Actual Flight Research Articles

A New Technique to Qualify Rotary Electro-Mechanical Actuator for Tactical Class of Missile System

A novel technique has been adopted to establish the functionality and to qualify the Rotary Electro-Mechanical Actuator (REMA) system developed for a typical tactical class of weapon system in addition to the conventional method of checking the functionality through On-Board Computer in-loop simulation, Actuator in-loop simulation and Hardware in-loop simulation. The REMA along with full scale fin surface subjected to wind tunnel tests at the actual flight Mach number and flight dynamic pressure conditions. Flight hardware of REMA and fin assembly has been used in the tests so as to derive direct conclusions without applying any corrections to the measured results. This technique revealed the capability of the actuator, the dynamic functionality of its components such as gear systems, bearings and structural integrity of the whole actuator under the flight aerodynamic load on the full scale fin surface and vibration environments. The test conducted is being the most critical condition and the REMA and fin combination performed the intended commands as predictable and survived without any structural dis-integrity qualifies for further flight use. Subsequently flight tests were conducted and found the performance of REMA was as anticipated. Based on these results, recommendation made that it is mandatory that the all the actuation systems developed for fin actuation in missiles has to undergo the wind tunnel tests and qualify further actual use. The details of wind tunnel tests and the results of typical REMA with full scale fin are presented in this paper.

Study on flexural resilience of composite foam sandwich structures under hygrothermal environment

Under hygrothermal environments, the structural stability and strength of all-fiber composite aircraft are significantly affected during long-term flight use. The wing skin, as a critical structural component, plays a vital role in bearing and transmitting aerodynamic loads. Therefore, it is crucial to investigate the structural compressive stability and strength of the wing skin throughout the aircraft's entire life cycle under these conditions. This study employs a real wing carbon fiber foam sandwich structure to investigate the compressive stability and strength of the wing skin structure of a new energy aircraft under actual flight conditions, specifically during the entire process of the room temperature dry state (RTD) and elevated temperature wet state (ETW). Initially, three-point bending tests were conducted on carbon fiber reinforced polymer (CFRP) laminates, foam cores, and CFRP reinforced foam sandwich structures. The CFRP laminates fully rebounded after bending damage in both the RTD and ETW environments. While CFRP reinforced foam sandwich structures also rebounded fully in the RTD environment, their rebound performance diminished in hygrothermal conditions due to the thermoplastic mobility of the foam cores, resulting in only weak rebound capabilities. In hygrothermal environments, the thermoplastic mobility of the foam core leads to diminished resilience after bending damage, resulting in only weak rebound capabilities. Subsequently, compressive instability tests were conducted on the wing skin foam sandwich structure. The results indicated that the basic test study effectively predicted the structural test outcomes. Structural components in the RTD environment exhibited overall flexural instability under compressive load, with damage morphology resembling a circular curve; the damaged specimens fully rebounded after unloading. Conversely, specimens in the ETW environment displayed localized instability, characterized by a wrinkled damage profile, resulting in only weak rebound capabilities after unloading.

Development of a Voice Information Presentation Guidance System (VIPGS) Using Coanda‐Drone‐V2

AbstractIn this study, an aerial tour guide drone was developed based on the existing Coanda‐drone. The research integrated a Voice Information Presentation System with ROS2 using the open‐source PX4 flight control system to update the drone's control module. This system can recognize and read QR codes on displayed items and convert the text information from QR codes into speech for playback. It also utilizes left and right laser sensors for obstacle avoidance. Furthermore, the study conducted both simulation experiments and actual flight tests to validate the feasibility of equipping the Coanda‐drone with the voice information presentation system and to assess the flight capabilities of the Coanda‐drone. This system offers a novel solution for guidance and obstacle avoidance, demonstrating the potential of drones as flexible, highly secure, and efficient mobile service robots in the future. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Optimizing air traffic management through point merge procedures: Minimizing delays and environmental impact in arrival operations

