Artificial Neural Networks for Maximimum Gust Load Search: An Application in Statistical Discrete Gust Methods

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 computational work for training using this technique would be faster and simpler in comparison with multi-dimensional table-look-up techniques. By reducing the number of gust responses needed to be numerically calculated, the search efficiency of the critical gust load condition would be improved considerably. The research was performed in two phases, first for one-dimensional search cases then this was extended to multi-dimensional search cases.