Unsteady Aerodynamic Model Research Articles

Unsteady aerodynamic modeling and flight trajectory simulation of dual-spin projectile based on DNN and transfer-learning

To evaluate flight performance and aerodynamic characteristics of a dual-spin projectile, the coupled computational fluid dynamics and rigid body dynamics (CFD/RBD) method is commonly used, which can simultaneously solve the flight mechanics and flow field. However, the efficiency is compromised by the large number of CFD calculations required. This paper develops an unsteady aerodynamic modeling method that combines deep neural networks and transfer learning, which can accurately predict unsteady aerodynamics of dual-spin projectiles under varying initial conditions. Considering the influence of flight state and aerodynamic data from short-term historical moments, we integrate them as input features of the aerodynamic model to reduce the impact of long-term historical data. To enhance the model generalization under varying initial conditions, we fine-tune the built aerodynamic model using small amounts of data under new conditions by transfer learning. The proposed method is validated through interpolated and extrapolated prediction cases, respectively. The results indicate that the proposed method can achieve better accuracy and generalizability than long short-term memory neural networks and autoregressive moving average method in unsteady aerodynamic modeling of the dual-spin projectile. By coupling the flight dynamics equations with the aerodynamic model in the time domain, the flight simulation only takes a few seconds, which can reduce computing time by three orders of magnitude compared to the coupled CFD/RBD method.

Development on Unsteady Aerodynamic Modeling Technology at High Angles of Attack

Development on Unsteady Aerodynamic Modeling Technology at High Angles of Attack

Nonlinear unsteady aerodynamic forces prediction and aeroelastic analysis of wind-induced bridge response at multiple wind speeds: A deep learning-based reduced-order model

Machine learning-based aerodynamic reduced-order models (ROMs) combine high accuracy with extremely low computational costs, making them highly effective in predicting nonlinear and unsteady bridge aerodynamic forces. Although several machine learning-based nonlinear aerodynamic models have been developed, the majority are built on a single wind speed parameter. However, in nonlinear aerodynamic prediction and aeroelastic analysis of bridges, the variability in incoming wind speed significantly influences the computed results. A ROM relying solely on a single wind speed lacks the ability to accurately forecast the intricate dynamic behaviors arising from changes in wind speed. When the incoming wind speed changes, the model's prediction accuracy significantly decreases. Usually, it is necessary to establish a new database and train a new model, which not only increases time and cost but also greatly reduces the convenience of the ROM. Addressing this challenge, this study proposes a multiple-wind-speed (MWS) nonlinear unsteady bridge aerodynamic model based on the LSTM deep neural network. Taking the Taohuayu Yellow River Bridge in the Henan Province of China as an example, the modeling process of the proposed MWS-ROM is demonstrated, along with non-linear aerodynamic predictions of the deck under various conditions and aerodynamic-elastic analysis of the deck under different wind speeds. The research results show that the trained LSTM network can accurately predict the nonlinear aerodynamic forces of bridges under single and double degrees of freedom vibration conditions. The MWS-ROM performed well in predicting convergent vibrations at low wind speeds and limits cycle oscillations at high wind speeds, aligning closely with results from the CFD full-order model. Compared to CFD, the aerodynamic ROM based on the LSTM network significantly enhances computational efficiency, consequently boosting the convenience and efficiency of bridge flutter analysis. Additionally, the methodology proposed herein can be extended for wind-induced vibration control and response prediction in other types of deck sections.

Three-dimensional configuration tip rotor dynamics analysis

Abstract In order to analyze and study the dynamic characteristics of the 3D blade tip rotor, an aeroelastic dynamic model for a complex 3D blade tip rotor is established based on mechanics principles. The theory of medium deformation beam or large deformation beam is adopted as the blade structural dynamic model, and the quasi-steady and unsteady aerodynamic models (ONERA model) are adopted as the aerodynamic model. The rotor dynamics, aeroelastic stability and rotor loads of three-dimensional blade tip and rectangular blade tip are compared by examples. The analysis shows that both the forward and backward sweep angles have a certain effect on the rotor blade frequency, but the downward dihedral angle has little effect. The 3D tip configuration has the greatest influence on torsion mode frequency, followed by lagging mode frequency, which has little impact on flapping frequency. Reasonable design of forward and backward sweep angles and downward dihedral angles of 3D tip configuration can effectively reduce rotor loads, increase damping margin for aeroelastic stability, and improve stability.

