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
2021 ◽  
pp. 1-25
Author(s):  
A. Filippone ◽  
B. Parkes ◽  
N. Bojdo ◽  
T. Kelly

ABSTRACT Real-time flight data from the Automatic Dependent Surveillance–Broadcast (ADS-B) has been integrated, through a data interface, with a flight performance computer program to predict aviation emissions at altitude. The ADS-B, along with data from Mode-S, are then used to ‘fly’ selected long-range aircraft models (Airbus A380-841, A330-343 and A350-900) and one turboprop (ATR72). Over 2,500 flight trajectories have been processed to demonstrate the integration between databases and software systems. Emissions are calculated for altitudes greater than 3,000 feet (609m) and exclude landing and take-off cycles. This proof of concept fills a gap in the aviation emissions inventories, since it uses real-time flights and produces estimates at a very granular level. It can be used to analyse emissions of gases such as carbon dioxide ( $\mathrm{CO}_2$ ), carbon monoxide (CO), nitrogen oxides ( $\mathrm{NO}_x$ ) and water vapour on a specific route (city pair), for a specific aircraft, for an entire fleet, or on a seasonal basis. It is shown how $\mathrm{NO}_x$ and water vapour emissions concentrate around tropospheric altitudes only for long-range flights, and that the cruise range is the biggest discriminator in the absolute value of these and other exhaust emissions.


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

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 132
Author(s):  
Jeffrey Chi Wai Lee ◽  
Christy Yan Yu Leung ◽  
Mang Hin Kok ◽  
Pak Wai Chan

A comparison was made of two eddy dissipation rate (EDR) estimates based on flight data recorded by commercial flights. The EDR estimates from real-time data using the National Center for Atmospheric Research (NCAR) Algorithm were compared with the EDR estimates derived using the Netherlands Aerospace Centre (NLR) Algorithm using quick assess recorder (QAR) data. The estimates were found to be in good agreement in general, although subtle differences were found. The agreement between the two algorithms was better when the flight was above 10,000 ft. The EDR estimates from the two algorithms were also compared with the vertical acceleration experienced by the aircraft. Both EDR estimates showed good correlation with the vertical acceleration and would effectively capture the turbulence subjectively experienced by pilots.


2014 ◽  
Vol 602-605 ◽  
pp. 3140-3143
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao

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.


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