Wind Farm Power Prediction in Complex Terrain

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

An Immersed Boundary Geometric Preprocessor for Arbitrarily Complex Terrain and Geometry

There is a growing interest to apply the immersed boundary method to compute wind fields over arbitrarily complex terrain. The computer implementation of an immersed boundary module into an existing flow solver can be accomplished with minor modifications to the rest of the computer program. However, a versatile preprocessor is needed at the first place to extract the essential geometric information pertinent to the immersion of an arbitrarily complex terrain inside a 3D Cartesian mesh. Errors in the geometric information can negatively impact the correct implementation of the immersed boundary method as part of the solution algorithm. Additionally, the distance field from the terrain is needed to implement various subgrid-scale turbulence models and to initialize wind fields over complex terrain. Despite the popularity of the immersed boundary method, procedures used in the geometric preprocessing stage have received less attention. The present study found that concave and convex regions of complex terrain are particularly challenging to process with existing procedures discussed in the literature. To address this issue, a geometric preprocessor with a distance field solver was presented, and the solver demonstrated its versatility for arbitrarily complex geometry, terrain, and urban environments. The distance field solver uses the initial distance field at the immersed boundaries and propagates it to the rest of the domain by solving the Eikonal equation with the fast sweeping method

\u3ci\u3eDrosophila\u3c/i\u3e Muller F Elements Maintain a Distinct Set of Genomic Properties Over 40 Million Years of Evolution

The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25–50%) than euchromatic reference regions (3–11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11–27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4–3.6 vs. 8.4–8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu