Flight Test Research Articles

Reducing contaminating noise effects when calculating low-boom loudness levels.

During NASA X-59 quiet supersonic aircraft community response tests, low-boom recordings will contain contaminating noise from instrumentation and ambient acoustical sources. This noise can inflate sonic boom perception metrics by several decibels. This paper discusses the development and comparison of robust lowpass filtering techniques for removing contaminating noise effects from low-boom recordings. The two filters are a time-domain Butterworth-magnitude filter and a frequency-domain Brick Wall filter. Both filters successfully reduce noise contamination in metric calculations for simulated data with real-world contaminating noise and demonstrate comparable performance to a modified ISO 11204 correction. The Brick Wall filter's success indicates that further attempts to match boom spectrum high-frequency roll-off beyond the contaminating noise floor are unnecessary and have marginal improvements on final metric calculations. Additionally, the Butterworth filter removes statistical correlation between ambient and boom levels for a real-world flight campaign, adding evidence that these techniques also work on other boom shapes. Overall, both filters can produce accurate metric calculations with only a few hundred hertz of positive signal-to-noise ratio. This work describes methods for accurate metric calculations in the presence of moderate noise contamination that should benefit X-59 and future low-boom supersonic aircraft testing.

Robust Geometric Control for a Quadrotor UAV with Extended Kalman Filter Estimation

This study proposes a robust geometric controller tailored for quadrotor unmanned aerial vehicles (UAVs). The original geometric controller exhibits excellent performance in quadrotor UAV maneuvers. However, as a model-based nonlinear control method, it is sensitive to system model parameters. By integrating a novel extended Kalman filter (EKF)-based estimator for real-time, online estimation of the quadrotor’s inertia parameters, the controller adeptly handles internal uncertainties and external perturbations during flight maneuvers. This approach significantly improves the robustness of the control system against model inaccuracies. Empirical evidence is provided through both simulation and extensive real-world flight tests, demonstrating the controller’s effectiveness and its practical applicability in dynamic environments. The results confirm that this integration substantially enhances system reliability and performance under varied operational conditions.

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.

Design, Construction, and Flight Performance of an Electrically Operated Fixed-Wing UAV

The development of unmanned aerial vehicles (UAVs) has attracted much attention in the global community and aviation industry. As UAVs have the potential to be applied for multiple missions, the level of research into improving their design and flight performance has also increased. In this context, the present paper aims to present the design, construction, and flight performance of an electrically operated fixed-wing UAV. As a first step in the design process, key performance requirements are defined, such as the thrust required, the stall speed, the minimum drag velocity, and the minimum power velocity. Wing and associated power loadings are calculated according to the defined performance requirements. In addition, payload and endurance requirements are set up in order to determine the wing and tail areas, the total mass, the power requirements, and the motor size. Aerodynamics and stability designs are also calculated. After the completion of the design process, the manufacturing of the UAV follows by using appropriate materials. Flight tests were carried out for the evaluation of the UAV’s flight performance, where the success of the design was demonstrated.

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Machine Learning Opportunities in Flight Test: Preflight Checks

Machine Learning Opportunities in Flight Test: Preflight Checks

Epilots : A System to Predict Hard Landing During the Approach Phase of Commercial Flights

This project aims to develop a system called E-Pilots that uses machine learning algorithms to predict hard landings during the approach phase of commercial flights.The system will analyze flight data to precede hard landings.The goal is to provide pilots with real-time warnings and guidance to prevent accidents and improve safety.The research methodology includes the collection and analysis of flight data, the development and testing of machine learning algorithms, and the integration of the E-Pilots system with existing flight systems.The findings of this project are expected to contribute to the improvement of aviation safety and reduce the occurrence of hard landings. The implications of this research may also extend to other areas of aviation safety and flight automation.

A deep reinforcement learning approach for dynamic task scheduling of flight tests

A deep reinforcement learning approach for dynamic task scheduling of flight tests

