Helicopter Flight Test Data Simulation Using CCSU Flight Simulator

2019 ◽  
Author(s):  
Fu-Shang (John) Wei ◽  
Kenneth Trochsler ◽  
David J. Broderick
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Flight Simulator ◽  
Data Simulation ◽  
Helicopter Flight

Development of Fly Neighborly helicopter model specific operational noise abatement guidance from acoustic flight test data

2021 ◽  
Vol 263 (1) ◽  
pp. 5671-5677
Author(s):  
Juliet Page ◽  
Amanda Rapoza ◽  
Eric Jacobs
Keyword(s):  
Test Data ◽  
Noise Exposure ◽  
Flight Test ◽  
Operating Conditions ◽  
Noise Emission ◽  
Noise Abatement ◽  
Local Conditions ◽  
Planning Stage ◽  
Helicopter Flight ◽  
Us Government

Improved helicopter noise abatement guidance has been developed based on acoustic test data acquired by NASA, FAA and Volpe in support of the Helicopter Association International (HAI)'s Fly Neighborly Program. This higher fidelity material was developed to supplement previous training programs based on pilot and operator feedback. The manner of presentation allows pilots to readily interpret the directional noise emission of their vehicle at different operating conditions. Flight path, airspeed, approach descent rate, and deceleration rate can be assessed to optimize flight patterns both during the pre-flight planning stage and in real time during flight operations in response to local conditions and events. The resultant sound directivity would be displayed as colored noise exposure contours overlaid onto a map of the area in the vicinity of the helicopter. New Fly Neighborly training modules have been developed utilizing directional operational noise plots based on Volpe's Advanced Acoustic Model (AAM) modeling with empirical sound sphere data from dedicated US Government helicopter flight tests. This paper will describe the acoustic analyses and will present the updated noise guidance for the AS350, AS365, AW139, Bell 205, Bell 206, Bell 407, R-44, R-66 and S-76D helicopters.

scholarly journals Correlation of SA349/2 Helicopter Flight Test Data with a Comprehensive Rotorcraft Model

1988 ◽  
Vol 33 (2) ◽  
pp. 31-42 ◽  
Author(s):  
Gloria K. Yamauchi ◽  
Ruth M. Heffernan ◽  
Michel Gaubert
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Helicopter Flight

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.

Analysis of OH-6A Helicopter Flight Test Data Using Lissajous Figures

1997 ◽  
Vol 34 (1) ◽  
pp. 20-22 ◽  
Author(s):  
Nesrin Sarigul-Klijn ◽  
Martinus M. Sarigul-Klijn
Keyword(s):  
Test Data ◽  
Flight Test ◽  
Helicopter Flight ◽  
Lissajous Figures

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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