We present an application of a mixed-integer programming (MIP) framework for automatic traffic synchronization, providing safe separation between the arriving traffic within the terminal maneuvering area (TMA) of an airport implementing point merge (PM) procedures. Additionally, the proposed methodology ensures conflict-free operations when departures and arrivals share a common runway. Based on real traffic scenarios for two European airports, we model realistic descent profiles and assume all the arrivals are performing the most fuel-efficient continuous descent operations (CDOs). We compare two scenarios: in the first, the arriving aircraft are strictly forced to adhere to the published arrival route structures, meaning that a turn towards the merge point may not be initiated prior to reaching the point merge system (PMS), while in the second scenario, aircraft may be assigned a shortcut from a published waypoint along the arrival route. We evaluate the resulting arrival flight efficiency and compare it to that of the actual flights, arriving during the hour selected for our optimization, noticing varying benefits for the two airports and whether shortcuts are allowed or not. Given the correct setting for the specific airport, we demonstrate that our approach provides significant benefits, including increased vertical performance as well as reduced time and distance, contributing to lower levels of noise and fuel savings, accompanied by reduced emissions.

Optimizing the function of aircraft WiFi based on the Internet of Things

Abstract. This paper explores the potential of full-duplex wireless communication technology to mitigate interference in aircraft WiFi systems, thereby enhancing the internet access quality and passenger experience. The study focuses on the application of Internet of Things (IoT) technology to improve the resilience of aircraft WiFi systems against interference. Through a series of MATLAB simulations, the research demonstrates that full-duplex technology can effectively cancel out interference signals, leading to clearer and more stable communication quality. The results show that the combined signal, after cancellation, closely resembles the original communication signal, indicating a significant reduction in interference. Despite the promising results, the study acknowledges limitations such as idealized assumptions in the simulation, hardware and software constraints, and the lack of consideration for the complex electromagnetic environment encountered during actual flight operations. Future research should focus on developing more advanced interference detection and mitigation algorithms, and further investigation into the integration of full-duplex technology with existing aircraft WiFi systems, considering hardware constraints and environmental factors, is necessary to fully realize its potential in enhancing communication performance.

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

Design of stiffness-variable dielectric elastomer wing based on seagull characteristics and its application in avian flight bionics

ABSTRACT Dielectric elastomers (DE), renowned for their lightweight, rapid response, high energy density, and efficient conversion, have garnered significant attention in the realm of avian flight bionics. However, a lack of understanding of the mechanical principles underlying flapping wing biomimetics has hindered accurate simulations of actual bird flight postures. To address this, a stiffness-variable DE-based wing (DEW) has been designed, inspired by the characteristics of seagulls, to mimic the wing deformations across different bird flight phases. The evolution of the DEW’s deformation is modeled based on the differential equation describing the bending curve of a DE cantilever beam. By applying different voltage cycles, three continuous avian flight postures have been replicated: takeoff, cruising, and hovering. The simulation results demonstrate that the stiffness-variable DEW effectively mimics the wing deformations observed in birds during different flight postures, closely resembling real-world flight conditions. This study has the potential to serve as an important reference for exploring changes in bird flight behavior, thereby advancing the application of DE materials in the field of soft robotics.

Improved modelling of low-pressure rotor speed in commercial turbofan engines: A comprehensive analysis of machine learning approaches

