A hybrid frequency-time domain technique for ground effect identification

2010 ◽  
Vol 114 (1156) ◽  
pp. 377-385
Author(s):  
A. Vitale ◽  
N. Genito ◽  
L. Garbarino ◽  
U. Ciniglio ◽  
F. Corraro
Keyword(s):  
Time Domain ◽  
Flight Control ◽  
Ground Effect ◽  
Model Parameters ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Hybrid Frequency ◽  
Effect Model ◽  
Aerodynamic Derivatives ◽  
Aerodynamic Parameters

Abstract The estimation from flight data of aerodynamic parameters for vehicle in steady-state conditions, perturbed by an identification manoeuvre, is a well-established technology, whereas system identification from dynamic flight data is a subject of continuous interest. This paper presents a hybrid frequency and time domain technique for identification of vehicle longitudinal aerodynamic model, including the ground effect. Identification is performed in the framework of a multi-step approach, in which, first aerodynamic coefficients are estimated in the frequency domain, using an equation error method; then time domain techniques are applied to identify out of ground effect aerodynamic derivatives and ground effect model parameters. The technique was successfully applied to flight data of an experimental ultra light aircraft. Identification results showed that the proposed method works properly also in the dynamic phases of the flight or when no dedicated identification manoeuvres are executed. Moreover, the identified longitudinal aerodynamic model was used to design the flight control system that successfully performed many autonomous landings.

A New Quasi-Steady In-Ground Effect Model for Rotorcraft Unmanned Aerial Vehicles

2019 ◽  
Author(s):  
Xiang He ◽  
Kam K. Leang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Ground Plane ◽  
Ground Effect ◽  
P Value ◽  
Model Parameters ◽  
Blade Element Theory ◽  
Aerial Vehicles ◽  
Effect Model ◽  
3D Printed ◽  
Induced Velocity

Abstract This paper introduces a new quasi-steady in-ground effect model for rotorcraft unmanned aerial vehicles to predict the aerodynamic behavior when the vehicle’s rotors approach ground plane. The model assumes that the compression of the outflow due to the presence of ground plane induces a change in the induced velocity that can drastically affect the thrust and power output. The new empirical model describes the change in thrust as a function of the distance to an obstacle for a rotor in hover condition. Using blade element theory and the method of image, the model parameters are described in terms of the rotor pitch angle and solidity. Experiments with off-the-shelf, fixed-pitch propellers and 3D-printed variable pitch propellers are carried out to validate the model. Experimental results suggest good agreement with 9.5% root-mean-square error (RMSE) and 97% p-value of statistic significance.

Induced-drag reduction of wing–wings and wings–ground configurations

2004 ◽  
Vol 108 (1088) ◽  
pp. 523-530
Author(s):  
L. Marino
Keyword(s):  
Drag Reduction ◽  
Ground Effect ◽  
Formation Flight ◽  
Optimum Configuration ◽  
Induced Drag ◽  
Aerodynamic Model ◽  
Line Theory ◽  
Aerodynamic Parameters ◽  
Lifting Line ◽  
Lifting Line Theory

Abstract The problem of induced drag reduction during formation flight is revisited by means of a simple aerodynamic model based on lifting line theory. The optimum configuration for minimum induced drag is analysed both in and out of the ground effect and the influence of the main geometrical and aerodynamic parameters is considered. The results are discussed and compared with existing numerical and experimental data.

Inflight Aerodynamic Parameter Estimation for Fixed Wing Unmanned Aerial Vehicle

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
R. Jaganraj ◽  
R. Velu
Keyword(s):  
Parameter Estimation ◽  
Unmanned Aerial Vehicle ◽  
Aerodynamic Parameter ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Aerial Vehicle ◽  
Frame Work ◽  
Aerodynamic Parameters

This paper presents the frame work for aerodynamic parameter estimation for small fixed wing unmanned aerial vehicle (UAV). The recent development in autopilot hardware for small UAV enables the in-flight data collection of flight characteristics. A methodology is outlined to collect, process and arrive at a conclusion from the in-flight data using commercial flight controller of under 2kg (micro) fixed wing aircraft, ‘VAF01’ for which a Fault Detection and Identification (FDI) system is under development. As a part of the FDI, the linear longitudinal (3 DOF) aerodynamic model is developed and in-flight experimental data is used to estimate the longitudinal aerodynamic parameters. The Flight Path Reconstruction is completed with the acquired parameters from in-flight experiments and results are discussed for further utilization of them.