Modelling and analysis of two-dimensional static and dynamic aeroelasticity of Fish Bone Active Camber morphing aerofoils

As a continuous and smooth morphing concept for aerofoils, the Fish Bone Active Camber (FishBAC) concept has demonstrated significant aerodynamic efficiency improvements over traditional hinged flaps. In this paper, to investigate the static and dynamic aeroelasticity of FishBAC aerofoils, an unsteady two dimensional coupled fluid-structure interaction model is developed, which includes the structural response of the FishBAC spine, skin, stringers, tendons and actuator, coupled to an unsteady aerodynamics model. The structural dynamic model is Timoshenko beam-theory-based, while the aerodynamic model is based on Peters’ unsteady model. The static and dynamic aeroelasticity is studied after the model is validated. Results show that the increase in pulley rotational angle reduces the zero-lift angle of attack, while keeping the slope between the lift coefficient and angle of attack the same. A shorter morphing region closer to the trailing edge is beneficial for generating larger lift coefficient with the same tendon moment and angle of attack. Flutter occurs with the increase of the air speed. When the morphing end position is fixed at 0.9 chord, increasing the morphing length reduces the critical flutter speed significantly, with the second bending mode tending to drive instability. With the same morphing length, moving the morphing region closer to the leading edge increases the critical flutter speed, and the unstable mode changes from the second mode into the first mode.

Time-Varying Aeroelastic Modeling and Analysis for a Morphing Wing

The paper presents a novel time-varying aeroelastic modeling approach for the morphing wing with the process of camber changing. The time-varying modeling methodology includes structural dynamics modeling, unsteady aerodynamic modeling, and interpolation for fluid–structure coupling. Firstly, the morphing wing is modularized and divided into fixed and variable parts by the floating frame of reference formulation. It is combined with the Craig–Bampton method to reduce order and eliminate nonindependent degrees of freedom. Then, the unsteady vortex-lattice method is used to calculate the transient, unsteady aerodynamic force for time evolution. Furthermore, the above time-varying structural dynamics modeling and a fluid–structure coupling interpolation are integrated to construct overall aeroelastic modeling, obtaining a set of differential-algebraic equations. Finally, these equations are numerically solved by the generalized-α method. The proposed approach provides an innovative idea to deal with the incredible complexity of the aeroelastic effect as structural characteristics change over time. It can efficiently and accurately analyze the morphing wing’s transient response or flutter property. To verify and utilize the time-varying aeroelastic modeling approach, numerical calculations and comparative studies were carried out regarding the time-varying characteristics of the structural modes, aerodynamic calculations, and flutter prediction of the morphing wing.

Dynamic Response Analysis of Moving Trains Passing Through the Stationary Thunderstorm Downburst Wind

The safety of the high-speed train traveling through the stationary thunderstorm downburst wind was studied. First, a thunderstorm wind test device was used to simulate the stationary thunderstorm downburst wind. Based on the rigid model pressure measurement tests, the aerodynamic forces of the train traveling along different paths through the stationary thunderstorm downburst wind were measured. The influence of the radial distance of the crossing path on the aerodynamic force coefficients of the train was investigated. On this basis, an unsteady aerodynamic model of the high-speed train crossing through the stationary thunderstorm downburst wind was established, and dynamic response analysis was carried out using the SIMPACK multibody dynamics simulation software to explore further the safety of the high-speed train crossing through the stationary thunderstorm downburst wind. The research showed that the stationary thunderstorm downburst wind field has significant spatial variation characteristics compared with the atmospheric boundary layer wind field. When the train passes through the thunderstorm downburst wind, the radial wind speed and wind yaw angle experienced by the train constantly change, and the change curve shows a symmetrical distribution. The aerodynamic force of the train will undergo sudden loading and unloading processes, and the lateral force coefficient of the train on different paths shows a “pulse-type” variation. Moreover, the lateral force coefficient increases with the increase of wind yaw angle. Under the influence of the thunderstorm downburst wind, the variation trend of the aerodynamic force coefficients of the train is consistent with that under crosswind. However, there are significant differences in the numerical values. Therefore, it is impossible to simply use the formula for calculating the aerodynamic force coefficients of the train under crosswinds to predict the aerodynamic force coefficients of the train under the thunderstorm downburst wind. While passing through the thunderstorm downburst wind, the overturning coefficient index plays a decisive role in the safety of train operations. Train rollover is the main form of train safety accidents, while derailment accidents are not easy. The numerical results obtained in this study are significant for evaluating the operational safety while moving trains traversing the stationary thunderstorm downburst wind.

