On the application of data-driven modeling within the rotorcraft design space

For highly iterative rotorcraft-based design tasks, such as design optimization or trajectory prediction, it is essential that there exists an aerodynamic model capable of providing an accurate representation of turbulent flow fields at a minimal computational expense. While high-fidelity computational fluid dynamics (CFD) has been proven capable of providing accurate aerodynamic predictions, for rotorcraft-based applications CFD's large computational expense has greatly limited its integration into highly iterative design tasks.This study will investigate the feasibility of leveraging proper orthogonal decomposition (POD) and convolutional neural networks (CNNs) for surrogate modeling within the rotorcraft design space. Surrogate modeling techniques are applied to both surface flow and rotorcraft wake modeling. For surface flow modeling, rotorcraft-based store separation is simulated using CFD from which both POD and CNN based surrogate models are generated for surface pressure and shear stress load distributions. Surrogate model predictions are coupled with the equations of motion to provide store trajectory replications. For rotor wake modeling, an isolated rotor is simulated in hover. Surrogate models are derived using both POD and CNN from which flow field reconstructions and predictions are generated.