A Time-Domain Fluid-Structure Interaction Analysis of Fan Blades

2008 ◽  
Author(s):  
Seung Ho Cho ◽  
Taehyoun Kim ◽  
Seung Jin Song
Keyword(s):  
Time Domain ◽  
Vortex Lattice ◽  
Interaction Analysis ◽  
Three Dimensional ◽  
Incoming Flow ◽  
Structure Interaction ◽  
Unsteady Aerodynamic ◽  
Aerodynamic Model ◽  
Blade Row ◽  
Fan Blade

This paper presents aerodynamic and aeromechanical analyses for an entire row of fan blades (i.e. tens of blades with a finite aspect ratio) subject to a uniform incoming flow. In this regard, a new unsteady three-dimensional vortex lattice model has been developed for multiple blades in discrete time domain. Using the new model, the characteristics of the unsteady aerodynamic forces on vibrating blades, including their temporal development, are examined. Also, the new aerodynamic model is applied to examine the aeromechanical behavior of fan blades by using a standard eigenvalue analysis. For this analysis, the fan blades have been modeled as three-dimensional plates, and, increasing the number of blades (or solidity) is predicted to destabilize the fan blade row.

scholarly journals A Case Study on the Detection and Prognosis of Internal Leakages in Electro-Hydraulic Flight Control Actuators

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 215
Author(s):  
Antonio Carlo Bertolino ◽  
Andrea De Martin ◽  
Giovanni Jacazio ◽  
Massimo Sorli
Keyword(s):  
Flight Control ◽  
Particle Filtering ◽  
Health Management ◽  
State Of The Art ◽  
Commercial Aircraft ◽  
Flight Data ◽  
Prognostic System ◽  
Hydraulic Servo ◽  
Definition Of

Electro-hydraulic servo-actuators (EHSAs) are currently considered the state-of-the art solution for the control of the primary flight control systems of civil and military aircraft. Combining the expected service life of a commercial aircraft with the fact that electro-hydraulic technology is employed in the vast majority of currently in-service aircraft and is planned to be used on future platforms as well, the development of an effective Prognostic and Health Management (PHM) system could provide significant advantages to fleet operators and aircraft maintenance, such as the reduction of unplanned flight disruptions and increased availability of the aircraft. The occurrence of excessive internal leakage within the EHSAs is one of the most common causes of return from the field of flight control actuators, making this failure mode a priority in the definition of any dedicated PHM routine. This paper presents a case study on the design of a prognostic system for this degradation mode, in the context of a wider effort toward the definition of a prognostic framework suitable to work on in-flight data. The study is performed by means of a high-fidelity simulation model supported by experimental activities. Results of both the simulation and the experimental work are used to select a suitable feature, then implemented within the prognostic framework based on particle filtering. The algorithm is at first theoretically discussed, and then tested against several degradation patterns. Performances are evaluated through state-of-the-art metrics, showing promising results and providing the basis towards future applications on real in-flight data.

Physical Essence of Stall Phenomena and its WNN-Based Aerodynamic Modeling from Flight Data

2014 ◽  
Vol 602-605 ◽  
pp. 3140-3143
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Flight Data ◽  
Numerical Examples ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Aerodynamic Method ◽  
Physical Essence ◽  
Set Up

To understand the characteristics of aircraft stall for better aerodynamic model, the physical essence of the stall phenomena of aircraft is first introduced, and then a Wavelet Neural Network (WNN) is proposed to set up the stall aerodynamic model. Numerical examples indicates that through the deep cognition of the stall phenomena of aircraft the proposed stall aerodynamic method has a better accuracy than the traditional neural network and is also effective and feasible.

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhigang Wang ◽  
Aijun Li ◽  
Lihao Wang ◽  
Xiangchen Zhou ◽  
Boning Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Aerodynamic Parameter ◽  
Content Type ◽  
Flight Data ◽  
Output Error ◽  
Aerodynamic Derivatives ◽  
Levenberg Marquardt ◽  
Error Method

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

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