Aerodynamic Uncertainties Research Articles

An Efficient Uncertainty Quantification Method Based on Inter-Blade Decoupling for Compressors

Abstract A compressor usually contains multiple blade rows, and its uncertainty dimensionality grows proportionally with the number of blade rows, leading to a rapid increase in required sample size for uncertainty quantification analysis. This paper proposes a new dimensionality reduction method based on inter-blade decoupling, by analyzing uncertainty propagation in compressors. Traditionally, a surrogate model with uncertainty variables of all blade rows as input is directly established and the dimensionality is high. To solve this problem, this study decomposes the compressor domain into sub-domains, each containing one blade row. For each sub-domain, a sub-model introduces aerodynamic uncertainties at the interfaces connecting different sub-domains. The dimensionality of a sub-model is roughly equal to the uncertainty factors in a single row, significantly reducing the required sample size. The uncertainties in the rotor and stator blade rows of a one-stage compressor are investigated to verify this method. Using principal component analysis and machine learning, the projection amplitudes of the interface aerodynamic flow field onto the principal modes are extracted, and sub-models are established. Results show that the original 25-dimensional model can be decoupled into a 13-dimensional sub-model for the rotor and a 16-dimensional sub-model for the stator, reducing the required sample size from 600 to 90 with similar accuracy. This dimensionality reduction method greatly reduces the cost of predicting compressor performance with uncertainty, laying a foundation for comprehensive analysis and effective control of uncertainty factors in engineering applications.

Automatic Aircraft Landing System: A Review

The aircraft landing phase, marking the finishing of a flight plane, is a critical yet dangerous aerial maneuver. Even though it might seem simple, predicting the complexities of performance during landing presents a big challenge due to the dynamic characteristics of the phase, interaction with piloting methods, as well as inherent uncertainties in aerodynamics. Landing is executed in close proximity to the ground at reduced airspeed, the landing phase entails an escalated safety risk. Notably, incidents and accidents, particularly overruns where aircraft fail to slow sufficiently on the runway before landing, underscore the imperative for advanced landing technologies. This paper presents a comprehensive review of research spanning from 2000 to the present exploring smart techniques like fuzzy logic and machine learning, an array of control strategies ranging from classic PID control to sophisticated hybrid control approaches, and optimization procedures utilizing diverse optimization algorithms. The primary objective is to provide a comprehensive evaluation of the existing research landscape, offering insights that can propel the evolution of strategies for safer and more efficient landings and making sure the plane touches down safely.

High-α maneuver under lateral centre of gravity uncertainty: A robust adaptive backstepping control scheme

The present note addresses the novel problem of executing complex aircraft maneuvers under considerable center of gravity (c.g.) uncertainties arising from asymmetrical loading or release of payloads, uneven fuel consumption etc. First, the aircraft flight dynamics under predominantly lateral c.g. movement, is approximated and expressed in a block strict feedback form and thereafter an adaptive backstepping controller is proposed to adapt to the c.g. variations. To alleviate the model uncertainty caused by this model approximation and also to provide robustness to aerodynamic uncertainties in high-alpha regions, a sliding mode control is further integrated with the adaptive backstepping control law. Asymptotic stability conditions of the proposed controller are derived from the first principle using Lyapunov’s method and Barbalat’s lemma. To validate the proposed control scheme, the high-alpha Herbst maneuver is implemented in simulation for the F18-HARV aircraft and the results show that the maneuver performance remains nearly the same under both the nominal and the off-nominal c.g. positions.

Addressing Launch and Deployment Uncertainties in UAVs with ESO-Based Attitude Control

This paper describes the design and implementation of a novel three-axis attitude control autopilot scheme for tube-launched, air-deployed UAVs. In early flight tests, various factors, such as model uncertainties during launch, aerodynamic uncertainties, geometric parameter changes during deployment, and significant uncertainties in booster rocket installation, exceeded the control capabilities of the attitude autopilot, causing flight instability. In order to address these issues, a numerical simulation model of the full launch process considering deviations was established based on early flight tests. A cascade attitude controller was then designed using an extended state observer (ESO), and the boundedness of control errors under unknown bounded disturbances was theoretically proven, providing requirements for the parameter tuning of the cascade controller. Comparative experiments and a second flight test both demonstrate that the ESO-based cascade attitude controller exhibits strong feedforward disturbance compensation under high-uncertainty conditions, effectively achieving stable control within the flight envelope.