Accurate engine thrust modelling offers notable opportunities for reducing fuel consumption, mitigating emission effects, managing air traffic, and optimizing the preliminary design of aircraft. This study focuses on the development of machine learning models to predict the low-pressure rotor speed (N1) parameter, which is closely related to the thrust of turbofan engines, for commercial aircraft during the cruise phase. The analysis utilizes a Flight Data Recorder (FDR) dataset from 1086 actual flights involving twin-engine, narrow-body, short-to-medium haul commercial aircraft equipped with CFM56-7B turbofan engines. To achieve accurate N1 parameter predictions, the study employs machine learning models, including Deep Learning (DL), Random Forest (RF), Gradient Boosted Machines (GBM), and Artificial Neural Networks (ANN). These models are evaluated using statistically significant indicators, demonstrating that machine learning models yield highly accurate results. Among the models tested, the DL model provides the most precise estimates of the N1 parameter. This study not only advances the understanding of engine thrust modelling through machine learning but also provides practical insights for the aerospace industry. The findings underline the potential of machine learning techniques in delivering superior prediction accuracy, which can be integrated into real-time flight management systems, ultimately contributing to more sustainable and efficient aviation operations.

Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the environment. Previous studies focused on the total emissions of a flight and lacked high precision in both spatial and temporal resolutions, especially aviation activities near ground. In this research, we propose an open-sourced emission calculation framework based on actual flight trajectories (TrajEmission), which calculates both the ground and airborne emissions simultaneously according to the configuration parameters, trajectory characteristics, and ambient conditions. We compare the emission results with five emission inventory methods. The results indicate that pollutant (nitrogen oxides, carbon monoxide, and unburned hydrocarbons) emissions in the landing and takeoff (LTO) cycle might usually be underestimated due to a lack of trajectory-based methods. In addition, in the overall results, the method based on the great circle route leads to an overestimation of 56.8% of pollutant emissions compared to the method based on actual routes. We also investigate the extent to which other factors could influence the emission results. To summarize, the TrajEmission framework can build inventories for the whole process of flight movements with high spatial–temporal resolutions and provide solid data support for environmental science and other related fields.

Optimization of multi-path approach flight sequencing based on rolling horizon control

Arrival flight sequencing optimization is an effective method to improve the landing efficiency of incoming flights and reduce flight delays. Based on this, with the goal of maximizing landing efficiency, combined with multi-runway and multi-route selection, and considering the actual waypoint restrictions, a mixed integer programming model for multi-path and multi-runway integrated arrival flight sequencing optimization is proposed. In order to solve the real-time problem of large-scale flight sequencing calculation, a multi-waypoint rolling horizon control algorithm is proposed. The terminal area of Guangzhou Baiyun International Airport is used as an example for verification, and the actual arrival flight data is used to carry out calculation experiments. In terms of wake turbulence safety interval, the RECAT-CN operation standard is used. The calculation results show that when the number of small-scale flights (23 flights) is small, the maximum landing time of the proposed model is 55 s earlier than the first-come-first-served method and 271 s earlier than the non-optimized one; when the number of large-scale flights (104 flights) is large, the solver alone cannot find a feasible solution within 3600 s, and the proposed algorithm finds a solution within 128.65 s. The proposed model and algorithm are effective and can be applied to actual flight scheduling optimization.

Numerical Study on Heat Transfer and Thermal–Mechanical Performance of Actively Cooled Channel of All-Movable Rudder under Supercritical Pressure

The utilization of an actively cooled thermal protection system is widely recognized as an effective approach to decrease the temperature of components exposed to severe aerodynamic heating. In this study, two cooling schemes with different flow paths and structural configurations were proposed, and six cooling channel designs were developed by modifying the leading-edge details. A numerical analysis on the heat transfer and thermal–mechanical performance was conducted under actual flight conditions (30 km altitude, Mach 8). The results highlight an optimal design scheme that balances temperature control and minimized coolant flow rates. The channel flow field demonstrated its superiority by effective convective heat transfer and improved fluid mixing facilitated through recirculation zones and turbulence at the bends. Structural assessments showed that the optimal scheme not only provided better cooling but also preserved the structural integrity. Overall, the study offers a practical and effective thermal protection approach for air rudders subjected to severe heat.