A deep neural network reduced order model for unsteady aerodynamics of pitching airfoils

A machine learning framework is developed to compute the aerodynamic forces and moment coefficients for a pitching NACA0012 airfoil incurring in light and deep dynamic stall. Four deep neural network frameworks of increasing complexity are investigated: two multilayer perceptrons and two convolutional neural networks. The convolutional framework, in addition to the standard mean squared error loss, features an improved loss function to compute the airfoil loads. In total, five models are investigated of increasingly complexity. The convolutional model, coupled with the loss function based on force and moment coefficients and embedding the attention mechanism, is found to robustly and efficiently predict pressure and skin friction distributions over the airfoil over the entire pitching cycle. Periodic conditions are implemented to grant the physical smoothness of the model output both in space and time. An analysis of the training dataset point distributions is performed to point out the effects of adopting low discrepancy sequences, such as Latin hypercube, Sobol', and Halton, compared to random and uniform sequences. The current model shows improved performances in predicting forces and pitching moment in a broad range of operating conditions.

Unsteady aerodynamic modeling of whirl flutter on a bending wing

Unsteady aerodynamic modeling of whirl flutter on a bending wing

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

Unsteady aerodynamic prediction at high angles of attack is of great importance to the design and development of advanced fighters. In this paper, a weighted feature fusion model (WFFM) that combines the state-space model and neural networks is proposed to build an unsteady aerodynamic model for the precise simulation and control of post-stall maneuvers. In the proposed model, the influences of the physical model on neural networks are considered and adjusted by introducing a standardization layer and a new weighting method. A long short-term memory (LSTM) network is used to fuse two mappings: one from flight states to aerodynamic loads, and the other from low-fidelity data to high-fidelity data. Data from wind tunnel oscillation experiments at high angles of attack using a new kind of wire-driven parallel robot and the traditional tail support are used for verifying the proposed aerodynamic model. The output of the WFFM is also compared with predictions from other models, such as the state-space model, single LSTM model, and feature fusion model not including a feature weighting layer. Results demonstrate improved accuracy of the proposed model in the interpolation and extrapolation tests. Furthermore, the WFFM is applied to the flight simulation of F-16 with different control inputs. Compared with conventional models, the WFFM shows improved accuracy and better generalization capability.

Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

The development of simple, low-order and accurate unsteady aerodynamic models represents a crucial challenge for the design optimisation and control of fluid dynamical systems. In this work, wind tunnel experiments of a pitching NACA 0018 aerofoil conducted at a Reynolds number $Re = 2.8 \times 10^5$ and at different free-stream turbulence intensities are used to identify data-driven nonlinear state-space models relating the time-varying angle of attack of the aerofoil to the lift coefficient. The proposed state-space neural network (SS-NN) modelling technique explores an innovative methodology, which brings the flexibility of artificial neural networks into a classical state-space representation and offers new insights into the construction of reduced-order unsteady aerodynamic models. The work demonstrates that this technique provides accurate predictions of the nonlinear unsteady aerodynamic loads of a pitching aerofoil for a wide variety of angle-of-attack ranges and frequencies of oscillation. Results are compared with a modified version of the Goman–Khrabrov dynamic stall model. It is shown that the SS-NN methodology outperforms the classical semi-empirical dynamic stall models in terms of accuracy, while retaining a fast evaluation time. Additionally, the proposed models are robust to noisy measurements and do not require any pre-processing of the data, thus involving only a limited user interaction. Overall, these features make the SS-NN technique an excellent candidate for the construction of accurate data-driven models from experimental fluid dynamics data, and pave the way for their adoption in applications entailing design optimisation and real-time control of systems involving lift.

