4.18.8. Future Work

This list contains features that could be implemented in future releases:

• Develop more efficient methods of generating/processing ambient wind from a high-fidelity precursor simulation, including:

  • Propagate 2D planes of ambient wind data using Taylor’s frozen turbulence hypothesis as an alternative to 3D volumes

  • Allow for nonuniform grids in Turbsim

  • Use Dynamic Mode Decomposition to compress the file size of the low-resolution domains

  • Implement Gabor mode enrichment to replace the high-resolution domains

  • Develop a more efficient ABLSolver based on a simple rectangular (rather than a generally unstructured) grid.

• Improve the eddy-viscosity formulation with additional physics.

• Pursue additional wake-modeling approaches, including:

  • Introduce simpler wake-deficit models, e.g., the Gaussian wake model by Bastankhah and Porté-Agel and the super-Gaussian model by Blondel and Cathelain

  • Introduce simpler wake-deflection models, e.g., the model by Jiménez or the model by Qian and Ishihara

  • Apply a free-vortex method for the near wake

  • Incorporate a kidney-shaped wake under skewed-flow conditions, e.g., by incorporating opposing vortices from the skew-induced horseshoe vortex

  • Deform the base-wake deficit (introduce asymmetry) as a result of background turbulence (in addition to wake meandering)

  • Incorporate wake-added turbulence

  • Improve the treatment of complex terrain (beyond specifying ambient wind data as NaN in VTK format)

  • Include wakes from the nacelle and support structure

  • Reflect wakes off of the ground.

• Address deep-array effects for large wind farms and account for flow speedup around the edges of the wind farm – i.e., account for the wind-farm blockage effect – e.g., by mimicking the wind farm-induced boundary layer with surface roughness in the LES ambient wind precursor.

• Implement a model to mimic the measurements taken from a LIDAR and other remote sensing technologies.

• Incorporate MPI to support the modeling of large wind farms by taking advantage of memory parallelization and parallelization between nodes of an HPC.

• Allow for a more general module form, e.g.:

  • Support continuous states

  • Support direct feedthrough of input to output

  • Support full-system linearization.

• Support an interface to Simulink for super and individual wind turbine controllers.

• Implement checkpoint-restart capability.

• Enable binary wind data input and output formats and binary time-series results output format.

• Add ability to output disturbed wind in VTK format on 2D slices that need not be parallel to the X-Y, Y-Z and/or X-Z planes of the global inertial-frame coordinate system.

• Rename the ambient wind data input files in VTK format following the naming convention used for the FAST.Farm-generated visualization output files in VTK format (with leading zeros and without the t).

• Support super controller-, inflow-, and wake-related output channels for more than the first 9 wind turbines in the wind farm.

• Interface FAST.Farm to the Wind-Plant Integrated System Design & Engineering Model (WISDEM\(^\text{TM}\)) for systems-engineering applications (multidisciplinary design, analysis, and optimization; uncertainty quantification; and so on).

• Develop a wrapper for stand-alone AeroDyn – the aerodynamics module of OpenFAST (or an equivalent BEM tool) – as an alternative to OpenFAST to support advanced performance-only wind-farm analysis that is much more computationally efficient than FAST.Farm analysis using OpenFAST.

• Address unique offshore wind energy challenges, e.g.:

  • Ensure consistent waves across an offshore wind farm

  • Support the air-water interface

  • Consider shared mooring and anchoring arrangements (for floating offshore wind farms).

• Adopt the capability to support undersea marine turbine arrays (which may require supporting direct feedthrough of input to output to handle the added-mass effects).