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The applicability of computational fluid dynamics (CFD) based design tools depend on the accuracy and complexity of the physical models, for example turbulence models, which remains an unsolved problem in physics, and rotor models that dictates the computational cost of rotorcraft and wind/hydro turbine farm simulations. The research focuses on investigation of the ability of neural networks to learn correlation between desired modeling variables and flow parameters, thereby providing surrogate models. For the turbulence modeling, the machine learned turbulence model is developed for unsteady boundary layer flow, and the predictions are validated against DNS data and compared with one-equation unsteady Reynolds Averaged Navier-Stokes (URANS) predictions. The machine-learned model performs much better than the URANS model due to its ability to incorporate the non-linear correlation between turbulent stresses and rate-of-strain. The development of the surrogate rotor model builds on the hypothesis that if a model can mimic the axial and tangential momentum deficit generated by a blade resolved model, then it should produce a qualitatively and quantitatively similar wake recovery. An initial validation of the hypothesis was performed, which showed encouraging results.

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