The efficiency of detecting seabird behaviour from movement patterns: the effect of sampling frequency on inferring movement metrics in Procellariiformes

BackgroundRecent technological advances have resulted in low-cost GPS loggers that are small enough to be used on a range of seabirds, producing accurate location estimates (± 5 m) at sampling intervals as low as 1 s. However, tradeoffs between battery life and sampling frequency result in studies using GPS loggers on flying seabirds yielding locational data at a wide range of sampling intervals. Metrics derived from these data are known to be scale-sensitive, but quantification of these errors is rarely available. Very frequent sampling, coupled with limited movement, can result in measurement error, overestimating movement, but a much more pervasive problem results from sampling at long intervals, which grossly underestimates path lengths.MethodsWe use fine-scale (1 Hz) GPS data from a range of albatrosses and petrels to study the effect of sampling interval on metrics derived from the data. The GPS paths were sub-sampled at increasing intervals to show the effect on path length (i.e. ground speed), turning angles, total distance travelled, as well as inferred behavioural states.ResultsWe show that distances (and per implication ground speeds) are overestimated (4% on average, but up to 20%) at the shortest sampling intervals (1–5 s) and underestimated at longer intervals. The latter bias is greater for more sinuous flights (underestimated by on average 40% when sampling > 1-min intervals) as opposed to straight flight (11%). Although sample sizes were modest, the effect of the bias seemingly varied with species, where species with more sinuous flight modes had larger bias. Sampling intervals also played a large role when inferring behavioural states from path length and turning angles.ConclusionsLocation estimates from low-cost GPS loggers are appropriate to study the large-scale movements of seabirds when using coarse sampling intervals, but actual flight distances are underestimated. When inferring behavioural states from path lengths and turning angles, moderate sampling intervals (10–30 min) may provide more stable models, but the accuracy of the inferred behavioural states will depend on the time period associated with specific behaviours. Sampling rates have to be considered when comparing behaviours derived using varying sampling intervals and the use of bias-informed analyses are encouraged.

Enhancing pilot vigilance assessment: The role of flight data and continuous performance test in detecting random attention loss in short IFR flights

Situational awareness (SA) and fatigue management are crucial aspects of aviation safety, particularly during demanding flight phases. This study introduces an innovative approach employing flight data, machine learning, and Continuous Performance Test (CPT) metrics to predict pilot performance and SA during instrument approaches under Instrument Meteorological Conditions (IMC). Data were collected from over 10 pilots across more than 40 flights in a high-fidelity Cessna 172 analog flight simulator.Significant correlations were observed between dynamic cognitive performance parameters and the exceedance shape factor, a novel measure of pilot sustained attention introduced in this research. Key variables identified through correlation analysis included variability, interstimulus change, and reaction time standard deviation.Importantly, commission scores and reaction time standard deviation emerged as key predictors in the machine learning model, specifically the Optimizable Gaussian Process Regression (GPR) model with a radial basis function kernel. The model achieved a validation R-squared of 0.90 and a test R-squared of 0.70. These systems could incorporate additional data sources, such as eye-tracking and scan pattern analysis, for a better assessment of pilot SA and fatigue levels. While post-flight measurements are inherently reactive, they are effective for monitoring the degradation of pilot CPT scores after each leg of high-frequency, short-duration flights.Notable limitations include the need to understand individual cognitive differences among pilots, such as age, experience, and cognitive style. The predictive model also requires validation in actual flight conditions to determine its ecological validity. Future research should aim to address these limitations, generalize the findings, and integrate CPT data with other sensor inputs to provide a more comprehensive understanding of pilot performance.

Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective

China's rapid expansion of civil aviation has led to an increase in pollution-related issues, causing adverse health effects on populations near airports and downwind. Accurately quantifying aviation emissions is essential for effective emission management. Here, we developed a high-resolution aviation emissions inventory for China by employing a bottom-up approach that relied on daily flight schedules. By using the Aeronautical Information Publication (AIP) to reproduce real-world flight routes rather than conventional great-circle routes, we improved the accuracy of emissions and investigated the potential for reducing these emissions. Our findings demonstrated substantial variations in domestic civil aviation emissions both spatially and temporally. Emissions peaked in most provinces during Chinese holidays, particularly the Chinese Lunar New Year and summer holidays, highlighting the importance of detailed activity data for accurate emissions calculations. Therefore, we recommend extensive utilization of real-world flight routes, particularly in areas with limited Automatic Dependent Surveillance-Broadcast (ADS-B) coverage since they provide more accurate representations of actual flight trajectories. Our study also identified regions like Shaanxi, Sichuan, Beijing, and their surroundings having considerable potential for emission reduction due to substantial deviations from great-circle routes. This approach can enhance the accuracy and spatiotemporal resolution of aviation emissions at national and global scales throughout the year, without relying on extensive, long-term real-time flight trajectories. Additionally, it provides a unique way to quantify the potential for emission reductions across provinces in civil aviation, ultimately contributing to mitigating pollution-related health impacts from aviation emissions and promoting a more sustainable aviation industry.

Enhancing low-temperature lithium-ion battery performance under high-rate conditions with niobium oxides

Low-temperature operation (−20 °C and below) under high-rate conditions is a critical deficiency for lithium-ion batteries. To achieve size, weight, and power requirements tailored for demanding applications, novel materials are needed to sustain high performance. In the present study, we synthesize a series of niobate anode materials (Nb2O5, Nb2O5–x, and Nb12O29) and tailor their particle size, defect nature, and electrical/ionic conductivity to enable high-performance operation at −20 °C under high-rate conditions (1.2C–2C). When paired with lithium manganese oxide (LMO) in a full-cell configuration, the Nb2O5–x-based full-cells achieve high-rate capability (∼90 mAh/g up to 2C cycling rate at −20 °C) and great long-term stability (>98% retention up to 50 cycles at −20 °C). During a simulated 30 min duty cycling test synthesized from measured data from an actual drone flight (continuous range of 1.2C–2C cycling rates), the Nb2O5–x||LMO cell enables full discharge at −20 °C, with only a 0.3 V voltage drop when compared to duty cycling at room temperature. The work presented herein demonstrates the future possibilities of expanding the operational capabilities of lithium-ion batteries.

Numerical simulation of ice accretion on helicopter rotor blades with trailing edge flap

PurposeThis paper aims to describe a numerical simulation method of ice accretion on BO105 helicopter blades for predicting the effects of trailing edge flap deflection on ice accretion.Design/methodology/approachA numerical simulation method of ice accretion is established based on Myers model. Next, the shape and location of ice accretion of NACA0012 airfoil are calculated, and a comparison between calculated results and experimental data is made to validate the method. This method is used to investigate the effect of trailing edge flap deflection on ice accretion of a rotor blade.FindingsThe numerical method is feasible and effective to study the ice accretion on helicopter rotor blades. The downward deflection of the trailing edge flap affects the shape of the ice.Practical implicationsThis method can be further used to predict the ice accretion in actual flights of the helicopters with multielement airfoils.Originality/valueThe numerical simulation method here can lay a foundation of the research about helicopter flight performance in icing condition through predicting the shape and location of ice accretion on rotor blades.

Study on dynamic scheduling method of airport refueling vehicles based on DQN

Aiming at the low utilization rate of airport refueling vehicles and long solution time of exact algorithm caused by the uncertainty of actual flight time, a deep Q network dynamic scheduling method for refueling vehicles combining with the multi-objective deep reinforcement learning framework was proposed. Firstly, an optimization model is established to maximize the on-time rate of refueling tasks and the average proportion of idle vehicles. Then, the five state features that measure the current state of the vehicle are designed as inputs to the network. According to the two objectives, the two scheduling strategies are proposed as the action space so that the algorithm can generate the dynamic scheduling scheme based on the dynamic flight data in real time. Finally, the dynamic scheduling model for airport refueling vehicles is solved, and the effectiveness and real-time performance of the algorithm are verified by different scale examples. The results show that the average number of on-time refueling tasks per day is 9.43 more than that via manual scheduling, and the average working time of vehicles is reduced by 57.6 minutes, which shows the excellent ability of the present method in solving the dynamic scheduling problem of refueling vehicles.