Uncertainty propagation of flutter analysis for long-span bridges using probability density evolution method

Dynamic and aerodynamic uncertainties are inevitably involved in the wind-bridge system due to some unknown information or complicated environment conditions. The deterministic aeroelastic flutter study is unable to account for these uncertainties and fail to describe the fragility or risk of flutter instability of the bridge. To achieve the uncertainty propagation of flutter analysis for long-span bridges, the advanced probability density evolution method is introduced. The increase of wind speed is treated as a time-varying parameter incorporated into the generalized density evolution equation. The two-dimensional bimodal and three-dimensional multi-mode methods for bridge flutter analysis are utilized to track the probability density evolution of damping ratio and frequency of each mode. The probability of flutter critical wind speeds can then be readily determined. The probability density evolution process is applied to a simply supported beam bridge with quasi-flat box girder and a suspension bridge with closed-box girder by considering the uncertainties of the structural properties and flutter derivatives. The probability density evolution method is proved to produce a reasonable result which shows a good agreement with Monte Carlo method, but requires fewer sample points, which improves the computational efficiency.

Uncertainty quantification of turbine endwall aero-thermal performance under combustor-turbine interface louver coolant and cavity

The uncertainty characteristics have a significant influence on turbine cooling performance and its reliability, especially for the combustor-turbine interface region which faces more complicated flow interactions. Previous researches under the certainty framework are not able to reflect the realistic cooling characteristics of the interface region under real operation circumstances. Considering the random fluctuations of the major structure and flow parameters, uncertainty quantification of turbine endwall cooling performance under realistic combustor-turbine interface conditions is conducted in this paper using the polynomial chaos expansion method. The mix characteristic of combustor louver coolant and interface cavity leakage in the turbine endwall region is investigated, and the influences of parameter uncertainties on endwall cooling, vane phantom cooling, and cascade aerodynamic characteristics are analyzed. The results indicate that the uncertainties at the combustor-turbine interface largely influence the endwall cooling by modifying the strength and position of the secondary flows. Under the random fluctuations of interface geometry parameters, the local mean value of endwall cooling effectiveness can reach up to 0.25, and the standard deviation of endwall cooling effectiveness can reach 0.1. The geometrical parameters of interface width and misalignment height are the decisive factors for endwall cooling uncertainty. The random fluctuations of interface width and misalignment height separately cause great cooling effectiveness uncertainties on the pressure side endwall and suction side hot ring. The uncertainties in mainstream and structure parameters exhibit a reduced propensity to propagate onto vane phantom cooling. The decisive factors for vane phantom cooling uncertainty are interface width and misalignment height, and the major influenced area is the vane middle part. Four mainstream and structure parameters all make great contributions to the aerodynamic uncertainty at the strong secondary flow region and vane wake flow region. This paper can provide a theoretical basis for the design of combustor-turbine interface cooling structures to accommodate the actual turbine operation uncertainty conditions.

Stochastic robustness analysis of wing-aileron flutter suppression systems

In this work a stochastic robustness analysis approach is used to compare the performances of two non-linear flutter suppression systems designed for stabilization of a wing-aileron lifting structure. A reduced order modelling approach based on an alternative aeroelastic beam finite element is presented and used for the flutter suppression systems design, which are driven by a simple adaptive controller and a sliding mode controller, respectively. A heuristic algorithm is used to determine the simple adaptive controller invariant parameters at the design point while a simple parametric study is performed to tune the sliding mode controller. The performances of each closed loop system are evaluated taking into account mass, stiffnesses, and aerodynamics uncertainties of the aeroelastic plant also considering the presence of discrete gust disturbances.

Fixed‐time observer‐based heading control of a paraglider recovery system with input saturation

AbstractThis paper investigates a challenging heading control problem for a flexible paraglider recovery system with input saturation. A fixed‐time observer‐based control approach is presented. In this approach, a fixed‐time disturbance observer is preliminarily designed to estimate the aerodynamic uncertainties and the complex coupling of the subsystems. A fixed‐time auxiliary system is then proposed to compensate for input saturation impact. With the application of the estimated and compensation values, a novel heading tracking controller is synthesized with compensated disturbance. The key feature of the scheme is that it is not only fixed time stable regardless of any initial state, but also it prevents control performance degradation due to input saturation and various disturbances. Simulation results verify the effectiveness of the proposed method.