Quantifying Impacts of Uncertainties on Certification-Driven Design

Airworthiness certification is challenging for novel aircraft concepts. To avoid the significant cost associated with the redesign for certification compliance, incorporating certification requirements into early aircraft design is desired for unconventional aircraft. However, epistemic uncertainties arising from modeling assumptions, and aleatory uncertainties stemming from uncontrollable noise factors may have impacts on the certification analysis and certification-constrained design process. This paper presents an uncertainty quantification study based on a certification-constrained design and optimization study previously conducted for NASA’s Parallel Electric–Gas Architecture with Synergistic Utilization Scheme (PEGASUS) concept. The epistemic uncertainties are modeled through a set of multiplicative factors applied to intermediate disciplinary variables. The sensitivity analysis between design metrics and multiplicative factors reveals that uncertainties in stability and control derivatives can significantly affect certification constraint predictions, and uncertainties in drag approximations have considerable impacts on the vehicle sizing process. The aleatory uncertainties added to flight dynamics simulations include wind velocities and variations of weight and center of gravity. Four representative design candidates are evaluated for their robustness against aleatory uncertainties based on the Monte Carlo simulation performed on noise factors. The results show that aleatory uncertainties can affect the aircraft dynamic responses in flight test simulations, thus compromising certification compliance.

Aggressive flight control of quadrotors using incremental nonlinear dynamic inversion with a high-fidelity thrust unit model

The application of quadrotors has been expanded into aggressive tasks such as military confrontation and aerobatics shows, making it crucial to enhance flight maneuverability to adapt to rapid and large-scale command changes. Unlike hovering or low-speed modes, aggressive flight exacerbates quadrotor model uncertainty, making it challenging for controllers to balance response speed and robustness. This paper proposes an INDI control method that integrates a high-fidelity thrust model unit to address this practical need. First, based on the pole distribution theory and the application of INDI on quadrotors, it is concluded that the dynamic response of quadrotor flight is impacted by the accuracy of the thrust model. Second, consider the airflow velocity and angles, the high-fidelity modeling approach, and the aerodynamic forces and torque expression validated through wind tunnel experiments are provided. Then the flight controller for the quadrotor is designed with the established high-fidelity thrust unit model. Finally, aggressive trajectory-tracking flight tests are set to evaluate the improvement of our work. The results demonstrate that our proposed method indeed enhances the response speed and tracking accuracy, as well as the flight stability of the quadrotor.

Angular acceleration estimation and aerodynamic parameter identification based on angular velocity equivalent model

Conventional angular acceleration estimation methods generate non-negligible errors when the control surface rapidly deflects; hence, the estimated angular acceleration cannot be used for aerodynamic parameter identification directly. This paper proposes an angular acceleration estimation method based on angular velocity equivalent model. The angular acceleration of the angular velocity equivalent model, whose angular velocity response is consistent with the flight test data, is used as the estimated angular acceleration. Firstly, the equivalent angular velocity model is established by combining the generic aerodynamic model with the rotational dynamic equations of aircraft. Secondly, the angular velocity data and the control surface deflection data are synchronized. Finally, the angular velocity response of the equivalent model is used as the predictor, and the estimated angular acceleration is obtained by extended Kalman filter. The aerodynamic parameter identification and validation are carried out using the estimated angular acceleration of a large civil aircraft flight test data. The results show that the angular acceleration obtained through the proposed angular acceleration estimation method meets the accuracy requirement of aerodynamic parameter identification.

Design and Analysis of a Base Bleed Unit for the Drag Reduction of a High-Power Rocket Operating at Transonic Speeds

In the present study, a passive flow device is considered for drag reduction purposes through implementation in a transonic high-power rocket. The high-power rocket serves as a reference platform that, apart from the operating conditions, enforces several constraints in terms of available volume and placement locations. A step-by-step methodology is suggested, where the unit is initially broken down into an inlet and an outlet component. The flow field is investigated by means of computational modeling (CFD), where the Reynolds-averaged Navier–Stokes equations are solved coupled with turbulence models that vary depending on the design phase and the individual component. In the first design phase, the best alternative configuration is selected for each component by comparing mass flow rates and discharge coefficients. In the second design phase, each component is analyzed in greater detail based on the first phase results. Indicatively, the protruding inlet diffuser-type channel is converted into a protruding inlet nozzle-type channel to avoid choked flow phenomena, and a nozzle geometry is selected as the outlet amongst the other considered scenarios. The two components are eventually integrated into a common base bleed unit and a final assessment is made. The computational results are used to predict the performance and trajectory of the rocket through a well-established trajectory software. The overall methodology is validated against full-scale test flight data. The results show that the base bleed unit developed in the framework of this study yields a drag reduction of approximately 15% at transonic speeds without impacting the rocket mass and stability.

