Flight Maneuvers Research Articles

Machine learning reveals the control mechanics of an insect wing hinge.

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs1, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings2. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder4 that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

Joint extension speed dictates bio-inspired morphing trajectories for optimal longitudinal flight dynamics.

Avian wing morphing allows dynamic, active control of complex flight manoeuvres. Previous linear time-invariant (LTI) models have quantified the effect of varying fixed wing configurations but the time-dependent effects of morphing between different configurations is not well understood. To fill this gap, I implemented a linear parameter-varying (LPV) model for morphing wing gull flight. This approach models the wing joint angles as scheduled parameters and accounts for nonlinear kinematic and gravitational effects while interpolating between LTI models at discrete trim points. With the resulting model, I investigated the longitudinal response associated with various joint extension trajectories. By optimizing the extension trajectory for four independent objectives (speed and pitch angle overshoot, speed rise time and pitch angle settling time), I found that the extension trajectory inherent to the gull wing does not guarantee an optimal response but may provide a sufficient response with a simpler mechanical implementation. Furthermore, the results indicated that gulls likely require extension speed feedback. This morphing LPV model provides insights into underlying control mechanisms, which may allow for avian-like flight in future highly manoeuvrable uncrewed aerial vehicles.

Study on the melting characteristics of composite phase change materials and the power generation performance of coupled thermoelectric systems under supergravity conditions

Miniaturization and integration of electronic equipment in aircraft have resulted in a substantial increase in thermal load. However, aircraft often experience acceleration maneuvering flights in supergravity environments, which makes comprehensive research on thermal management under supergravity conditions necessary. The influence of gravity on the thermal storage properties of composite phase change materials doped with graphene nanoparticles (GNP-PCM) was investigated in this study by simulation. Under 2g, 4g, 6g, 8g, and 10g gravity conditions, the melting time is reduced by 14.86%, 27.05%, 33.31%, 37.00%, and 39.79%, respectively, compared with normal gravity conditions. Moreover, the integration of the PCM with a thermoelectric power system facilitated waste heat utilization. Incorporating GNPs markedly enhances power generation efficiency under 10g supergravity conditions. With an open-circuit voltage of 0.8 V and a maximum output power of 0.11 W, the power generation reached 81.62 J, which is 53.91% higher than that of pure PCM. This enhancement can be attributed to the improved natural convection and accelerated melting rate facilitated by the GNPs. This study elucidates the underlying physical mechanisms affecting the melting rate of PCM under increased gravity conditions. Furthermore, coupling PCM with a thermal power generation system provides a rational approach for the reuse of waste heat.

Linearized Models of the Coupled Rotorcraft Flight Dynamics and Acoustics for Real-Time Noise Prediction

This article demonstrates the linearization of the coupled rotorcraft flight dynamics and aeroacoustics to provide realtime acoustic predictions in generalized maneuvering flight. To demonstrate the methodology, the study makes use of a nonlinear simulation model of a generic utility helicopter (PSUHeloSim) that is coupled with an aeroacoustic solver based on a marching cubes algorithm. A periodic equilibrium of the coupled rotorcraft flight dynamics and acoustics is first found at a desired flight condition using a modified harmonic balance trim solution method. Next, the nonlinear time-periodic dynamics are linearized about that periodic equilibrium and transformed into an equivalent higher order linear time-invariant system in harmonic decomposition form. Composite aeroacoustic measures are included as an output of this system. To speed up runtime and make control design tractable, the order of these harmonic decomposition models is reduced via residualization to an 8-state model where the states are representative of the rigid-body dynamics of the aircraft. This 8-state model is shown to provide accurate acoustic response predictions for small-amplitude pilot inputs and to abate runtime by a factor of approximately 104, thus enabling acoustic predictions in generalized maneuvering flight that are significantly faster than real time. The 8-state model is subsequently used to demonstrate the use of linear system tools for the dynamic analysis of the coupled rotorcraft flight dynamics and acoustics.