Assessment of small satellite aerocapture with a morphable entry system at Mars

A significant challenge for interplanetary small satellites is fully propulsive orbit insertion due to limited total velocity change capabilities. At destinations with significant atmospheres, this challenge can be circumvented via aerocapture, a technique that uses a single atmospheric pass to convert a hyperbolic approach trajectory into a captured elliptical orbit. This paper investigated small satellite aerocapture at Mars with the morphable entry system, a deployable entry vehicle concept that uses shape morphing for trajectory control. A Monte Carlo dispersion analysis was conducted to assess the vehicle’s robustness with respect to uncertainties in entry state, atmospheric density, and vehicle aerodynamics. Under the combined effects of these uncertainties, the morphable entry system achieved a 100% aerocapture success rate. Furthermore, the total velocity change required to place the vehicle on the target orbit for 99% of the simulated trajectories was 166.5 m/s, which is within the current capabilities of small satellite propulsion systems. The results suggest that the morphable entry system is a viable option for smallsat aerocapture at Mars.

Landing control of a returned spacecraft under conditions of uncertainty

The nonlinear model of a spacecraft requires high robustness, and the classical control method, as one of the earliest developed control methods, has simple algorithms and high robustness. A control system structure that ensures retention attack angle and overload by combining fuzzy control with classical control for adaptive optimization and adjustment of control parameters and tracking of the attack angle step signals is proposed. The simulation results show that the performance of the improved system is better than that of the original system. Improved algorithms are used to simulate the attack angle and overload retention sections, also simulated aerodynamic uncertainties is carried out. The results show that the developed control law meets the requirements of a certain level of robustness. Keywords aerospace vehicles, position control, adaptive fuzzy control, PID adjusting, uncertainty

A novel re-entry trajectory design strategy enforcing inequality and terminal constraints in height-velocity plane

A novel re-entry trajectory design approach for a winged reusable launch vehicle that satisfies path and terminal state constraints in the height velocity plane is proposed in this paper. A two-phase re-entry trajectory is proposed. A height-velocity plane entry corridor is designed using all path and quasi-equilibrium glide constraints. The proposed method creates the reference trajectory inside the re-entry corridor using three piecewise polynomials, such as linear and logarithmic. A two-parameter search optimization problem minimizes terminal range-to-go error for this reference trajectory generation problem. Improved Search Space Reduction solves this optimization problem. The Improved Search Space Reduction algorithm is compared to the Grey Wolf Optimizer and Particle Swarm Optimisation algorithms to prove its efficacy and superiority. The reference bank angle is modified using feedback linearization and bank reversal logic to create an entry trajectory that meets all path and terminal constraints. The effectiveness of the proposed method has been validated through Monte Carlo simulations that incorporate uncertainties such as variations in initial state, uncertainties in aerodynamics, and atmospheric perturbations. The controller OPAL-RT OP4510 provides bank angle command at every instant based on the current state of the vehicle parameters associated with real-time simulator OP4510. The real-time hardware-in-loop simulation results show that the obtained re-entry trajectory satisfies all path and terminal constraints.

Aerodynamic Uncertainty Quantification of a Low-Pressure Turbine Cascade by an Adaptive Gaussian Process

Stochastic variations of the operation conditions and the resultant variations of the aerodynamic performance in Low-Pressure Turbine (LPT) can often be found. This paper studies the aerodynamic performance impact of the uncertain variations of flow parameters, including inlet total pressure, inlet flow angle, and turbulence intensity for an LPT cascade. Flow simulations by solving the Reynolds-averaged Navier–Stokes equations, the SST turbulence model, and γ−Re˜θt transition model equations are first carried out. Then, a Gaussian process (GP) based on an adaptive sampling technique is introduced. The accuracy of adaptive GP (ADGP) is proven to be high through a function experiment. Using ADGP, the uncertainty propagation models between the performance parameters, including total pressure-loss coefficient, outlet flow angle, Zweifel number, and the uncertain inlet flow parameters, are established. Finally, using the propagation models, uncertainty quantifications of the performance changes are conducted. The results demonstrate that the total pressure-loss coefficient and Zweifel number are sensitive to uncertainties, while the outlet flow angle is almost insensitive. Statistical analysis of the flow field by direct Monte Carlo simulation (MCS) shows that flow transition on the suction side and viscous shear stress are rather sensitive to uncertainties. Moreover, Sobol sensitivity analysis is carried out to specify the influence of each uncertainty on the performance changes in the turbine cascade.

