Neural network applications in fluid dynamics

In the present study neural networks are investigated for use, in fluid dynamics simulations. These range from static simulations for a simple 2D geometry like an airfoil section to dynamic simulations for a complicated 3D geometry like a model submarine. A detailed analysis of the application of neural networks for the case of vehicle trajectory determination is provided. This involves identifying the physics of the problem and tailoring it to a neural network architecture. The learning process involves training the neural network on a variety of maneuvers and the prediction process involves applying new maneuvers to the neural network. The results are compared to both experimental data and CFD data for the training sets and the prediction sets. The need and scope for parallelization in neural networks is also examined and the performance of pattern partitioning and vertical partitioning algorithms is studied.