Parametric Design Method and Lift/Drag Characteristics Analysis for a Wide-Range, Wing-Morphing Glide Vehicle

The parametric design method is widely utilized in the preliminary design stage for hypersonic vehicles; it ensures the fast iteration of configuration, generation, and optimization. This study proposes a novel parametric method for a wide-range, wing-morphing glide vehicle. The whole configuration, including a waverider fuselage, a rotating wing, a blunt leading edge, rudders, etc., can be easily described using 27 key parameters. In contrast to the typical parametric method, the new method takes internal payloads into consideration during the shape optimization process. That is, the vehicle configuration can be flexibly adjusted depending on the internal payloads; these payloads may be of random amounts and have different shapes. The code for the new parametric design method is developed using the secondary development tools of UG (UG 10.0) commercial software. The lift and drag characteristics over a wide operational range (H = 6–25 km, M = 2.5–8.5, AOA = 0–10°) were numerically investigated, as was the influence of the retracting angle of the morphing wings. It was found that, for the mode of the fully deployed wings, the lift-to-drag ratio (L/D) remained at a high level (≥4.7) over a Mach range of 4.0–8.5 and an AOA range of 4–7°. For the mode of the fully retracted wings, the drag coefficient remained smaller than 0.02 over a Mach range of 4.0–8.5 and an AOA range of 0–5°. A wide L/D of 0.3–4.7 could be achieved by controlling the retracting angle of the wings, thus demonstrating a good potential for flight maneuverability. The flexible change in L/D proved to be a combined result of varying pressure distribution and edge-flow spillage. This will aid in the further optimization of lift/drag characteristics.

The Functions of Phasic Wing-Tip Folding on Flapping-Wing Aerodynamics.

Insects produce a variety of highly acrobatic maneuvers in flight owing to their ability to achieve various wing-stroke trajectories. Among them, beetles can quickly change their flight velocities and make agile turns. In this work, we report a newly discovered phasic wing-tip-folding phenomenon and its aerodynamic basis in beetles. The wings' flapping trajectories and aerodynamic forces of the tethered flying beetles were recorded simultaneously via motion capture cameras and a force sensor, respectively. The results verified that phasic active spanwise-folding and deployment (PASFD) can exist during flapping flight. The folding of the wing-tips of beetles significantly decreased aerodynamic forces without any changes in flapping frequency. Specifically, compared with no-folding-and-deployment wings, the lift and forward thrust generated by bilateral-folding-and-deployment wings reduced by 52.2% and 63.0%, respectively. Moreover, unilateral-folding-and-deployment flapping flight was found, which produced a lateral force (8.65 mN). Therefore, a micro-flapping-wing mechanism with PASFD was then designed, fabricated, and tested in a motion capture and force measurement system to validate its phasic folding functions and aerodynamic performance under different operating frequencies. The results successfully demonstrated a significant decrease in flight forces. This work provides valuable insights for the development of flapping-wing micro-air-vehicles with high maneuverability.

Experimental Nonlinear and Incremental Control Stabilization of a Tail-Sitter UAV with Hardware-in-the-Loop Validation

Tail-sitters aim to combine the advantages of fixed-wing aircraft and rotorcraft but require a robust and fast stabilization strategy to perform vertical maneuvers and transitions to and from aerodynamic flight. The research conducted in this work explores different nonlinear control solutions for the problem of stabilizing a tail-sitter when hovering. For this purpose, the first controller is an existing strategy for tail-sitter control obtained from the literature, the second is an application of Nonlinear Dynamic Inversion (NDI), and the last one is its incremental version, INDI. These controllers were implemented and tuned in a simulation in order to stabilize a model of the tail-sitter, complemented by estimation methods that allow the feedback of the necessary variables. These estimators and controllers were then implemented in a microcontroller and validated in a Hardware-in-the-Loop (HITL) scenario with simple maneuvers in vertical flight. Lastly, the developed control solutions were used to stabilize the aircraft in experimental flight while being monitored by a motion capture system. The experimental results allow the validation of the model of the X-Vert and provide a comparison of the performance of the different control solutions, where the INDI presents itself as a robust control strategy with accurate tracking capabilities and less actuator demand.

Design of Pseudo-Command Restricted Controller for Tailless Unmanned Aerial Vehicles Based on Attainable Moment Set

This work investigates the pseudo-command restricted problem for tailless unmanned aerial vehicles with snake-shaped maneuver flight missions. The main challenge of designing such a pseudo-command restricted controller lies in the fact that the necessity of control allocation means it will be difficult to provide a precise envelope of pseudo-command to the flight controller; designing a compensation system to deal with insufficient capabilities beyond this envelope is another challenge. The envelope of pseudo-command can be expressed by attainable moment sets, which leave some open problems, such as how to obtain the attainable moment sets online and how to reduce the computational complexity of the algorithm, as well as how to ensure independent control allocation and the convexity of attainable moments sets. In this article, an innovative algorithm is proposed for the calculation of attainable moment sets, which can be implemented by fitting wind tunnel data into a function to solve the problems presented above. Furthermore, the algorithm is independent of control allocation and can be obtained online. Moreover, based on the above attainable moment sets algorithm, a flight performance assurance system is designed, which not only guarantees that the command is constrained within the envelope so that its behavior is more predictable, but also supports adaptive compensation for the pseudo-command restricted controller. Finally, the effectiveness of the AMS algorithm and the advantages of the pseudo-command restricted control system are validated through two sets of independent simulations.

