Publication Date


Date of Final Oral Examination (Defense)


Type of Culminating Activity


Degree Title

Master of Science in Mechanical Engineering


Mechanical and Biomechanical Engineering

Major Advisor

Inanc Senocak, Ph.D.


Ralph S. Budwig, Ph.D.


Trevor Lujan, Ph.D.


Clare Fitzpatrick, Ph.D.


There has been increasing interest in predicting the velocity field within wind farms in complex terrain for resource assessment, turbine siting, and power forecasting. These capabilities are made possible by advancements in computational speed from a new generation of computing hardware and numerical methods. The current thesis research focuses on two technical components to advance the current state in wind power forecasting. The first component is improved prediction of wind flow over complex terrain using the versatile immersed boundary method to represent surface boundary conditions on a fixed Cartesian mesh. The proposed approach embodies the law-of-the-wall for rough surfaces and produces good results for benchmark wind data for complex terrain. The second component is the implementation and validation of wind turbine wake models and a first-principle based method to predict available wind power. Actuator disk models with and without rotation are considered. The wake models are validated against data from a published wind tunnel experiment and full-scale field data from an operational wind farm. The power prediction method is compared against normalized power data from operational wind farms and other computational studies available in literature. The actuator disk model with rotation simulates wake velocity deficits with better accuracy than the non-rotational model. The proposed power prediction method shows agreement with standard energy assessment methods without any ad-hoc decisions. Finally, the computational capability is applied to a hypothetical wind farm in Southern Idaho to demonstrate its versatility.