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Updates log

PyWake 2.4.1 (July 7, 2022)

  • Fix a bug in WindFarmModel.aep that ignored the n_cpu, wd_chunks and ws_chunks arguments and always computed on only one CPU.

PyWake 2.4 (July 6, 2022)

New features and API changes

New models

  • RotorAvgModels

    • New WSPowerRotorAvg, which computes the rotor average deficit by, \(deficit = WS - \sqrt[\alpha]{\frac{1}{N} \sum_{i}{\left(WS - deficit_i\right)}^\alpha}\). Note that WS is the rotor center wind speed and thus shear and terrain-dependent inflow variation are not taken into account when computing the rotor average deficit.

  • Power/Ct functions

    • New DensityCompensation which scales the wind speed wrt. air density. In most cases this model is more realistic than the existing alternative model, DensityScale, which scales the power and ct wrt. air density.

PyWake 2.3 (March 18, 2022)

New features and API changes

  • GroundModel is now an input to DeficitModel instead of WindFarmModel. This means that a ground model can be applied to the blockage or wake, only.

  • PyWake can now compute gradients via finite differnece, complex step and automatic differentiation, see https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/gradients_parallelization.html#Gradients. Most models supports all three methods, while a few do not work yet.

  • Flow maps can be computed in both the vertical downwind and crosswind plane.

New models

  • WakeDeficitModels

    • CarbajofuertesGaussianDeficit

    • TurboNOJDeficit

    • TurboGaussianDeficit

  • BlockageDeficitModels

    • RathmannScaled

  • DeflectionModels

    • GCLHillDeflection

    • JimenezWakeDeflection (extended with vertical deflection due to rotor tilt)

  • WeightModels (to be used with the STF2005 and STF2017 TurbulenceModels)

    • FrandsenWeight (the previous implementation)

    • IECWeight (weight as specified in the IEC standard)

  • SiteModels

    • GlobalWindAtlasSite (site with data from online global wind atlas)

    • DistanceModels

      • JITStreamlineDistance (compute distances between wind turbines along streamlines)

    • ShearModels

      • LogShear

PyWake 2.2 (March 26, 2021)

New features and API changes

  • All DeficitModels should inherit either WakeDeficitModel or BlockageDeficitModel.

  • All Sites are now subclasses of XRSite.

  • WeightedSum SuperpositionModel reimplemented to be more efficient.

  • TurbulenceModels now take a RotorAvgModel as optional input. This allows PyWake to use different RotorAvgModels for wake and turbulence.

  • Validation feature updated, see here.

  • The Power/Ct curve functionality of WindTurbines has been updated to support multidimensional Power and Ct curves, e.g. curves depending on turbulence intensity, air density, yaw misalignment, operational mode etc. This means that instantiating WindTurbines and OneTypeWindTurbines with the old set of arguments, i.e. name, diameter, hub_height, ct_func, power_func, power_unit, is deprecated. Use the the new WindTurbine and Windturbines classes with the arguments name, diameter, hub_height, powerCtFunction instead, see here. Backward compatibility is ensured (with runtime warning) for most use cases. The powerCtFunction can be one of the classes from py_wake.wind_turbines.power_ct_functions, i.e.

    • PowerCtFunction

    • PowerCtTabular

    • PowerCtFunctionList

    • PowerCtNDTabular

    • PowerCtXr

    • CubePowerSimpleCt

  • Support for time series of wd and ws, see here. Possible use cases:

    • Time-dependent inflow, e.g. measurements of wd, ws, ti, shear, density, etc.

    • Time-dependent operation, e.g. periods of failure or maintaince of a wind turbine

  • Added support for load surrogates to predict wind turbine loads.

New models

  • BlockageDeficitModels (see here)

    • SelfSimilarityDeficit2020

    • HybridInduction

    • RankineHalfBody

    • VortexCylinder

    • VortexDipole

    • Rathmann

  • DeflectionModels

    • FugaDeflection (requires Fuga look-up tables, UL, UT, VL, VT)

  • GroundModels

    • Mirror

    • MirrorSquaredSum

PyWake 2.1 (September 14, 2020)

New features and API changes

  • New xarray data structure

    • LocalWind, SimulationResult and FlowMap are now xarray.Dataset-objects with some additional methods and attributes.

    • simulationResult.aep() now returns a xarray.DataArray with aep for all wind turbines, wind directions and wind speeds. To get the total AEP as before, use simulationResult.aep().sum().

    • New general XRSite where the site is defined as an xarray with the following structure:

      • Required data variables:

        • P(probability) or f(sector frequency), A(Weibull scale), k(Weibull shape)

      • Optional data variables:

        • WS(defaults to reference wind speed, ws), TI(turbulence intensity), SpeedUp, Turning

      • All data variables may be constant or dependent on any of:

        • ws (reference wind speed)

        • wd (reference wind direction)

        • position in terms of

          • gridded 2D position, (x,y)

          • gridded 3D position, (x,y,z)

          • wt position, (i)

  • Include effects of neighbouring wind farms in site (wind resource) to speed up optimization of a wind farm with neighbouring farms. image-2.png

  • Vertical flow map via the YZGrid. image.png

New models

  • New RotorAverageModel, see here. The default model, RotorCenter, behaves as before as it estimates the rotor-average wind speed from the wind speed at the rotor center. Other models, however, provide a more accurate estimate based on multiple points on the cost of computation. The CGIRotorAvg(4) and CGIRotorAvg(7) with 4 and 7 points, respectively, provide good compromises between accuracy and computational cost.

  • Deficit model:

    • GCLDeficit: The Gunner Larsen semi-analytical wake model.

  • Superposition model:

    • WeightedSum A weighted sum approach taking wake convection velocity into account. The model is so far only applicable to the gaussian models. The model is based on “A momentum-conserving wake superposition method for wind farm power prediction” by Haohua Zong and Fernando Porté-Agel, J. Fluid Mech. (2020), vol. 889, A8; doi:10.1017/jfm.2020.77.

PyWake 2.0 (April 15, 2020)

  • New structure

    • Purpose:

      • Easier combination of different models for flow propagation, wake and blockage deficit, superposition, wake deflection and turbulence.

      • More consistent interface to and support for engineering models and PyWake-Rans.

    • Changes

      • WakeModel class refactored mainly into the WindFarmModels EngineeringWindFarmModel and PropagateDownwind.

      • WindFarmModels, e.g. NOJ, Fuga, BastankhahGaussian returns a SimulationResult containing the results as well as an AEP and a flow_map method. See the QuickStart tutorial,

      • and many more.

    • Backward compatibility

      • AEP Calculator works as before, but is now deprecated.

      • Lower level interfaces and implementations has changed.

  • New documentation matching the new structure.

  • Optional blockage deficit models and implementation of the SelfSimilarity model.

  • Optional wake deflection models and implementation of a model by Jimenez.