(Georgia Institute of Technology, 2009-05-04)
Nucci, Michael
Rising oil costs have created a need for a new sustainable energy source. Currently wind energy is beginning to fulfill this need. With many financial incentives being offered for clean energy, wind turbines are a promising green energy source. Wind turbine analysis can be difficult and costly. Accurate spanwise pressure distributions are difficult to measure experimentally, and a full-fledged Navier-Stokes analysis is very computationally expensive. A comparison of two separate computer codes was performed. These include PROPID, which uses a blade element momentum theory method and empirical data about the wind turbine airfoil. The second method is a Reynolds Averaged Navier-Stokes (RANS) CFD code called windrotor2 which also was used to predict the performance of the NREL Phase VI rotor. Once the codes were validated they were then used to predict the performance of new rotor designs. This research shows that PROPID can be used as a surrogate model for turbine analysis and design. PROPID can be shown to predict performance that is on par with CFD methods in terms of accuracy, but takes only a fraction of the time to perform the analysis. PROPID can also be shown to accurately predict the performance of new turbine configurations as long as empirical data is readily available.
(Georgia Institute of Technology, 2009-05-04)
Verma, Siddhartha
Automated shape optimization involves making suitable modifications to a geometry that can lead to significant improvements in aerodynamic performance. Currently available mid-fdelity Aerodynamic Optimizers cannot be utilized in the late stages of the design process for performing minor, but consequential, tweaks in geometry. Automated shape optimization involves making suitable modifications to a geometry that can lead to significant improvements in aerodynamic performance. Currently available mid-fidelity Aerodynamic Optimizers cannot be utilized in the late stages of the design process for performing minor, but consequential, tweaks in geometry. High-fidelity shape optimization techniques are explored which, even though computationally demanding, are invaluable since they can account for realistic effects like turbulence and viscocity. The high computational costs associated with the optimization have been avoided by using an indirect optimization approach, which was used to dcouple the effect of the flow field variables on the gradients involved. The main challenge while performing the optimization was to maintain low sensitivity to the number of input design variables. This necessitated the use of Reverse Automatic differentiation tools to generate the gradient. All efforts have been made to keep computational costs to a minimum, thereby enabling hi-fidelity optimization to be used even in the initial design stages. A preliminary roadmap has been laid out for an initial implementation of optimization algorithms using the adjoint approach, into the high fidelity CFD code NASCART-GT.High-fidelity shape optimization techniques are explored which, even though computationally demanding, are invaluable since they can account for realistic effects like turbulence and viscocity. The high computational costs associated with the optimization have been avoided by using an indirect optimization approach, which was used to dcouple the effect of the flow field variables on the gradients involved. The main challenge while performing the optimization was to maintain low sensitivity to the number of input design variables. This necessitated the use of Reverse Automatic differentiation tools to generate the gradient. All efforts have been made to keep computational costs to a minimum, thereby enabling hi-fidelity optimization to be used even in the initial design stages. A preliminary roadmap has been laid out for an initial implementation of optimization algorithms using the adjoint approach, into the high fidelity CFD code NASCART-GT.