Mechanism of single-mode panel flutter in low supersonic flow

Supersonic panel flutter can be of two possible types: single-mode flutter and coupled-mode flutter. Compared to coupled-mode flutter, the physical mechanism of single-mode flutter is less studied at present. In order to reveal the inducing mechanism of single-mode flutter, the present paper constructed a reduced-order fluid model by using the system identification and the Auto Regressive with eXogenous input (ARX) model. Coupling the reduced-order model (ROM) for unsteady aerodynamics in low supersonic flow with the structural equation, a highly efficient ROM-based aeroelastic model in state space is formulated, then the flutter boundaries are obtained and the stability of aeroelastic modes are investigated by the complex eigenvalue analysis. According to the physical meaning of relevant parameters in unsteady aerodynamic reduced order model ARX, it is found that the unsteady characteristic of the flow, specifically the history effects of the unsteady aerodynamic forces, plays a dominant role in causing the single-mode panel flutter at low supersonic speeds. It is also shown that the higher modes are indeed weakly unstable in the absence of any structural damping or a viscous boundary layer.

A case study on neuro-fuzzy architectures applied to the system identification of a reduced scale fighter aircraft

This study investigates different architectures of Neuro-Fuzzy applied to unsteady aerodynamic modeling based on experimental data from a reduced-scale aircraft, known as Generic Future Fighter. The comparison is made considering different fuzzy inference methods, membership function shapes, number of membership functions to describe the input variables and different output functions, in the case of Takagi-Sugeno inference method. All these comparisons are made using the differential evolution as an optimization tool. In the end, the results present the best Neuro-Fuzzy configuration applied to the system identification of the GFF. Furthermore, the conclusion presents insights about the possible future implementation of the methodology.

The effect of variable rotary motion of a flapping wing rotor on its aerodynamic performance based on an improved unsteady vortex ring method

In the previous aerodynamic analysis using a potential flow-based unsteady aerodynamic model for the flapping wing rotor (FWR), the average rotary velocity and flapping frequency together with the flapping and twist angles are usually taken as the FWR kinematics of motion. In the present study, an unsteady vortex ring method (UVRM) is developed to take the rotary velocity variation of the FWR during the flapping motion into account based on 3D unsteady potential flow theory. The UVRM is validated by comparing with the published results of a flapping wing and an FWR model. An FWR test model and the corresponding experimental platforms including a wing motion tracking system and a force measurement system are built to measure the FWR motion and associated forces. Three FWR test cases of different input voltages and kinematics of flapping motion are considered to measure the aerodynamic forces and compare with the UVRM results. The results show that the differences between the measured and pre-set flapping angles (-50°∼ 20°) for the FWR are negligible for all the cases, but the differences in the variation amplitudes of the measured twist angle and the corresponding pre-set rigid twist angles can be as much as about 15°. Also, the FWR rotary velocity varies dramatically during a flapping cycle. For one of the cases with the -10°∼30° pre-set twist angle and 4 V input voltage, the difference between the maximum and minimum rotary speed is about 1451°/s, which makes a significant effect on the FWR aerodynamic performance. The results also show that the calculated lift forces using the UVRM and the measured real-time rotary speeds during a flapping motion are very close to the experimental results. While the calculated results by taking the FWR's average rotary speeds show apparent differences (up to about 25%) from the measured lift forces in most of the cases. Overall, the UVRM provides an efficient method for the FWR aerodynamic analysis with higher accuracy by taking the effect of variable rotary motion and twist angle of the FWR during a flapping cycle into account.

