{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Updates log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyWake 2.5 (February 15, 2023)\n",
    "\n",
    "### New Features and API changes\n",
    "- PyWake conda package available. Install by `conda install -c https://conda.windenergy.dtu.dk/channel/open py_wake`\n",
    "- Before `RotorAvgModel` was an input to the WindFarmModel. This is ambigious as the rotor average models may be applied to both wake deficit, blockage deficit and turbulence. Instead the `RotorAvgModel` is now an input option to `WakeDeficitModel`, `BlockageDeficitModel` and `TurbulenceModel`\n",
    "- Before the an area overlapping rotor average model was integrated into the `NOJDeficit` Model. These models have now been separated. The default behaviour is unchanged as the default rotor average model of `NOJDeficit` is set to `AreaOverlapAvgModel`\n",
    "- The `IEA37SimpleBastankhahGaussian` wind farm model is deprecated. Please use the `IEA37CaseStudy1` model from py_wake.literature.iea37_case_study1 instead\n",
    "- Notebook with verification of the TurbOPark model from Ørsted\n",
    "- New netcdf-based Fuga look-up table format. The function `dat2netcdf` py_wake.utils.fuga_utils can be used to convert files from the old deprecated format to the new format.\n",
    "- Wind turbine positions may now depend on wind direction and wind speed (e.g. floating wind turbines or multirotors)\n",
    "- Before `All2AllIterative` took an input `initialize_with_PropagateDownwind` which defaulted to True to decide whether the effective wind speed in `All2AllIterative` should start with the free stream value or the effective wind speed computed by `PropagateDownwind` (i.e. without blockage). This input has been replaced with the optional input argument `WS_eff`. If `WS_eff=None`(default) then the initial effective wind speed is obtained from `PropagateDownwind`. Alternatively, the initial effective wind speed can be set to the free wind by `WS_eff=0` or directly to a custom value by `WS_eff=effective wind speed`. Note, however, that bypassing some iterations by setting the \"correct\" effective wind speed may result in wrong gradients\n",
    "- `All2AllIterative` will now return after first iteration if `convergence_tolerance` is set to `None`. This only makes sense if CT and the deficit are independent of the effective wind speed, like the IEA37CaseStudy1 setup.\n",
    "- The default behaviour of `StraightDistance` is now to use the reference wind direction\n",
    "- New method to avoid deficit and turbulence from wind turbines on themselves. This allows flow maps without discontinuties at the wind turbines\n",
    "- New `InputModifierModel` type that capable of modifying inputs before or during the simulations. This enables simulation of multirotor and floating wind turbines\n",
    "\n",
    "\n",
    "### New models and functions\n",
    "- DeficitModels\n",
    "  - FugaMultiLUTDeficit, which allows different wind turbine types\n",
    "  - XRDeficitModel for deficit models based on xarray.dataarray look-up table (with linear interpolation)\n",
    "- RotorAvgModels\n",
    "  - GaussianOverlap. The model is based on a lookup table with numerically integrated overlap factors based on normalized input of downstream rotor diamter and crosswind distance.\n",
    "- TurbulenceModels\n",
    "  - XRTurbulenceModel for turbulence models based on xarray.dataarray look-up table (with linear interpolation)\n",
    "- Predefined WindFarmModels\n",
    "  - TurbOPark. A setup very similar to the original Ørsted implementation\n",
    "- InputModifierModels\n",
    "  - MultiRotor. Model to change the position of the rotors on a multirotor wind turbine depending on the wind direction\n",
    "- ISONoiseModel. Simple noise propagation model, see https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/Noise.html\n",
    "- InputModifierModel. New model type that allows to modify inputs before or during the simulations. This allows multirotor \n",
    "- Functions\n",
    "  - New `circular` method in py_wake.utils.layouts to generate circular layouts\n",
    "  \n",
    "\n",
    "\n",
    "### Bug fixes\n",
    "- Fix a bug  in `WindFarmModel.aep` that ignored the `n_cpu`, `wd_chunks` and `ws_chunks` arguments and always computed on only one CPU.\n",
    "- Fix `NOJLocalDeficit`. Before a layout term was precalculated, but in the local version this term depends on the effective TI which was unknow at this stage\n",
    "- Fix error `ModuleNotFoundError: No module named 'xarray.plot.plot'` occurring with newer version of xarray\n",
