scholarly journals Aerodynamic Model Identification of the Flying V from Sub-Scale Flight Test Data

2022 ◽  
Author(s):  
Alberto Ruiz Garcia ◽  
Malcom Brown ◽  
Daniel Atherstone ◽  
Nando v. Arnhem ◽  
Roelof Vos
Keyword(s):  
Test Data ◽  
Model Identification ◽  
Flight Test ◽  
Aerodynamic Model

scholarly journals Linear Aerodynamic Model Identification of a Flapping Wing MAV Based on Flight Test Data

2013 ◽  
Vol 5 (4) ◽  
pp. 273-286 ◽  
Author(s):  
J.V. Caetano ◽  
C.C. de Visser ◽  
G.C.H.E. de Croon ◽  
B. Remes ◽  
C. de Wagter ◽  
...  
Keyword(s):  
Test Data ◽  
Model Identification ◽  
Flight Test ◽  
Flapping Wing ◽  
Aerodynamic Model

Wavelet Neural Network with PLS Feature Extraction and its Application in Aerodynamic Modeling for Aircraft Stall

2014 ◽  
Vol 602-605 ◽  
pp. 3131-3134
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Test Data ◽  
Flight Test ◽  
Data Simulation ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Simulation Results

An aerodynamic modeling method based on WNN with Partial Least Squares (PLS) is proposed. In the method, PLS first is applied to extract the feature of original aerodynamic data samples, and then the obtained feature is used to establish the WNN aerodynamic model for aircraft stall from flight test data. Simulation results are given to illustrate that the proposed method is effective and feasible.

Experimental Power Model Identification of a Flapping Wing Air Vehicle With Flight Test Data

2017 ◽  
Author(s):  
John Gerdes ◽  
Hugh A. Bruck ◽  
Satyandra K. Gupta
Keyword(s):  
Test Data ◽  
Model Identification ◽  
Flight Test ◽  
Flight Time ◽  
Mission Planning ◽  
Power Model ◽  
Data Set ◽  
Air Vehicle ◽  
Window Design

Current mission planning typically involves feed-forward strategies that do not adapt flight conditions and mission properties according to live vehicle power state updates. This results in autonomous flights that adhere to rigid rules for flight time, potentially missing opportunities to enhance mission range or improve recovery likelihood. To address these shortfalls, aircraft states and flight dynamics under varying conditions are experimentally characterized using a customized on-board data collection suite consisting of sensors and microprocessors. Post-processing is used to improve the quality of the data extracted from the sensors. A custom filtering window design provides timescale separation and filtering and cycle-synchronized averaging reduces noise in the data set. A linear vehicle power model is derived from the test data that describes operation in the neighborhood of stable cruising flight conditions. The vehicle power model is extended with models of the drive motors and battery to provide a framework for making mission-level predictions about the power requirements. The framework described is suitable for usage in adapting autonomous flight behaviors and mission planning to changing power availability.

Stall Buffet Modeling using Swept Wing Flight Test Data

2021 ◽  
Author(s):  
Sven Marschalk ◽  
Peter C. Luteijn ◽  
Dirk van Os ◽  
Daan M. Pool ◽  
Coen C. de Visser
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Swept Wing
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