Singular perturbation decomposition-based ride quality control of elastic aircraft

Considering the reduction of ride comfort under wind disturbance, the ride quality control method based on singular perturbation decomposition is proposed. For the dynamic model of elastic aircraft, the singular perturbation theory is used to decouple the model into the rigid-slow subsystem and the flexible-fast subsystem. Considering the additional time-varying disturbance and aerodynamic uncertainty of the rigid subsystem, the disturbance observer is designed to estimate disturbance effect and the neural network is used to deal with model uncertainty. The adaptive robust control is constructed using the composite estimation information as feedforward compensation and the tracking error of pitch rate and normal overload as feedback design. For the flexible subsystem, a nonsingular terminal sliding mode controller is designed to achieve active vibration suppression. The ride quality control law of elastic aircraft is obtained by combining the control inputs of the rigid and flexible subsystems, and the additional normal overload and elastic mode can be quickly restrained and converged. Based on Lyapunov stability analysis, the uniformly ultimate boundedness of the system is proved. The simulation results show that the proposed method can reduce the additional normal overload at the key positions of the aircraft under discrete gust and atmospheric turbulence, and the riding quality of elastic aircraft is effectively improved.

A Modified Model-Free Adaptive Control Method for Large-Scale Morphing Unmanned Vehicles

This paper investigates the attitude control problem for large-scale morphing unmanned vehicles. Considering the rapid time-varying and strong aerodynamic interference caused by large-scale morphing, a modified model-free control method utilizing only the system input and output is proposed. Firstly, a two-loop equivalent data model for the morphing unmanned vehicle is developed, which can better reflect the practical dynamics of morphing unmanned vehicles compared to the traditional compact form dynamic linearization data model. Based on the proposed data model, a modified model-free adaptive control (MMFAC) scheme is proposed, consisting of an external-loop and an inner-loop controller, so as to generate the required combined control torques. Additionally, in light of the aerodynamic uncertainties of the large-scale morphing unmanned vehicle, a rudder deflection actuator control scheme is designed by employing the model-free adaptive control approach. Finally, the boundedness of the closed-loop system and the convergence of tracking errors are guaranteed, based on the stability analysis. Additionally, numerical examples are presented to demonstrate the effectiveness and robustness of the proposed control scheme in the case of the effect of large-scale morphing.

Adaptive reinforcement learning control for a class of missiles with aerodynamic uncertainties and unmodeled dynamics

AbstractIn this paper, a super-twisting disturbance observer (STDO)-based adaptive reinforcement learning control scheme is proposed for the straight air compound missile system with aerodynamic uncertainties and unmodeled dynamics. Firstly, neural network (NN)-based adaptive reinforcement learning control scheme with actor-critic design is investigated to deal with the tracking problems for the straight gas compound system. The actor NN and the critic NN are utilised to cope with the unmodeled dynamics and approximate the cost function that are related to control input and tracking error, respectively. In other words, the actor NN is used to perform the tracking control behaviours, and the critic NN aims to evaluate the tracking performance and give feedback to actor NN. Moreover, with the aid of the STDO disturbance observer, the problem of the control signal fluctuation caused by the mismatched disturbance can be solved well. Based on the proposed adaptive law and the Lyapunov direct method, the eventually consistent boundedness of the straight gas compound system is proved. Finally, numerical simulations are carried out to demonstrate the feasibility and superiority of the proposed reinforcement learning-based STDO control algorithm.

Coordinated Symmetrical Altitude Position and Attitude Control for Stratospheric Airship Subject to Strong Aerodynamic Uncertainties

The stratospheric airship has important value in both commercial and military use. The altitude position control is very crucial for the airship to conduct specific missions, which is also a challenge because of both the severe relative aerodynamic mismatches and the large lag due to the quite low speed of the airship within 15 m/s. In this paper, a coordinated altitude and attitude control method was proposed to realize satisfactory altitude position control while maintaining the attitude stability by properly employing the two actuators, the propeller thrust and the elevator, in a consistent manner. In this process, the references for the vertical speed and the pitch were specified in a straightforward way of proportionating them by considering their physical characteristics and the inherent symmetrical relationship between them, which can be obtained through the kinematics. An extended disturbance observer was used to eliminate the severe aerodynamic uncertainties to symmetrically distribute the two actuator outputs by dynamically decoupling the vertical speed and the pitch angular rate loops into the two independent integrators. As a result, the explicit proportional controllers were sufficient to realize efficient command tracking. Rigorous theoretical investigation was provided to symmetrically prove the quantitative bounded property of the estimation and tracking errors. The simulation results demonstrated the effectiveness of the proposed approach, which can realize a 500-m altitude difference tracking within 200 s with less than 0.5 deg/s pitch angular rate.

