speeds close to the borderline low are analyzed. Possible actions of the operator (external pilot) to return the unmanned aerial vehicle to the operating speed range from the area of instability are considered. The article shows the features of stability and controllability of an unmanned aerial vehicle in a route flight mode. Attention is paid to the complexity of piloting and safety of flight in the second modes. The article demonstrates the method of determining the boundary between the first and second flight modes. The features of unmanned aerial vehicle management and ensuring stability in the first and second modes are shown. The issue of dynamic stability of the unmanned aerial vehicle is investigated. Numerical modeling of the longitudinal movement of the unmanned aerial vehicle in free, uncontrolled flight was carried out, trajectories for the unmanned aerial vehicle of the selected configuration with a change in speed and angle of inclination of the trajectory in time were obtained. Graphs of movement trajectories with changes in speed and height over time were constructed, graphs of transient processes were constructed for cases of stable and unstable flight modes. An analysis of the external factors that most affect the flight parameters in various conditions of unmanned aerial vehicle use was carried out.

A multi-model architecture based on deep learning for aircraft load prediction

Monitoring aircraft structural health with changing loads is critical in aviation and aerospace engineering. However, the load equation needs to be calibrated by ground testing which is costly, and inefficient. Here, we report a general deep learning-based aircraft load model for strain prediction and load model calibration through a two-phase process. First, we identified the causality between key flight parameters and strains. The prediction equation was then integrated into the monitoring process to build a more general load model for load coefficients calibration. This model achieves a 97.16% prediction accuracy and 99.49% goodness-of-fit for a prototype system with 2 million collected flight recording data. This model reduces the effort of ground tests and provides more accurate load prediction with adapted aircraft parameters.

A Variable Gain Complementary Filtering Fusion Algorithm Based on Distributed Inertial Network and Flush Air Data Sensing

Aiming at the problem that the accuracy of flight parameters calculated by the traditional flush air data sensing (FADS) and inertial navigation system (INS) complementary filter cannot meet the fine real-time control of the aircraft, a data fusion algorithm based on distributed inertial network and FADS is proposed. This method converts the inertial navigation parameters calculated by the distributed inertial network into flight parameters, and using the least squares fitting theory, the flight parameters are obtained from the pressure data measured by the FADS pressure hole. Then, by adjusting the filter constant of the complementary filtering algorithm, the flight parameters calculated by the inertial network are fused with the flight parameters calculated by the air data sensing to obtain high-precision flight parameters. Finally, the simulation results show that the proposed filtering algorithm can keep the flight parameter estimation error less than 0.01 degrees throughout the flight phase. Compared with the traditional complementary filter, the estimation error of the proposed algorithm is smaller.

Machine Learning Assisted Prediction of Airfoil Lift-to-Drag Characteristics for Mars Helicopter

The aerodynamic properties of rotor systems operating within low Reynolds number flow field conditions are profoundly influenced by their geometric and flight parameters. Precise estimation of optimal airfoil parameters at different angles of attack is indispensable for enhancing these aerodynamic properties. This study presents a technique for optimizing the airfoil parameters of a Mars helicopter by employing machine learning methods in conjunction with computational fluid dynamics (CFD) simulations, thereby circumventing the need for expensive experiments and simulations. The effectiveness of diverse machine learning algorithms for prediction is evaluated, and the resultant models are utilized for airfoil optimization. Ultimately, the aerodynamic properties of the optimized airfoil are experimentally validated. The experimental findings exhibit agreement with the simulated predictions, indicating the successful optimization of the aerodynamic properties. This research offers valuable insights into the influence of airfoil parameters on the aerodynamic properties of the Mars helicopter, along with guidance for airfoil optimization.

Development of an Unpiloted Aircraft System–Based Sensing Approach to Detect and Measure Pavement Frost Heaves

Abstract The combination of low temperatures, precipitation, and high-water table causes the formation of subsurface frost in pavements leading to the formation of frost heaves. This leads to significant damage to the structural integrity of the pavements and causes extensive surface roughness. Frost heaving is a common type of distress in cold climate regions, especially for road and airport infrastructures located in remote areas. Research discussed in this article focuses on determining the efficiency and accuracy of photogrammetry-based sensing systems to measure the extent of pavement frost heaves. The sensors are mounted on an unpiloted aircraft system (UAS) capable of providing measurements over large spatial domains in a single flight and are well suited for difficult-to-access regions. Experiments have been conducted on simulated heaves as well as actual cold climate pavement sections. The research described herein provides suitable UAS flight parameters for conducting frost heave measurements. Flight at an altitude of 50 m above the ground surface and a flight speed of 5 m/s with capturing images every 2 s resulted in more than 80 % front and side image overlap. The pavement surface profile has been constructed from extracted photogrammetry elevation data collected in three different seasons. Pavement surface roughness has been measured in terms of the international roughness index (IRI), and variation between measured IRI values has been evaluated during different seasons because of the formation of the frost heave distress. As it is expected, the IRI values increase as the cold season approaches. Moreover, the measured IRI values have been compared with the most recent available IRI data collected by an instrumented vehicle. The key outcomes of this work demonstrate that photogrammetry can reliably detect pavement frost heaves and provide high confidence in the future development of an automated system to measure pavement roughness that is attributable to frost heave distresses.

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper, an intelligent control method applying on numerical virtual flight is proposed. The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect. Firstly, a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed. In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow, a numerical simulation of the basic finner projectile without control is carried out. The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation. Secondly, combined with the real-time control requirements of aerodynamic, attitude and displacement parameters of the projectile during the flight process, the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative) control strategy and the intelligent PID control strategy respectively. The intelligent PID controller based on BP(Back Propagation) neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters. Compared with the traditional PID controller, the concerned control variable overshoot, rise time, transition time and steady state error and other performance indicators have been greatly improved, and the higher the learning efficiency or the inertia coefficient, the faster the system, the larger the overshoot, and the smaller the stability error. The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Research on aircraft automation warning model based on gradient boosting tree algorithm

Flight safety is the basis for the survival and development of civil aviation transportation industry, and with the rapid development of China's civil aviation industry, the study of aircraft safety issues is becoming more and more important. Serious flight accidents can lead to great threat to life as well as economic loss, so this paper focuses on flight safety issues, and analyzes and investigates flight key parameters segment data, aircraft overruns, and flight parameters data. In this paper, based on the relevant data from QAR , t-test was performed on the processed data through data analysis and data cleaning to ensure the reliability of the data. And the principal component components of the key data were extracted using the random forest algorithm, and the 6-bit eigenvalues were selected as the key parameters, which had the greatest influence on the landing G value of the aircraft. Then, the data set was divided according to the ratio of 8:2, and the pilot qualification was used as the label value, and the machine learning classification prediction was performed by the gradient boosting tree GBDT algorithm to obtain the pilot qualification prediction model, and the highest accuracy reached 76.3% in the test set. This paper also compares the extreme gradient boosting XGBoost algorithm, with the highest current parameter reaching 73.4%. In this paper, the gradient boosting tree GBDT is chosen to construct a flight crew qualification assessment model.