Discovering optimal flapping wing kinematics using active deep learning

This paper focuses on the discovery of optimal flapping wing kinematics using a deep learning surrogate model for unsteady aerodynamics and multi-objective optimisation. First, a surrogate model of the unsteady forces experienced by a 3-D flapping wing is built, based on deep neural networks. The model is trained on a dataset of randomly generated kinematics simulated using direct numerical simulation (DNS). Once trained, the neural networks can quickly predict the unsteady lift and torques experienced by the wing, using sparse information on the kinematics. This fast surrogate model allows multi-objective optimisation to be performed. The resulting Pareto front consists of new kinematics that may be very different from the kinematics of the initial dataset. A few arbitrarily chosen kinematics on the Pareto front are thus simulated using DNS and used to enhance the database. The new dataset is used to train again the networks, and this active deep learning/optimisation framework is performed until convergence, obtained after only two iterations. Overall, this method reduced the cost of optimisation by 83 %. Results reveal two distinct families of motions. Kinematics promoting high efficiency are characterised by large stroke amplitudes and relatively low angles of attack, as observed for fruit flies, honeybees or hawkmoths. For those, lift production is driven by quasi-steady effects and the formation of a stable leading edge vortex. Kinematics promoting high lift are characterised by small stroke amplitudes and high angles of attack, reminiscent of mosquitoes. Lift production is driven by the rapid generation of vorticity at the trailing edge.

Technical modeling challenges for large idling wind turbines

This paper presents comprehensive investigations into idling instability occurring on the IEA 15 MW reference turbine. The systematic studies are carried out by means of Blade Element Momentum (BEM) and free wake vortex (Vortexline) methods. Two state-of-the-art dynamic stall models are tested in the present investigations, namely the Beddoes-Leishman and the IAG dynamic stall models, implemented into a development version of the wind turbine design code Bladed. The studies highlight the importance of unsteady aerodynamic modeling to predict idling instabilities and emphasize the characteristics of each modeling strategy. It is demonstrated that the IAG dynamic stall model predicts a more physically reasonable idling instabilities. Furthermore, Vortexline enables the calculations of the induced velocities even under idling conditions in contrast to BEM. The combination of the Vortexline method and the IAG model is considered to provide the most reasonable turbine response. The studies will be helpful for load engineers to select appropriate modeling strategies and shed some light into future engineering modeling improvements of wind turbines.

On the Aeroelasticity of a Cantilever Wing Equipped with the Spanwise Morphing Trailing Edge Concept

This paper studies the aeroelastic behavior of a rectangular, cantilever wing equipped with the spanwise morphing trailing edge (SMTE) concept. The SMTE consists of multiple trailing edge flaps that allow controlling the spanwise camber distribution of a wing. The flaps are attached at the wing’s trailing edge using torsional springs. The Rayleigh–Ritz method is used to develop the equations of motion of the wing-flap system. The use of shape functions allows for representing the wing as an equivalent 2D airfoil with generalized coordinates that are defined at the wingtip. Strip theory, based on Theodorsen’s unsteady aerodynamic model, is used to compute the aerodynamic loads acting on the wing. A representative Padé approximation for Theodorsen’s function is utilized to model the aerodynamic behaviors in a state-space form allowing time-domain simulation and analysis. The model is validated using a rectangular cantilever wing and the data are available in the literature. A comprehensive parametric comparison study is conducted to assess the impact of flap stiffness on the aeroelastic boundary. In addition, the potential of the SMTE to provide load alleviation and flutter suppression is assessed for a wide range of flight conditions, using a discrete (1-cosine) gust. Finally, the implementation and validation of a controller for a wing with SMTE for gust load alleviation are studied and controller parameters are tuned for a specific gust model.

Severity assessment of sudden plunging motion for jet transport aircraft in response to severe atmospheric turbulence