Theoretical and experimental study on the dynamic behavior of spur gear transmission system during hovering maneuver flights

Gear transmission systems, a crucial component of aircraft power transmission, are subjected to time-varying additional inertial forces and gyroscopic moments in noninertial frame environments when the aircraft performs maneuvering flight actions. In this study, a noninertial frame dynamic model of the gear transmission system during hovering maneuver flights was established, and the accuracy of the theoretical model was verified through conducting hovering maneuver flight simulation experiments. The dynamic behavior of the gear transmission system housing, bearings, and shafts under different hovering maneuver flight parameters was studied. The findings reveal that the maneuvering acceleration of the carrier causes varying degrees of change in the vibration acceleration of the housing, the center distance of the gear pair, the bearing force, and the bending deformation of the shafts, among which the magnitude and direction of the radial bearing force and shaft bending deformation are significantly affected by the maneuvering acceleration. Moreover, the risk of failure can be mitigated through the adjustment of the inclination angle to minimize the bending deformation of the fragile shaft resulting from maneuvering acceleration.

Of bats and robots

Some bat species feature joint adaptations to their biosonar sensing and flight systems that allow them to navigate and hunt in dense vegetation. Understanding how biosonar sensing and flight are connected in these species poses a challenge: Kinematics recordings of bat-flight can document the system outputs, but due to various limitations, especially on processing, analysis of bat flight has been limited to minimal numbers of flights and hence has had difficulty to capture the natural variability in flight maneuvers. On the input side of the bats' flight-control system, it is necessary to understand the stimulus ensemble that guides flight in dense vegetation. Navigation in these cases must be based on clutter echoes, i.e., signals consisting of many unresolved components. Since the biosonar inputs into a bat are difficult to record without heavy interference with the animals' behaviors. Hence, biomimetic sonar robots can be used to collect stimuli from natural environments and their recreations. With their superior abilities to find patterns, deep-learning offer an opportunity to cut through the complexity of the input and output data of bat flight control and elucidate how clutter echoes are interfaced with the high-dimensional flight kinematics of bats to create the animals' exceptional maneuvering capabilities.

An experimental array setup to study the integration of biosonar and maneuvering flight in bats

Understanding biosonar-based flight control in bats remains a challenge for fundamental bioacoustics and holds promise for engineered flight-control systems, especially for highly maneuverable drones. To accomplish this, detailed data on bat flight is required that captures the variability across flight situations, individuals, and species. Hence, a flight tunnel has been constructed that provides enough space for natural flight maneuvers. Reconstructing the detailed geometry of freely maneuvering bats requires capturing a flying bat from many different angles to ensure freedom from occlusions. Ideally, this would also be done without placing artificial markers on the bat. Hence, a flight tunnel has been instrumented with arrays of 50 high-speed video cameras and 28 ultrasonic microphones that were all integrated within a modular canvas setup. The optical properties of each camera have been determined from multiple images taken of a panel with a calibration grid seen from different orientations. The spatial relationship between the cameras was estimated based on a large set of images containing random points created with an LED. Reconstructing the 3D geometries of the bats from large numbers of high-speed video frames requires an automated method due to the problem's complexity. To this end, deep-learning methods are currently under development.

The engine mission simulation system: coupled simulation of aircraft and engine in the mission

AbstractWhen designing future aircraft, the entire system including airframe, propulsion system, energy management and mission control must be considered in a coupled approach at an early design stage. To model and study this complex interaction, the Engine Mission Simulation System (EMSS) environment was created in cooperation with the Bundeswehr Technical Center for Aircraft and Aircraft Equipment. EMSS is intended to serve as a digital, flying test bed at the Institute of Jet Propulsion for the rapid and low-cost evaluation of new concepts for propulsion and power supply systems for new and legacy aircraft designs. This tool chain allows a detailed engine simulation under consideration of its flight condition while using an external propulsion and power control system. First, the software architecture of EMSS is discussed, which involves a propulsion simulation in NPSS® coupled with the X-Plane 11 flight simulator via MATLAB®/Simulink®. The aircraft itself is modeled as a six degrees of freedom simulation using the blade element theory, allowing both complex flight maneuvers and unusual aircraft to be easily simulated. The EMSS achieves real-time capability and enables user-friendly pre-design as well as quick trend detection through the ability to perform focused parametric studies. To evaluate the accuracy of the system’s results, the engine model used is validated against experimental data. Subsequently, the aerodynamic characteristics of the aircraft model are verified with a CFD simulation. Finally, a parametric study is used to demonstrate the mission as well as propulsion evaluation capabilities of the Engine Mission Simulation System.

