Multiscale Modeling and Direct Statistical Simulation of Marine Atmospheric Boundary Layer Turbulence for Offshore Wind Energy

G. P. Chini, University of New Hampshire (Principal Investigator, Project Director)

N. Laxague, University of New Hampshire (Co-Investigator)

C. M. White, University of New Hampshire (Co-Investigator)

M. Wosnik, University of New Hampshire (Senior Personnel)

B. Fox-Kemper, Brown University (Co-Investigator)

J. B. Marston, Brown University (Co-Investigator)

J. Oishi, Bates College (Co-Investigator)

 

Over two-thirds of the U.S. wind energy resources---sufficient to provide over two times the installed generating capacity in the U.S.---are located in deep waters, prompting growing interest in the deployment of floating offshore wind farms. This Department of Energy (DOE) implementation project brings together a team of researchers from the University of New Hampshire (UNH), Bates College, and Brown University, representing the three coastal New England EPSCoR ("Established Program to Stimulate Competitive Research") jurisdictions, to address key aspects of the fluid mechanics of floating offshore wind farms. The ultimate objective of the project is to develop an innovative and forward-looking basic research capability for the computer simulation of turbulent air flow in and around floating offshore wind farms embedded within the marine atmospheric boundary layer---the approximately 500-1000 meter deep region of the atmosphere that is in direct contact with and directly influenced by the ocean. This new capability, which will offer unmatched computational efficiency for wind-farm-design-level accuracy, will be used to investigate fundamental turbulent flow phenomena in offshore wind farms.

A primary challenge in the design and optimization of wind farms is understanding and quantitatively predicting the interactions between preexisting turbulent atmospheric flows and turbulent wakes generated by wind turbines. This coupling, which significantly impacts wind-farm performance, occurs over distances ranging from millimeters to tens of kilometers and time periods from seconds to days, rendering computer simulations challenging even with the world's most powerful supercomputers. The prediction and optimization challenges are compounded for floating offshore wind farms, owing to turbulent exchanges of heat, moisture, and momentum between the atmosphere and ocean across a dynamic sea surface. Even with exascale computing, current algorithms used for predictive computer simulations capture only several minutes of physical time and only marginally resolve important wake/atmospheric-turbulence interactions. By developing new, systematically reduced mathematical formulations that exploit the anisotropic structure of wind-farm flows, our multiscale and direct statistical simulation approach avoids the complexity of multi-code coupling and specifically targets interactions between atmospheric turbulence and turbine wakes. The new models will be validated via comparison with traditional "large-eddy" simulations, experiments in the UNH Flow Physics Facility (FPF), and field measurements of air-sea fluxes and flow structure off the NH coast. The FPF, the world’s largest physics-quality, low-speed boundary layer wind tunnel, affords unprecedented opportunities for reliably quantifying turbine-wake/boundary-layer-turbulence interactions.

The validated, multiscale predictive capability developed in this project can be leveraged to maximize power production, limit hardware damage, and reduce the cost per megawatt-hour of floating offshore wind energy technology. Our new computational framework will fill an important gap in the modeling spectrum, accurately capturing a range of flow scales inaccessible to large-eddy simulations and unreliably represented by simplified analytical approaches. The multiscale and direct statistical simulation methods are particularly well-suited for the study of processes and systems phenomena across spatial and temporal scales, as also needed to address other DOE Energy Earthshots initiatives. By working together on this project in an intense and coordinated fashion, the research team thus will position themselves to investigate other pressing challenges in renewable energy and environmental sustainability. Finally, the states of New Hampshire, Maine, and Rhode Island will enjoy long-term benefits from this project, which will promote development of a workforce equipped with the technical skills to fill the growing needs in the energy sector and the blue economy.