    "- Fix parallel executino with FugaDeficit\n",
    "- and many more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyWake 2.4 (July 6, 2022)\n",
    "### New features and API changes\n",
    "- Before, the `Mirror` ground model used linear superposition of the above- and below-ground wind turbines while `MirrorSquaredSum` used squared sum. In this version the `MirrorSquaredSum` has been removed and `Mirror` is now using the superposition model of the wind farm model to calculate the sum. I.e. `Mirror` behaves as before if the superposition model is `LinearSum` and as the previuos `MirrorSquaredSum` if the superposition model is `SquaredSum`.\n",
    "- Easy chunkification and parallelization via the arguments `n_cpu`, `wd_chunks` and `ws_chunks`, see https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/RunWindFarmSimulation.html#Chunkification-and-parallelization and https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/Optimization.html#Chunkify-and-Parallelization.\n",
    "- Change dAEPdxy to automatically compute gradients of aep wrt. the concatenated list of x,y which is faster than computing first wrt. x then y.\n",
    "- `py_wake.utils.layouts` contains functions to create rectangular and square wind turbine layouts.\n",
    "- New approach to switch numpy backend (used when switching to `autograd.numpy`, `Numpy32` (see below, etc.). The new approach requires all modules to import np from py_wake, i.e. `from py_wake import np`.\n",
    "- Easy way to switch between double presicion (standard numpy) and single precision (`Numpy32`), see https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/gradients_parallellization.html#Precision.\n",
    "- New function `floris_yaml_to_pywake_turbine` (see https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake/-/blob/master/py_wake/utils/floris_wrapper.py). This function creates a PyWake WindTurbine object from a Floris wind turbine yaml file and allows more direct comparison.\n",
    "- Previuosly, `LocalWind` (returned by `site.localWind`) was an xarray `Dataset` subclass. Due to issues with autograd and cupy, this has been changed such that `LocalWind` is now a `dict` subclass with numpy arrays, `{'WS_ilk': np.array([...])}`. Xarray DataArrays are created by `LocalWind` when requesting attributes without `_ilk`, e.g. `localWind.WS`.\n",
    "- Long list of bug and issue fixes.\n",
    "\n",
    "### New models\n",
    "- RotorAvgModels\n",
    "  - 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.\n",
    "- Power/Ct functions\n",
    "  - 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyWake 2.3 (March 18, 2022)\n",
    "### New features and API changes\n",
    "- `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.\n",
    "- 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.\n",
    "- Flow maps can be computed in both the vertical downwind and crosswind plane.\n",
    "\n",
    "\n",
    "### New models\n",
    "- WakeDeficitModels\n",
    "  - CarbajofuertesGaussianDeficit \n",
    "  - TurboNOJDeficit \n",
    "  - TurboGaussianDeficit\n",
    "- BlockageDeficitModels\n",
    "  - RathmannScaled\n",
    "- DeflectionModels\n",
    "  - GCLHillDeflection \n",
    "  - JimenezWakeDeflection (extended with vertical deflection due to rotor tilt)\n",
    "- WeightModels (to be used with the STF2005 and STF2017 TurbulenceModels)\n",
    "  - FrandsenWeight (the previous implementation)\n",
    "  - IECWeight (weight as specified in the IEC standard)  \n",
    "- SiteModels\n",
    "    - GlobalWindAtlasSite (site with data from online global wind atlas)\n",
    "    - DistanceModels\n",
    "      - JITStreamlineDistance (compute distances between wind turbines along streamlines)\n",
    "    - ShearModels\n",
    "      - LogShear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyWake 2.2 (March 26, 2021)\n",
    "### New features and API changes\n",
    "- All DeficitModels should inherit either `WakeDeficitModel` or `BlockageDeficitModel`.\n",
    "- All Sites are now subclasses of XRSite.\n",
    "- WeightedSum SuperpositionModel reimplemented to be more efficient.\n",
    "- TurbulenceModels now take a RotorAvgModel as optional input. This allows PyWake to use different RotorAvgModels for wake and turbulence.\n",
    "- Validation feature updated, see [here](https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/exercises/Validation.html).\n",
    "- 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](https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/WindTurbines.html). Backward compatibility is ensured (with runtime warning) for most use cases.\n",
