Computationally Efficient Wave-Modeled Large Eddy Simulation of Finite Offshore Wind Farms

ORAL

Abstract

The increasing development of offshore wind, especially on the East Coast of the United States, requires computationally efficient simulation tools that can predict full-scale farm power production. A key coupling in offshore wind farms is the momentum transfer between the marine atmospheric boundary layer and ocean waves. A recently developed sea surface-based drag model is adopted within a Large Eddy Simulation framework to model this interaction combined with an actuator disk model for the wind turbines. This modeling framework is far less computationally intensive than wave phase-resolved approaches but more accurate than wave phase-averaged ones. Additionally, many proposed offshore wind farms on the East Coast are relatively small and are close to the shore where the boundary layer has not fully developed and a traditional streamwise periodic (effectively infinite) framework may not be accurate. The goal of this work is to extend the developed framework to the simulation of finite offshore wind farms to investigate the senstivity of velocity fields and power production to the incoming flow and to ask: do variations in incoming atmospheric turbulence or sea state have a larger influence on the wind farm?

*The authors gratefully acknowledge financial support from the Princeton University Andlinger Center for Energy and the Environment and High Meadows Environmental Institute. The simulations presented in this article were performed on computational resources supported by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology's High Performance Computing Center and Visualization Laboratory at Princeton University.

Presenters

  • Hannah H Williams

    • Princeton University

Authors

  • Hannah H Williams

    • Princeton University
  • Aditya Aiyer

    • Princeton
  • Luc Deike

    • Princeton University
    • Princeton
  • Michael E Mueller

    • Princeton University