A Methodology to Predict the Empennage In-flight Loads of a General Aviation Aircraft Using Backpropagation Neural Networks
Author: David Kim
Publisher:
Published: 2001
Total Pages: 32
ISBN-13:
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Author: David Kim
Publisher:
Published: 2001
Total Pages: 32
ISBN-13:
DOWNLOAD EBOOKAuthor: Maciej Marciniak
Publisher:
Published: 1996
Total Pages: 106
ISBN-13:
DOWNLOAD EBOOK"The purpose of this research was to develop a methodology for prediction of strain in the tail section of a general aviation aircraft and to determine the minimum set of sensors necessary to adequately train the neural networks."--Leaf v.
Author: David Kim
Publisher:
Published: 2001
Total Pages: 26
ISBN-13:
DOWNLOAD EBOOKAuthor: Philippe Marchand
Publisher:
Published: 2000
Total Pages: 204
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DOWNLOAD EBOOKAuthor: Oleg Paul Levinski
Publisher:
Published: 2001
Total Pages: 38
ISBN-13:
DOWNLOAD EBOOKThe use of artificial neural networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The buffet loads prediction method which is developed depends on experimental data to train the ANN alogorithm and is able to expand its knowledge base with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Bases on the ANN methodology investigated, a buffet prediction system can be developed to characterise the F/A-18 vertical tail buffet environment at different flight conditions. It will allow better understanding and more efficient alleviation of the empennage buffeting problem.
Author: Randolph Carlyle Thompson
Publisher:
Published: 2007
Total Pages: 84
ISBN-13:
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Publisher:
Published: 1999
Total Pages: 974
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DOWNLOAD EBOOKAuthor: Nicolas Vincent-Boulay
Publisher:
Published: 2020
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKPerformance models used in the aircraft development process are dependent on the assumptions and approximations associated with the engineering equations used to produce them. The design and implementation of these highly complex engineering models are typically associated with a longer development process. This study proposes a non-deterministic approach where machine learning techniques using Artificial Neural Networks are used to predict specific aircraft parameters using available data. The approach yields results that are independent of the equations used in conventional aircraft performance modeling methods and rely on stochastic data and its distribution to extract useful patterns. To test the viability of the approach, a case study is performed comparing a conventional performance model describing the takeoff ground roll distance with the values generated from a neural network using readily-available flight data. The neural network receives as input, and is trained using, aircraft performance parameters including atmospheric conditions (air temperature, air pressure, air density), performance characteristics (flap configuration, thrust setting, MTOW, etc.) and runway conditions (wet, dry, slope angle, etc.). The proposed predictive modeling approach can be tailored for use with a wider range of flight mission profiles such as climb, cruise, descent and landing.
Author: M. A. Ferman
Publisher:
Published: 1990
Total Pages: 18
ISBN-13:
DOWNLOAD EBOOKA unified approach has been derived from predicting buffet response of fighter aircraft empennage operating in high angle of attack maneuvering conditions. Since the advent of high angle of attack flight using controlled vortex flows, incidences of severe structural stress, and in some cases, damages have resulted. This has been pronounced on twin tailed aircraft, including McDonnell's F-15 and F/A-18 aircraft which require structural beef-ups to their empennage. Two concepts are shown for predicting buffet response of empennage. The first approach uses elastically scaled models in wind tunnel tests to provide full scale prediction. The second approach is based on calculations using measured pressure data from wind tunnel tests. The latter method is more versatile. Detailed applications are shown for the F/A-18 empennage, while other applications at McDonnell are noted.
Author: Magnus Norgaard
Publisher:
Published: 1997
Total Pages: 24
ISBN-13:
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