    "The `powerCtFunction` can be one of the classes from py_wake.wind_turbines.power_ct_functions, i.e.\n",
    "    - `PowerCtFunction`\n",
    "    - `PowerCtTabular`\n",
    "    - `PowerCtFunctionList`\n",
    "    - `PowerCtNDTabular`\n",
    "    - `PowerCtXr`\n",
    "    - `CubePowerSimpleCt`\n",
    "- Support for time series of wd and ws, see [here](https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/RunWindFarmSimulation.html#Time-series). Possible use cases:\n",
    "    - Time-dependent inflow, e.g. measurements of wd, ws, ti, shear, density, etc.\n",
    "    - Time-dependent operation, e.g. periods of failure or maintaince of a wind turbine\n",
    "- Added support for load surrogates to predict wind turbine loads.\n",
    "\n",
    "### New models\n",
    "- BlockageDeficitModels (see [here](https://topfarm.pages.windenergy.dtu.dk/PyWake/notebooks/EngineeringWindFarmModels.html#Blockage-deficit-models))\n",
    "    - SelfSimilarityDeficit2020\n",
    "    - HybridInduction\n",
    "    - RankineHalfBody\n",
    "    - VortexCylinder\n",
    "    - VortexDipole\n",
    "    - Rathmann\n",
    "- DeflectionModels\n",
    "    - FugaDeflection (requires Fuga look-up tables, `UL`, `UT`, `VL`, `VT`)\n",
    "- GroundModels\n",
    "    - Mirror\n",
    "    - MirrorSquaredSum"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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"
    },
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyWake 2.1 (September 14, 2020)\n",
    "\n",
    "\n",
    "### New features and API changes\n",
    "- New xarray data structure\n",
    "    - LocalWind, SimulationResult and FlowMap are now `xarray.Dataset`-objects with some additional methods and attributes.\n",
    "    - `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()`.\n",
    "    - New general XRSite where the site is defined as an xarray with the following structure:\n",
    "        - Required data variables: \n",
    "            - P(probability) or f(sector frequency), A(Weibull scale), k(Weibull shape)\n",
    "        - Optional data variables: \n",
    "            - WS(defaults to reference wind speed, ws), TI(turbulence intensity), SpeedUp, Turning\n",
    "        - All data variables may be constant or dependent on any of:\n",
    "            - ws (reference wind speed)\n",
    "            - wd (reference wind direction)\n",
    "            - position in terms of\n",
    "                - gridded 2D position, (x,y)\n",
    "                - gridded 3D position, (x,y,z)\n",
    "                - wt position, (i)\n",
    "- [Include effects of neighbouring wind farms](Site.ipynb#wake-effects-from-neighbouring-wind-farms) in site (wind resource) to speed up optimization of a wind farm with neighbouring farms.\n",
    "![image-2.png](attachment:image-2.png)\n",
    "- Vertical flow map via the [YZGrid](RunWindFarmSimulation.ipynb#YZGrid).\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "### New models\n",
    "\n",
    "- New `RotorAverageModel`, see [here](EngineeringWindFarmModels.ipynb#Rotor-average-models). 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.\n",
    "- Deficit model: \n",
    "    - [GCLDeficit](EngineeringWindFarmModels.ipynb#GCLDeficit): The Gunner Larsen semi-analytical wake model.\n",
    "- Superposition model:\n",
    "    - 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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyWake 2.0 (April 15, 2020)\n",
    "\n",
    "- New structure\n",
    "    - Purpose:\n",
    "        - Easier combination of different models for flow propagation, wake and blockage deficit, superposition, wake deflection and turbulence.\n",
    "        - More consistent interface to and support for engineering models and PyWake-Rans.\n",
    "    - Changes\n",
    "        - `WakeModel` class refactored mainly into the `WindFarmModel`s `EngineeringWindFarmModel` and `PropagateDownwind`.\n",
    "        - `WindFarmModel`s, 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,\n",
    "        - and many more.\n",
    "    - Backward compatibility\n",
    "        - AEP Calculator works as before, but is now deprecated.\n",
    "        - Lower level interfaces and implementations has changed.\n",
    "- New documentation matching the new structure.\n",
    "- Optional blockage deficit models and implementation of the SelfSimilarity model.\n",
    "- Optional wake deflection models and implementation of a model by Jimenez."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}