Accession Number:

ADA446788

Title:

A Comparison of Main Rotor Smoothing Adjustments Using Linear and Neural Network Algorithms

Descriptive Note:

Master's thesis Jun 2005-Mar 2006

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH DEPT OF AERONAUTICS AND ASTRONAUTICS

Personal Author(s):

Report Date:

2006-03-01

Pagination or Media Count:

146.0

Abstract:

Helicopter main rotor smoothing is a maintenance procedure that is routinely performed to minimize airframe vibrations induced by non-uniform mass andor aerodynamic distributions in the main rotor system. This important task is both time consuming and expensive, so improvements to the process have long been sought. Traditionally, vibrations have been minimized by calculating adjustments based on an assumed linear relationship between adjustments and vibration response. In recent years, artificial neural networks have been trained to recognize non-linear mappings between adjustments and vibration response. This research was conducted in order observe the character of the adjustment mapping of the Vibration Management Enhancement Programs PC-Ground Base System PC-GBS. Flight data from the UH-60, AH-64A, and AH-64D were utilized during the course of this study. What has been determined is that the neural networks of PC-GBS produce adjustments that can be reproduced by a linear algorithm, thus implying that the shape of the mapping is in fact linear.

Subject Categories:

  • Helicopters
  • Anatomy and Physiology
  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE