New Technique for a Helicopter Flight Model Estimation Based on Flight Test Data

2006 ◽  
Author(s):  
Adrian Hiliuta ◽  
Ruxandra Botez
Keyword(s):  
Test Data ◽  
Flight Test ◽  
New Technique ◽  
Model Estimation ◽  
Helicopter Flight ◽  
Flight Model

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

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

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

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

scholarly journals Improving a real-time helicopter simulator model with linear input filters

2021 ◽  
Author(s):  
Pavle Šćepanović ◽  
Frederik A. Döring
Keyword(s):  
Feedback Control ◽  
Time Domain ◽  
Flight Test ◽  
Flight Mechanics ◽  
Mean Square ◽  
Qualification Test ◽  
Model Inversion ◽  
Helicopter Flight ◽  
The Time Domain ◽  
Mean Square Errors

AbstractFor a broad range of applications, flight mechanics simulator models have to accurately predict the aircraft dynamics. However, the development and improvement of such models is a difficult and time consuming process. This is especially true for helicopters. In this paper, two rapidly applicable and implementable methods to derive linear input filters that improve the simulator model are presented. The first method is based on model inversion, the second on feedback control. Both methods are evaluated in the time domain, compared to recorded helicopter flight test data, and assessed based on root mean square errors and the Qualification Test Guide bounds. The best results were achieved when using the first method.

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