PurposeThe purpose of this paper is to present a comparative study of flight circumstances, dynamic stability characteristics and controllability for two transport aircraft in severe atmospheric turbulence at transonic cruise flight for the purpose to obtain the prevention concepts of injuries to passengers and crew members for pilot training in International Air Transport Association (IATA) – Loss of Control In-flight (LOC-I) program.Design/methodology/approachA twin-jet and a four-jet transport aircraft encountering severe atmospheric turbulence are the study cases for this paper. The nonlinear unsteady aerodynamic models are established through flight data mining and the fuzzy-logic modeling technique based on the flight data of flight data recorder. This method can be adopted to examine the influence of horizontal wind shear and crosswind on loss of control, dynamic stability characteristics and controllability for transport aircraft in different weights and different sizes in tracking aviation safety of existing different types of aircraft.FindingsThe horizontal wind shear or crosswind before the turbulence encounter will easily induce rolling motion and then initiate the sudden plunging motion during the turbulence encounter. The roll rate will increase the oscillatory rolling motion during plunging motion, if the rolling damping is insufficient. The drop-off altitude will be enlarged by the oscillatory rolling motion during the sudden plunging motion.Research limitations/implicationsA lack of the measurement data of vertical wind speed sensor on board to verify the estimated values of damping term is one of the research limitations for this study. The fact or condition of being severe in sudden plunging motion can be judged through the analysis of oscillatory derivatives with both dynamic stability and damping terms.Practical implicationsThe roll rate will increase the oscillatory rolling motion during plunging motion, if the rolling damping is insufficient. The drop-off altitude will be enlarged by the oscillatory rolling motion during the sudden plunging motion. The horizontal wind shear or crosswind before the turbulence encounter will easily induce rolling motion and then initiated the sudden plunging motion during the turbulence encounter. If the drift angle is large, to turn off the autopilot of yaw control first and stabilize the rudder by the pedal. When passing through the atmosphere turbulence area, the pilots do not need to amend the heading angle urgently.Social implicationsThe flight safety prevention in avoidance of injuries for passengers and cabin crews is essential for the airlines. The horizontal wind shear or crosswind before the turbulence encounter will easily induce rolling motion and then initiated the sudden plunging motion during the turbulence encounter.Originality/valueThe flight safety prevention in avoidance of injuries for passengers and cabin crews is essential. The present assessment method is an innovation to examine the loss of control problems of aviation safety and promote the understanding of aerodynamic responses of the jet transport aircraft. It is expected to provide a valuable lecture for the international training courses for IATA – LOC-I program after this paper is being published.

Fuzzy Neural Network PID Control Used in Individual Blade Control

In order to further reduce the vibration level of helicopters, the active vibration control technology of helicopters has been extensively studied. Among them, individual blade control (IBC) independently applies high-order harmonics to each blade with an actuator, which can improve the aerodynamic environment of the blade and effectively reduce the vibration load of the hub. The rotor structural dynamics model based on the Hamilton energy variation principle and the medium deformation beam theory were established firstly, and the aerodynamic model based on the dynamic inflow model and the Leishman–Beddoes unsteady aerodynamic model were also established. The structural finite element method and the direct numerical integration method were used to calculate the vibration response of the rotor to determine the vibration load of the hub. After these, the steepest descent-golden section combinatorial optimization algorithm was used to find the optimization parameters of IBC. Based on this, the input parameters of fuzzy neural network PID control were determined, and the rotor hub vibration load control simulation was conducted. Under the effect of IBC, the vibration loads of the hub could be reduced by about 60%. The article gives the best control laws of individual harmonic pitch control and their combinations. These results can theoretically be applied to the design of control law to reduce helicopter vibration loads.

An Experimental and Numerical Evaluation of the Aerodynamic Performance of a UAV Propeller Considering Pitch Motion

Considering the vibration generated by a propeller-driven UAV or encountering gust, the propeller will perform a very complex follower motion. A pitch and rotating coupled motion is proposed in the present work that can take more complex unsteady performance of follower force than a regular fixed-point rotating motion. In order to evaluate the unsteady follower force and conduct parametric study, an extensive ground test bench was designed for this purpose where the whole test system was driven by a linear servo actuator and the follower force was measured by a 6-component balance. For CFD simulation, coupled motion in particular needs detailed unsteady aerodynamic model; therefore, a high-fidelity CFD-based study integrated with the overset mesh method was complemented to solve the unsteady fluid of varying conditions. The results suggest that a significant influence on unsteady follower force is observed, and the mean value of in-plane force does not equal to zero during the coupled motion process. Compared with the regular fixed-point rotation of propeller, the fluctuation frequency of follower force in present work couples the rotation and pitch motion frequencies. In addition, the oscillation amplitude of out-plane force and torque is positively related with the pitch frequency, pitch amplitude, and relative length from leading edge of wing to the rotation center. For example, the oscillation amplitude of 1-blade’s out-plane force and torque increases by 57.122% and 66.542% for the 5 Hz-5 deg case compared with the 5 Hz-3 deg case, respectively. However, the torque is not sensitive to frequency of pitch motion. The generally excellent agreement evident between the ground test and numerical simulation results is important as guidance for our future investigation on “dynamic” aerodynamic performance of a propeller-driven UAV.