.. _FF:FutureWork: 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 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 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 `__\ :math:`^\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).