Fuel Efficiencies Through Airframe Improvements

The factors of continuing strong growth in air traffic volume, the vital role of the air transport system on the economy, and concerns about the environmental impact of aviation have added focus to the National Aeronautics Research Policy. To address these concerns in the context of the National Policy, NASA has set aggressive goals in noise reduction, emissions, and energy consumption. With respect to the goal of reducing energy consumption in the fleet, the development of promising airframe technologies is required to realize the significant improvements that are desired. Furthermore, the combination of advances in materials and structures with aerodynamic technologies may lead to a paradigm shift in terms of potential configurations for the future. Some of these promising airframe technologies targeted at improved efficiency are highlighted

Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned