  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 NavierStokes (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.
