A deep artificial neural network-assisted genetic-algorithm method to optimize a slotted hydrofoil (Python, Matlab, C++)

This project is the continuation of the Study on the optimization of a slotted hydrofoil using Genetic algorithm project. In that project, the Gaussian Process Regression method (called Kriging) as a surrogate model is used alongside a genetic algorithm method to optimize a slotted hydrofoil. Here, a deep Artificial Neural Network (ANN) is used to be served as a surrogate model in the process of the optimization. For this aim, a deep ANN is developed to accurately emulate a physical solver based on the finite volume discretization of the governing equations. The physical solver is written in C++ and the deep machine learning and the connection between the CFD solver and the optimizer are established in Matlab and python. Both the versions of Matlab and python are available upon request. It is recommended to read this document before going to the details of this project.

Results