A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning

Unmanned aerial vehicles (UAVs) require pre-planned flight paths that are energy-efficient, safe and smooth across their wide range of application scenarios. In this study, a novel UAV path planning method is proposed. Firstly, the UAV path planning under numerous obstacles is modeled as a continuous constrained optimization problem. The cost function is formulated as a linear combination of length, height variation, and smoothness, while the constraints include obstacle avoidance, height limitation, and the maneuverability of UAV. Subsequently, a novel constrained state transition algorithm with adaptive fuzzy penalty (AFSTA) is proposed to solve the optimization problem. In AFSTA, a novel adaptive fuzzy penalty function is designed to leverage expert knowledge to establish a reasonable mapping relationship from the fitness value and the degree of constraint violation to the penalty factor for a candidate solution. Meanwhile, the state transition algorithm (STA) is used as the search engine for both global and local search. Experimental results illustrate that the proposed method can find energy-efficient, safe, and maneuverable flight paths successfully with the superiority over other state-of-the-art metaheuristic algorithms.

Enhanced dispersal capacity in edge population individuals of a rapidly expanding butterfly.

Natural range shifts offer the opportunity to study the phenotypic and genetic changes contributing to colonization success. The recent range shift of the Southern small white butterfly (Pieris mannii) from the South to the North of Europe offers a prime example to examine a potential dispersal syndrome in range-expanding individuals. We compared butterflies from the core and edge populations using a multimodal approach addressing behavioral, physiological, and morphological traits related to dispersal capacity. Relative to individuals from the core range (France), individuals from the edge (Germany) showed a higher capacity and motivation to fly, and a higher flight metabolic rate. They were also smaller, which may enhance their flight maneuverability and help them cope with limited resource availability, thereby increasing their settlement success in novel environments. Altogether, the behavioral, physiological, and morphological differences observed between core and edge populations in P. mannii suggest the existence of a dispersal syndrome in range-expanding individuals. Whether these differences result from genetic and/or phenotypic responses remains, however, to be determined.

Aviation Safety Risk Analysis Based on Bayesian Network Modeling

The occurrence of serious flight accidents not only brings huge economic losses to airlines but also poses a great threat to passengers' lives. The occurrence of serious flight accidents will not only bring great economic losses to airlines but also cause great threats to the lives of passengers. Therefore, in this paper, based on the flight records of different flight crews, flight routes, airports, and specific flight conditions, aircraft safety risk analysis is conducted to calculate and evaluate the risk propensity. Firstly, the data are preprocessed, and then the QAR data are clustered to extract the key data items affecting the safe flight of the aircraft; then the data are transformed to the attached data, and a hierarchical clustering model is established based on the K-means clustering algorithm, which classifies the factors affecting the safety quality of the aircraft flight into the three main aspects of environmental factors, crew maneuvering, and aircraft state, and the key parameters are weighted by the Random Forest algorithm. weights of the key parameters. Then, based on the 3 criterion, the outliers exceeding 3 were selected as the dependent variables leading to the occurrence of deviation; based on the three-layer model, a Bayesian neural network model based on the three-layer model, and then determine the Bayesian nodes; finally, calculate the Bayesian node probability, and then provide a reasonable quantitative description of the flight maneuver.

Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow

Explorations involving long-endurance and maneuvering flights in the upper atmosphere, as well as research on atmospheric entries of space debris or asteroids, call for a full understanding of hypersonic rarefied flows. The inverse Magnus effect occurs in the hypersonic rarefied flow past a rotating sphere, but the aerodynamic behavior is contrary to the Magnus effect in the continuum flow regime. In this article, a series of such flows are numerically studied using the direct simulation Monte Carlo (DSMC) method. By analyzing the flow fields, as well as the distributions of pressure and shear stress on the sphere, the formation of the inverse Magnus force can be attributed to the tangential momentum transfer between incident gas molecules and the windward surface. The variation laws of aerodynamic parameters with the rotation rate are presented and discussed.

