Theses and Dissertations

Issuing Body

Mississippi State University


Pankajakshan, Ramesh

Committee Member

Whitfield, David

Committee Member

Soni, Bharat

Date of Degree


Original embargo terms

MSU Only Indefinitely

Document Type

Graduate Thesis - Campus Access Only


Computational Engineering (program)

Degree Name

Master of Science


College of Engineering


Computational Engineering Program


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.



fluid dynamics||backpropagation||computational fluid dynamics||neural networks