Research on the drone endoscopic system for fusion reactors

This paper proposes an air-ground separation drone endoscopic system (DES) design scheme for the fusion reactor internal inspection task. A foldable rotor drone with vertical takeoff and landing capability is used as the main body of DES. The nest platform is designed separately from the drone to reduce the weight. Meanwhile, the nest platform can be installed with a safety cable and 2D code to realize emergency rescue and precise take-off and landing. DES adds 3D lidar sensors for 360° stereo sensing, realizing real-time positioning in dark or even lightless environments in vacuum chambers. The first-generation DES prototype is developed, and the flight test is performed inside the PF6 superconducting coil shell by fusing 3D lidar point cloud data with visual camera image data. The results show that the position error of DES is stabilized within ±0.05 m during the hovering phase. The error is mainly due to the narrow space inside the PF6 coil shell, which produces a perturbed flow field on the DES. In general, the DES accomplished circumferential autonomous flight control and 3D point cloud data acquisition in a confined space, proving the feasibility of drone inspection in vacuum chambers.

Computer Simulation of a Capillary “Jump” of a Mercury Drop or an Air Bubble Under Conditions of Short-Term Weightlessness

The capillary “jump” of drop of mercury or an air bubble that occurs during the transition of gravity acceleration from earth to microgravity is called a drop of mercury sharp “jump” starting from the bottom of a container with water, where it is located, to its top or a sharp “jump” of the surface of the air shell, which has turned into a spherical air bubble, starting from the top of a container with water down to the bottom of the container. Article presents both a computer model of a capillary “jump” of a mercury drop in a container with water and computer model of a capillary “jump” of an air bubble in a container with water, previously developed by author during the transition of gravitational acceleration from earthly to microgravity. The developed computer models, implemented in the Turbo C and Turbo Basic computer languages, permitted to simulate on the computer display a capillary “jump” of mercury drop and an air bubble in container with water; quantitatively to calculate the energy of motion of a capillary “jump” of a mercury drop and an air bubble in container with water during the transition of the acceleration of gravity from earth to microgravity. The results of calculations of the energy of motion of a capillary “jump” of a mercury drop showed that the surface tension of a mercury drop plays a significant role in the driving of a mercury drop compared to its density. Flight tests have shown that the developed computer models can be successfully used in main systems of spacecraft under conditions of microgravity to predict the occurrence of a capillary “jump” of wide range real mercury drops and air bubbles in these systems.

Density gradient-RRT: An improved rapidly exploring random tree algorithm for UAV path planning

In-depth studies of algorithms for solving motion planning problems have been conducted due to the rapid popularization and development of unmanned aerial vehicles in previous decades. Among them, the classic rapidly exploring random tree (RRT) algorithm has derivative algorithms (e.g., RRT*, Q-RRT*, and F-RRT*) that focus on the optimal path cost of the initial solution. Other improved algorithms, such as RRT-connect and BG-RRT, focus on the optimal time of the initial solution. This article proposes an improved density gradient-RRT (DG-RRT) algorithm based on RRT that improves the efficiency of the guide point and reduces the time lost in the process of obtaining the initial solution through the dynamic gradient sampling strategy. Simultaneously, it reduces the path cost by reconstructing the output path. The proposed algorithm is an expansion algorithm of a random tree, and the performance of the algorithm can be further improved by combining it with other RRT optimization algorithms. DG-RRT and other algorithms are compared in different environments through simulation experiments to verify the advantages of DG-RRT. In addition, it used a set of simulation flight tests to verify the feasibility of the DG-RRT algorithm for UAV path planning.

Air-Ground Characteristic Test and Analysis on Fan Blade Vibration Stress of Turbofan Engine

Obtaining the vibration stress of fan rotor blades under actual working conditions through flight tests is a necessary means to carry out design verification and optimization improvement of turbofan engine. Taking the high bypass ratio turbofan engine as research object, installed ground and flight tests were carried out. The vibration characteristics and stress distribution of the fan blade were obtained by direct measurement means of strain gauge, and the air-ground characteristics of the test data were compared and analyzed. The results show that under the steady state, the vibration stress level of fan blade in ground test is significantly higher than that in flight test; There is a significant difference in frequency distribution of excitation factors on fan blade in the ground and flight tests.

