Partitioning of flight data for aerodynamic modeling of aircraft at high angles of attack

1989 ◽  
Vol 26 (4) ◽  
pp. 334-339 ◽  
Author(s):  
James G. Batterson ◽  
Vladislav Klein
Keyword(s):  
Flight Data ◽  
Aerodynamic Modeling ◽  
High Angles Of Attack

scholarly journals Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

2016 ◽  
Vol 53 (5) ◽  
pp. 1261-1297 ◽  
Author(s):  
Jay M. Brandon ◽  
Eugene A. Morelli
Keyword(s):  
Real Time ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Nonlinear Aerodynamic

Spin Aerodynamic Modeling for a Fixed-Wing Aircraft Using Flight Data

2022 ◽  
Author(s):  
James L. Gresham ◽  
Benjamin M. Simmons ◽  
Jeremy W. Hopwood ◽  
Craig A. Woolsey
Keyword(s):  
Flight Data ◽  
Aerodynamic Modeling

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.

Research on Longitudinal Aerodynamics Modeling Method for Aircraft Stall Based on Wavelet Neural Network from Flight Data

2014 ◽  
Vol 602-605 ◽  
pp. 3144-3147
Author(s):  
Xu Sheng Gan ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Modeling Method ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Modeling Problem ◽  
Modeling Flow

For the aerodynamic modeling problem from measured flight data for aircraft stall, a WNN aerodynamic modeling method is proposed. According to the aerodynamic modeling flow from flight data for aircraft stall, WNN is introduced to establish the aerodynamic model of aircraft for stall phenomenon. Experiment shows that the method can improve the modeling ability of WNN, and is suitable for actual aerodynamic modeling for aircraft stall.

High-fidelity aerodynamic modeling of an aircraft using OpenFoam – application on the CRJ700

2021 ◽  
pp. 1-22
Author(s):  
M. Segui ◽  
F.R. Abel ◽  
R.M. Botez ◽  
A. Ceruti
Keyword(s):  
Open Source Software ◽  
Absolute Error ◽  
Flight Simulator ◽  
High Fidelity ◽  
Maximum Absolute Error ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Data Source ◽  
Source Level

Abstract This study is focused on the development of longitudinal aerodynamic models for steady flight conditions. While several commercial solvers are available for this type of work, we seek to evaluate the accuracy of an open source software. This study aims to verify and demonstrate the accuracy of the OpenFoam solver when it is used on basic computers (32–64GB of RAM and eight cores). A new methodology was developed to show how an aerodynamic model of an aircraft could be designed using OpenFoam software. The mesh and the simulations were designed only using OpenFoam utilities, such as blockMesh, snappyHexMesh, simpleFoam and rhoSimpleFoam. For the methodology illustration, the process was applied to the Bombardier CRJ700 aircraft and simulations were performed for its flight envelope, up to M0.79. Forces and moments obtained with the OpenFoam model were compared with an accurate flight data source (level D flight simulator). Excellent results in data agreement were obtained with a maximum absolute error of 0.0026 for the drag coefficient, thus validating a high-fidelity aerodynamic model for the Bombardier CRJ-700 aircraft.

Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm

2012 ◽  
Vol 83 ◽  
pp. 212-221 ◽  
Author(s):  
Meng Yue-bo ◽  
Zou Jian-hua ◽  
Gan Xu-sheng ◽  
Zhao Liang
Keyword(s):  
Pso Algorithm ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Improved Pso
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