Specifying exhaust nozzle contours with a neural network

Abstract

Thrust vectoring is continuing to become an important issue in future military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a nonuniform flowfield containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. However, this approach is computational ly intensive, preventing nozzles from being designed on demand, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real time, two-dimensional nozzle contours. Results show that genetic-algorithm-trained neural networks provide a viable, time-efficient alternative for designing thrust vectoring nozzle contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.