Singularity Methods For Rapid Design And Analysis Of Cross Flow Propellers And Turbines

In their respective quests for higher energy efficiency and greater power extraction, the marine transportation and marine hydrokinetic industries have developed a class of devices known as cross-flow propellers and turbines. To predict the performance of these devices, engineers require accurate computational models. Traditional Reynolds-averaged Navier- Stokes equation (RANS) solvers provide an accurate, detailed description of the fluid flow, but the transient solution for each design iteration often requires a number of days to complete. At the other end of the spectrum, lumped airfoil models are fast, but accuracy is limited to certain design geometries and operating conditions. This thesis applies the vortex lattice method (VLM) to the cross-flow rotor design problem, to fill the need for a computational method to analyze cross-flow rotors that is at once fast, accurate, and robust. We first explore the standard lumped airfoil model as applied to a cross-flow propeller. We perform a design study to demonstrate the applicability of the model to design a propeller for an inland waterway vessel, and we conduct a model-scale experiment of a novel propeller design in order to validate our model for the propeller case. Through comparison with RANS simulations, however, we find the lumped airfoil model to be inaccurate for the turbine case. We then present the vortex lattice method, which includes implementation features to improve accuracy and computational efficiency for the cross-flow rotor design problem. We compare our VLM results to RANS solutions to demonstrate our significant improvement over the state-of-the-art lumped airfoil methods. Finally, we investigate the sensitivity of the model to changes in each of its parameters in order to determine the robustness of our model. Throughout this thesis, we follow the cycle of engineering design to make observations of the operation of cross-flow devices, gain insights on the physical phenomena governing their operation, and incorporate those insights into our models.