Air and Structural Loads Analysis of a 5-Ton Class Rotorcraft in a Pull-Up Maneuver Using CFD/CSD Coupled Approach

The air and structural loads of a 5-ton class light helicopter (LH) rotor in a 2.24 g pull-up maneuver are investigated using a coupling between the computational structural dynamics (CSD) and computational fluid dynamics (CFD) methods. The LH rotor is characterized by a five-bladed system with elastomeric bearings and inter-bladed dampers. The periodic trim solution along with the converged CFD/CSD delta airloads obtained in steady-level flight (advance ratio of 0.287) are used to perform the transient CSD maneuver analysis. The resulting vehicle attitude angles and velocity profiles of the aircraft are then prescribed in the quasi-static (QS) CFD maneuver analysis. It is demonstrated that the present QS approach provides an effective means for the maneuver loads’ analysis. The important flow behaviors such as BVI (blade–vortex interaction)-induced oscillations and the negative pitching moment peaks met in maneuver flight are captured nicely with the proposed method. Either the vortex trajectories or the surface pressure distributions are examined to identify the sources of the oscillations. A loose CFD/CSD coupling (LC) is used to predict the blade elastic motions, structural moments, and pitch link loads at the specified maneuver revolution of the rotor and also to correlate these with the transient CSD-based predictions. A reasonable correlation is obtained. The LC results show more pronounced 5P (five per revolution) oscillations on the structural response than those of the CSD-based methods.

Drone noise differs by flight maneuver and model: implications for animal surveys

Drones are becoming a common tool for animal monitoring; however, sound emitted from drones may disturb animals and bias survey results. Understanding noise levels produced by different flight maneuvers, altitudes (i.e., above ground level (AGL)), and drone models could mitigate animal disturbance during surveys. We measured maximum sound (dB) emitted during three flight maneuvers (hovering, flyover, and turning) among eight AGLs (15–120 m) and two vertical maneuvers (ascending and descending) for four commercially available quadcopter drone models (DJI Matrice 300, Matrice 200, Phantom 3, and Autel Evo II), accounting for wind speed and comparing to ambient (background) noise. Ascending, descending, and hovering produced more noise compared to flyover and turning maneuvers. One large drone (Matrice 200, 4.7 kg) produced more noise than the two smaller drones (Evo II, 1.2 kg and Phantom 3, 1.1 kg). However, the largest drone (Matrice 300, 6.4 kg) produced noise similar to smaller models and was the quietest among all models from 75 to 120 m AGL, providing potential size advantages with less noise disturbance. Our results indicate that flights consisting of flyover and turning maneuvers likely cause less noise disturbance than surveys with prolonged hovering over animals.

Comparison of Machine Learning and Classic Methods on Aerodynamic Modeling and Control Law Design for a Pitching Airfoil

Aerodynamic modeling and control law design methods are crucial foundational technologies that enable efficient maneuvering flight of aircraft. Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. To conduct this, a modified NACA0012 airfoil with a tail functioning as an elevator is employed as the geometry model. Through the utilization of the rigid dynamic grid method and overlapping grid technology, the pitch of the airfoil and the deflection of the elevator are efficient to execute. Firstly, the pitching moment coefficient is sampled through both steady and unsteady computations. Then, multivariate nonlinear regression (MNR) models and artificial neural network (ANN) models are established based on steady and unsteady sampling data, respectively. Additionally, the obtained model is evaluated using open‐loop control laws. Based on the evaluation, the proportional‐integral‐derivative (PID) control algorithm is used to design the airfoil pitching control law using the MNR model. Meanwhile, deep reinforcement learning (DRL) is used to design the pitching control law using the ANN model. Finally, the PID and DRL controllers are implemented in a CFD environment for airfoil pitching control to verify their effectiveness in application scenarios. The results suggest that models based on both steady and unsteady data can reflect dynamic aerodynamic characteristics. However, using unsteady computation for data sampling significantly reduces time consumption compared to steady computation. Furthermore, the model constructed by ANN may have unexpected excellent characteristics. Both the PID and DRL control laws, designed based on the models, perfectly complete the control process in the CFD environment. This study provides valuable insights for the implementation of controllable maneuvering flight in aircraft.

Numerical analysis of insect flight maneuverability using feedback control model

Numerical analysis of insect flight maneuverability using feedback control model