Aerodynamic parameters of the X-31 drop model estimated from flight-data at high angles of attack

1992 ◽  
Author(s):  
VLADISLAV KLEIN ◽  
KEITH NODERER
Keyword(s):  
Flight Data ◽  
Aerodynamic Parameters ◽  
High Angles Of Attack

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.

Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data

2017 ◽  
Vol 121 (1237) ◽  
pp. 320-340 ◽  
Author(s):  
S. Saderla ◽  
R. Dhayalan ◽  
A.K. Ghosh
Keyword(s):  
Parameter Estimation ◽  
Flow Separation ◽  
Linear Models ◽  
Rectangular Cross Section ◽  
Flight Test ◽  
Point Mapping ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Non Linear ◽  
High Angles Of Attack

ABSTRACTThe paper presents the aerodynamic characterization of a low-speed unmanned aerial vehicle, with cropped delta planform and rectangular cross section, at and around high angles-of-attack using flight test methods. Since the linear models used for identification from flight data at low and moderate angles of attack become unsuitable for accurate parameter estimation at high angles of attack, a non-linear aerodynamic model has to be considered. Therefore, the Kirchhoff's flow separation model was used to incorporate the non-linearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The Maximum Likelihood (ML) and Neural Gauss-Newton (NGN) methods were used to perform the parameter estimation on one set of low angle-of-attack and one set of near-stall flight data. It is evident from the estimates that the NGN method, which does not involve solving equations of motion, performs on a par with the classical ML method. This may be attributed to the reason that NGN method uses a neural network which has been trained by performing point to point mapping of the measured flight data. This feature of NGN method enhances its application over a wider envelope of high angles of attack flight data.

Estimation of the longitudinal aerodynamic parameters from flight data for the NASA F/A-18 HARV

1996 ◽  
Author(s):  
Marcello Napolitano ◽  
Alfonso Paris ◽  
Brad Seanor ◽  
Albion Bowers
Keyword(s):  
Flight Data ◽  
Aerodynamic Parameters

scholarly journals Fuel Consumption Model of the Climbing Phase of Departure Aircraft Based on Flight Data Analysis

2019 ◽  
Vol 11 (16) ◽  
pp. 4362 ◽  
Author(s):  
Zhang ◽  
Huang ◽  
Liu ◽  
Zhang
Keyword(s):  
Wind Tunnel ◽  
Fuel Consumption ◽  
Operating Cost ◽  
Fuel Load ◽  
Accurate Estimation ◽  
Flight Data ◽  
Proposed Model ◽  
Aircraft Performance ◽  
Aerodynamic Parameters ◽  
Consumption Model

Accurate estimation of the fuel consumed during aircraft operation is key for determining the fuel load, reducing the airline operating cost, and mitigating environmental impacts. Aerodynamic parameters in current fuel consumption models are obtained from a static diagram extracted from the outcomes of wind tunnel experiments. Given that these experiments are performed in a lab setting, the parameters cannot be used to estimate additional fuel consumption caused by aircraft performance degradation. In addition, wind tunnel experiment results rarely involve the influence of crosswind on fuel consumption; thus, the results could be inaccurate when compared with field data. This study focuses on the departure climbing phase of aircraft operation and proposes a new fuel consumption model. In this model, the relationships between aerodynamic parameters are extracted by fitting quick access recorder (QAR) actual flight data, and the crosswind effect is also considered. Taking QAR data from two airports in China, the accuracy of the proposed model and its transferability are demonstrated. Applying the proposed model, the fuel saving of a continuous climb operation (CCO) compared with the traditional climb operation is further quantified. Finally, how aircraft mass, climbing angle, and different aircraft models could affect the fuel consumption of the climbing phase of aircraft operation is investigated. The proposed fuel consumption model fills gaps in the existing literature, and the method can be used for developing specific fuel consumption models for more aircraft types at other airports.

ANFIS-Delta method for aerodynamic parameter estimation using flight data

2018 ◽  
Vol 233 (8) ◽  
pp. 3016-3032 ◽  
Author(s):  
Ajit Kumar ◽  
Ajoy Kanti Ghosh
Keyword(s):  
Fuzzy Inference ◽  
Simulated Data ◽  
Delta Method ◽  
Feed Forward Neural Network ◽  
Aerodynamic Parameter ◽  
Inference System ◽  
Flight Data ◽  
Novel Approach ◽  
Neuro Fuzzy ◽  
Aerodynamic Parameters

In this paper, aerodynamic parameters have been estimated using neuro-fuzzy-based novel approach (ANFIS-Delta). ANFIS-Delta is an extension of a feed-forward neural network based Delta method. This method augments the philosophies of an adaptive neuro-fuzzy inference system (ANFIS) in the Delta method. The current work studies the comparison of ANFIS-Delta estimated results with the existing methods using the flight data gathered on the Hansa-3 research aircraft at IIT Kanpur and also, demonstrates the efficacy of the algorithm on DLR HFB-320 aircraft data. Further, the robustness of the ANFIS-Delta is examined using simulated data with known measurement noise of various strength and estimated parameters are compared with the wind tunnel extracted aerodynamic parameters.

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