Autonomous reliable intelligent control design under condition monitoring mechanism: Applied to hypersonic flight vehicles

For the six-degree of freedom dynamics of hypersonic flight vehicle, an autonomous reliable intelligent controller using online estimation and condition monitoring is designed in this paper. Based on model transformation, the outer-loop trajectory tracking is converted to the inner-loop attitude tracking. According to the aerodynamic model of large flight envelope and the thrust model of combined engine, the original dynamics is transformed into a switched nonlinear form. For each subsystem, neural network and nonlinear observer are employed to deal with aerodynamic uncertainty and external disturbance, while modeling errors are built to design composite update laws. Considering that the statically unstable HFV is prone to large deviation due to mode switching and wind disturbance, the condition monitoring signal with finite-time robust design is introduced to adjust control surfaces. With the average dwell time on the switching signal, the stability of the whole switched system is proved using Lyapunov approach. Simulation studies are performed to show the effectiveness of the design.

Uncertainty quantification analysis with arbitrary polynomial chaos method: Application in slipstream effect of propeller aircraft

The slipstream effect of propeller aircraft has a major impact on aircraft aerodynamic characteristics. Predicting the interaction of propeller slipstream on flow field over a complete aircraft has been an important topic in the field of fluid mechanics. In the flight test, we found that parameters in the flight data of propeller aircraft exhibit significant stochastic characteristics, and the mechanism of the influence of these stochastic parameters on aerodynamic characteristics of propeller aircraft needs to be further studied. Therefore, we combine arbitrary Polynomial Chaos method with Computational Fluid Dynamics (CFD) according to the characteristics of stochastic parameter distribution, propose an uncertainty CFD analysis method, and apply it to the aerodynamic uncertainty analysis of propeller aircraft. Results show that the standard deviation (Std) of the pressure coefficient [Formula: see text] on the wing surface will form an extreme region at windward side and separation position, respectively, which will gradually decrease with the flow direction. Furthermore, the slipstream will reduce the local Std on wing surface, and the downwash caused by slipstream will change the Std distribution on the leading edge of the horizontal tail.

Entry trajectory optimization for hypersonic vehicles based on convex programming and neural network

Trajectory optimization is important in achieving long-range atmospheric entry hypersonic vehicles. However, the trajectory optimization problem for atmospheric entry of hypersonic vehicles is characterized by strong nonlinearity, parameter uncertainties and multiple constraints. This study proposes a novel online trajectory optimization method for hypersonic vehicles based on convex programming and a feedforward neural network. A sequential second-order cone programming (SOCP) method is obtained to describe the trajectory optimization problem after the Gauss pseudo-spectral discretization. Subsequently, multiple optimal trajectories under aerodynamic uncertainties are generated offline and classified as the training and validation datasets. Then, a multilayer feedforward neural network is trained using these datasets and to output the optimal control command online. This method yields approximately 95% shorter computation time compared with the offline SOCP method. Considering the existence of the aerodynamic uncertainties, three terminal states calculated by this method are all smaller than 4.1%. In conclusion, the proposed trajectory optimization method can provide a high-precision, robust entry trajectory for hypersonic vehicles efficiently.

Incremental control system design and flight tests of a micro-coaxial rotor UAV

This paper applies the incremental nonlinear dynamic inversion (INDI) method to the control system design of coaxial rotor UAVs. The aerodynamic uncertainties and anti-disturbances problems are solved in the control system design. The designed controller gives the UAV excellent flight performance and control robustness. The incremental gain method (IGM) is adopted for the delay problem of the state derivatives. This method has the advantages of less calculation load and simple parameter adjustment, which provides excellent convenience for applying INDI controllers to coaxial rotor UAVs. The principle of IGM is further investigated by the discrete-time stability analysis methods, and the parameter selection strategy of the IGM is provided. The advantages of the controller design method are verified by comparative flight tests. The experimental results show that the average trajectory tracking error of INDI is only 58.3% of that of NDI under the same wind speed. When the angular acceleration delay is less than 0.06 s, IGM can easily keep the system stable. In addition, INDI has better robustness under obvious model errors and strong input disturbances.