Aero-propulsive coupling modeling and dynamic stability analysis of distributed electric propulsion tandem-wing UAV with rapid ascent capability

The distributed electric propulsion tandem-wing unmanned aerial vehicle (UAV) offers higher aerodynamic efficiency than conventional electric vertical takeoff and landing UAV and superior rapid ascent takeoff and landing capabilities compared to regular fixed-wing aircraft. However, for this configuration, the aero-propulsive coupling effects between the propellers and wings are significant. To study the mutual interaction between distributed electric propulsion and aerodynamics on the flight dynamics characteristics and rapid ascent flight capability of UAV, an aero-propulsive coupling model incorporating the effects of propellers slipstream is established. Ground-based blown wing experiment is conducted to validate the modeling accuracy. Subsequently, a theoretical modeling framework based on standard stability derivatives is developed, incorporating the influence of thrust on the dynamics within stability derivatives. The dynamic characteristics of distributed electric propulsion tandem-wing configuration are then studied and aero-propulsive coupling effects on natural modes of motion and flight qualities are analyzed. Finally, flight simulations and actual flight tests are conducted to validate the rapid ascent flight capability of UAV in real-world. The experimental results indicate that UAV can achieve rapid ascent flight with significant pitch angles and high climb rates, showing promising results with more than a 91.9% improvement in takeoff performance compared to conventional rolling takeoff.

Accuracy analysis of UAV aerial photogrammetry based on RTK mode, flight altitude, and number of GCPs

Abstract The optimization of an unmanned aerial vehicle (UAV) aerial photogrammetry scheme is crucial for achieving higher precision mapping results. Three representative factors, namely the real-time kinematic (RTK) mode, flight altitude, and the number of ground control points (GCPs) were selected to analyze their impact on UAV aerial photogrammetry accuracy. Four flight altitude tests were conducted separately in two RTK modes, and five GCP layout schemes were designed. Based on this, the root mean square error (RMSE) values of 40 aerial photogrammetric results were analyzed. The results showed a significant correlation between flight altitude and resolution of the UAV aerial photogrammetric results. Further, conversion formulas between actual image resolution and flight altitude for different GCP values were also derived in RTK and non-RTK modes. In the case of precise positioning, the horizontal and vertical accuracy of the aerial photogrammetric image decreased with increasing flight altitude. Under the same flight altitude, the addition or no addition of GCPs, including changes in GCP numbers, had no significant effect on improving the accuracy of aerial photogrammetry in RTK mode. However, in non-RTK mode, the number of GCPs significantly affected accuracy. The horizontal and vertical RMSE values decreased rapidly with the increase in GCP numbers and then stabilized. However, regardless of whether RTK was activated, an excessive number of GCPs was not conducive to improving the accuracy of aerial photogrammetric results. The mapping accuracy of UAVs in RTK mode without GCPs was equivalent to that in non-RTK mode with GCPs. Therefore, when using RTK-UAVs, deploying GCPs is unnecessary under suitable circumstances. Finally, practical suggestions for optimizing the UAV aerial photogrammetry scheme are provided as a reference for related applications.

Experimentation on Leader-Following Formation for Fixed-Wing UAVs

Fixed-wing unmanned aerial vehicles (UAVs) are a primary focus of current UAV research. Challenges arise in their flight due to high speed and complex maneuverability. This paper explores the coordinated turn guidance law for fixed-wing UAVs and validates an experimental leader-follower formation platform in flight. Results demonstrate the effectiveness of the proposed algorithm and platform in enabling actual leader-follower formation flights for fixed-wing UAVs.