Research on Simulation Technology of Excitation Response of Aircraft Structural Based on Finite Element Model in Flutter Flight Test

To simulate excitation response of the aircraft structural of flutter test aircraft under different excitation methods. A simulation method for excitation response of flutter flight tests based on finite element models has been proposed. This method is based on the finite element model of the aircraft structure. Establish aerodynamic and excitation force models for flutter flight tests. Obtain a simulation model for the excitation response of aircraft flutter flight tests. Solve the model and simulate various excitation methods for flutter flight tests. Firstly, a detailed introduction was given to the excitation modeling method for aircraft flutter flight tests, including two excitation models: external excitation and control surface excitation. Then, the finite element model of the structural dynamics of a single wing with ailerons is taken as the research object. Establish a single wing flutter flight test excitation response model. Implemented simulation of control surface sweep excitation, control surface pulse excitation, and small rocket excitation. The feasibility of this method has been verified. Finally, based on the finite element model of the entire aircraft, a flutter flight test excitation response simulation model was established. We have conducted analysis and research on sensor position optimization, excitation method optimization, excitation response amplitude prediction, and flutter boundary prediction. And the simulation optimization analysis results were compared with the flight test results. Analyzed the promoting effect of flutter flight test excitation response simulation on flight tests.

Numerical Investigation of Gas Models for Retropropulsion Flows for Future Mars Missions

Supersonic retropropulsion is a key technology for next-generation rockets and necessary to land large payloads for human-scale missions. The recent decade has demonstrated the capability to land rockets on Earth as well as the reuse of said rockets to reduce cost. This capability thus far has relied on flight tests. For future missions to Mars, testing requires many compromises, as flight tests are not possible on Earth. Computational fluid dynamics (CFD) simulations are critical to the design and analysis of extraplanetary retropropulsion configurations. This work investigates a single-engine configuration that was previously tested in a wind tunnel using an air medium. The configuration is scaled to Martian conditions. Various gas modeling approaches are employed, including pure CO2, pseudo-species, and a chemical mechanism to model the afterburning of the engine plumes in the Martian atmosphere. The scale-resolving CFD simulations are compared to the simulation and experimental data of the air configuration. The lower-fidelity gas modeling simulations are also compared against the higher-fidelity chemical mechanism simulations to examine the impact of gas models on plume flowfields and forces and moments that are of interest to vehicle design.

Research on icing scaling technology for large civil aircraft icing wind tunnel test

This article explores the icing scaling technology for large civil aircraft icing wind tunnel tests. Based on the typical critical icing conditions of large civil aircraft and the calibration of icing wind tunnel, and through the similarity analysis of water droplet trajectories, water collection, and energy balance, four similar parameters, namely modified inertia parameter, drop energy transfer parameter, stagnation freezing coefficient, and accumulation parameter, were selected as similarity criteria for icing scaling. Based on this criteria, icing scaling was conducted on the critical icing conditions of large civil aircraft, and the scaling results were evaluated by icing code. The results show that the water droplet collection and the ice shape before and after the scaling are in good agreement, and the similarity criteria used are appropriate. Finally, icing wind tunnel test was conducted in 3m × 2m test section using scaled icing conditions. The test results show that the main characteristics of the experimental ice shape are in good agreement with the calculated ice shape, and the calculated ice shape is more critical. The calculated ice shape can be used as a critical ice for aerodynamic wind tunnel tests and flight tests with simulated ice.

Using mylar-insulated cryopumping panels to improve vacuum level during warm temperature testing at JSC’s large thermal vacuum facilities

Johnson Space Center’s Space Environment Simulation Lab (SESL) has both Chamber A, the world’s largest purpose-built thermal vacuum chamber capable of creating deep space conditions, and Chamber B, the largest human rated thermal vacuum chamber. A unique design feature of these chambers is the gaseous helium cryopumping panels within the liquid nitrogen shroud. This shroud is used to bring the chamber to cryogenic temperatures while the cryopumping panels trap gasses on its surface area to create a high vacuum environment of 5*10−6 Torr. In preparation for the James Webb Space Telescope (JWST) flight test, a series of functionals required the chamber to run at higher temperatures, and therefore did not need active cooling from the liquid nitrogen shroud. During testing, cryopumping panels were used to mitigate contamination during this main shroud warm-up. One of the cryopumping panels in Chamber A was covered with several layers of aluminized mylar to thermally protect the zone from the warmed shroud. This strategy was effective and became part of operations during JWST testing. Currently, Chamber B has requests for both commercial and NASA space suit tests at both high and low temperatures to validate thermal models. High temperature tests could benefit from reduced heater demand with an insulated shroud while still sustaining the high vacuum environment provided by the cryopumping panels. This paper will quantitatively define the thermal loads on the panels used in previous Chamber A warm up sequences. Additionally, this report will perform thermal analyses to assess the feasibility of adding layers of aluminized mylar to the cryopumping panels of Chamber B.