Search Results

The architecture and training of artificial neural networks are briefly described. Five applications of these networks to design and analysis problems are presented; three in aerodynamics and two in flight dynamics.

In nonlinear cases, the SDG method requires multidimensional search procedures. However, in linear cases only one-dimensional search procedures are required to identify the critical gust load conditions. In this study the application of the backpropagation ANN method as a multi-dimensional modeling tool has been proposed to model or identify the global and local extrema of one-dimensional gust load responses. The maximum and minimum response values of ramp-step input gust profiles were considered to investigate the ANN modeling capability and effectiveness. The actual SDG analysis for nonlinear cases was hypothesized to be performed over a large and sparse domain, therefore the ANN could be trained to quickly identify the region of the domain containing the global extrema. The SDG analysis, then, could be concentrated on a smaller region thereby reducing computation time.

The method uses inverse control to decouple the flight controls and to modify the handling qualities of the aircraft, while employing artificial neural networks in order to compensate for any modeling error. These errors can stem from any differences between the model and the actual aircraft.