foxes.models.wake_models.induction.rathmann¶

Classes¶

`Rathmann`	The Rathmann induction wake model

Module Contents¶

class foxes.models.wake_models.induction.rathmann.Rathmann(superposition: str = 'ws_linear', induction: str = 'Madsen', pre_rotor_only: bool = False)[source]¶

Bases: foxes.models.wake_models.turbine_induction_model.TurbineInductionModel

The Rathmann induction wake model

The individual wake effects are superposed linearly, without invoking a wake superposition model.

Notes¶

Reference: Forsting, Alexander R. Meyer, et al. “On the accuracy of predicting wind-farm blockage.” Renewable Energy (2023). https://www.sciencedirect.com/science/article/pii/S0960148123007620

Parameters:

superposition: The wind speed superposition
induction: The induction model
pre_rotor_only: Calculate only the pre-rotor region

contribute(algo: foxes.core.algorithm.Algorithm, mdata: foxes.core.data.MData, fdata: foxes.core.data.FData, tdata: foxes.core.data.TData, downwind_index: int, wake_coos: numpy.ndarray, wake_deltas: dict[str, numpy.ndarray]) → None[source]¶

Modifies wake deltas at target points by contributions from the specified wake source turbines.

Parameters:¶

algo: The calculation algorithm
mdata: The model data
fdata: The farm data
tdata: The target point data
downwind_index: The index of the wake causing turbine in the downwind order
wake_coos: The wake frame coordinates of the evaluation points, shape: (n_states, n_targets, n_tpoints, 3)
wake_deltas: The wake deltas. Key: variable name, value (n_states, n_targets, n_tpoints, …)

finalize(algo: foxes.core.algorithm.Algorithm, verbosity: int = 0) → None¶

Finalize the model.

Parameters:¶

algo: The calculation algorithm.
verbosity: The verbosity level; 0 is silent.

finalize_wake_deltas(algo: foxes.core.algorithm.Algorithm, mdata: foxes.core.data.MData, fdata: foxes.core.data.FData, tdata: foxes.core.data.TData, wake_deltas: dict[str, numpy.ndarray]) → None¶

Finalize the wake calculation.

Modifies wake_deltas on the fly.

Parameters:¶

algo: The calculation algorithm
mdata: The model data
fdata: The farm data
tdata: The target point data
wake_deltas: The wake deltas object at the selected target turbines. Keys are variable names and values are arrays with shape (n_states, n_targets, n_tpoints)

get_data(variable: str, target: str, lookup: str = 'smfp', mdata: foxes.core.data.MData | None = None, fdata: foxes.core.data.FData | None = None, tdata: foxes.core.data.TData | None = None, downwind_index: int | None = None, accept_none: False = False, accept_nan: bool = True, algo: foxes.core.algorithm.Algorithm | None = None, upcast: bool = False, selection: numpy.ndarray[Any, Any] | tuple[Any, ...] | list[Any] | None = None) → numpy.ndarray[Any, Any]¶

get_data(variable: str, target: str, lookup: str = 'smfp', mdata: foxes.core.data.MData | None = None, fdata: foxes.core.data.FData | None = None, tdata: foxes.core.data.TData | None = None, downwind_index: int | None = None, accept_none: True = True, accept_nan: bool = True, algo: foxes.core.algorithm.Algorithm | None = None, upcast: bool = False, selection: numpy.ndarray[Any, Any] | tuple[Any, ...] | list[Any] | None = None) → numpy.ndarray[Any, Any] | None

Getter for a data entry in the model object or provided data sources

Parameters:¶

variable: The variable name used as the data key.
target: The dimensions identifier for the output: FC.STATE_TURBINE, FC.STATE_TARGET, or FC.STATE_TARGET_TPOINT.
lookup: The order of data sources. Combination of: 's' for self, 'm' for mdata, 'f' for fdata, 't' for tdata, and 'w' for wake-modeling data.
mdata: The model data.
fdata: The farm data.
tdata: The target point data.
downwind_index: The index in the downwind order.
accept_none: Do not raise an error if the data entry is None.
accept_nan: Do not raise an error if the data entry is np.nan.
algo: The algorithm, needed for data from previous iterations.
upcast: Ensure the target dimensions are present. If expansion is needed, the result is a read-only broadcasted view.
selection: Apply this selection to the result, for state-turbine, state-target, or state-target-tpoint outputs.

initialize(algo: foxes.core.algorithm.Algorithm, loaded_data: foxes.core.model.LoadedData | None = None, force: bool = False, verbosity: int = 0) → foxes.core.model.LoadedData[source]¶

Initializes the model.

Parameters:¶

algo: The calculation algorithm
loaded_data: Data that has already been loaded, to be extended by this function. Keys are “coords”, a dict with entries dim_name_str -> dim_array; “data_vars”, a dict with entries name_str -> (dim_tuple, data_ndarray); and “extra_data”, a dict with non-array additional data.
force: Overwrite existing data
verbosity: The verbosity level, 0 = silent

Returns:¶

loaded_data: The loaded data, containing keys “coords”, “data_vars”, and “extra_data”. Keys are “coords”, a dict with entries dim_name_str -> dim_array; “data_vars”, a dict with entries name_str -> (dim_tuple, data_ndarray); and “extra_data”, a dict with non-array additional data.

load_data(algo: foxes.core.algorithm.Algorithm, loaded_data: LoadedData, force: bool = False, verbosity: int = 0) → None¶

Load and/or create all data required for model calculations.

The function adds to loaded_data.

Parameters:¶

algo: The calculation algorithm.
loaded_data: Data that has already been loaded, to be extended by this function. Keys are “coords”, a dict with entries dim_name_str -> dim_array; “data_vars”, a dict with entries name_str -> (dim_tuple, data_ndarray); and “extra_data”, a dict with non-array additional data.
force: Overwrite existing data.
verbosity: The verbosity level, where 0 is silent.

classmethod new(wmodel_type: str, *args: Any, **kwargs: Any) → TurbineInductionModel¶

Run-time turbine induction model factory.

Parameters:¶

wmodel_type: The selected derived class name
args: Additional parameters for constructor
kwargs: Additional parameters for constructor

new_wake_deltas(algo: foxes.core.algorithm.Algorithm, mdata: foxes.core.data.MData, fdata: foxes.core.data.FData, tdata: foxes.core.data.TData) → dict[str, numpy.ndarray][source]¶

Creates new empty wake delta arrays.

Parameters:¶

algo: The calculation algorithm
mdata: The model data
fdata: The farm data
tdata: The target point data

Returns:¶

wake_deltas: Key: variable name, value: The zero filled wake deltas, shape: (n_states, n_targets, n_tpoints, …)

set_running(algo: foxes.core.algorithm.Algorithm, data_stash: dict[str, dict[str, Any]] | None, sel: dict[str, Any] | None = None, isel: dict[str, Any] | None = None, verbosity: int = 0) → None¶

Set this model to the running state and move large data to the stash.

The stashed data is restored by unset_running after the calculation has finished.

Parameters:¶

algo: The calculation algorithm.
data_stash: The large-data stash. This function adds entries here when provided. Keys are model names and values are dictionaries of large model data.
sel: The subset selection dictionary.
isel: The index subset selection dictionary.
verbosity: The verbosity level; 0 is silent.

sub_models() → list[foxes.core.model.Model][source]¶

List of all sub-models

Returns:¶

smdls: All sub models

unset_running(algo: foxes.core.algorithm.Algorithm, data_stash: dict[str, dict[str, Any]] | None, sel: dict[str, Any] | None = None, isel: dict[str, Any] | None = None, verbosity: int = 0) → None¶

Set this model status to not running and recover large data from stash.

Parameters:¶

algo: The calculation algorithm.
data_stash: Reconstruct model data from this stash when provided. Keys are model names and values are dictionaries of large model data.
sel: The subset selection dictionary.
isel: The index subset selection dictionary.
verbosity: The verbosity level; 0 is silent.

unvar(vnm: str) → str | None¶

Translate a model-specific variable name to the original variable name.

Parameters:¶

vnm: The model-specific variable name.

Returns:¶

v: The original variable name.

var(v: str) → str¶

Create a model-specific variable name.

Parameters:¶

v: The variable name.

Returns:¶

vnm: The model-specific variable name.

waked_variables() → list[str][source]¶

Returns a list of variable names that are affected by this wake model.

Returns:¶

waked_variables: A list of variable names affected by this wake model.

property affects_downwind : bool¶

Flag for downwind or upwind effects on other turbines

Returns:¶

affects_downwind: Flag for downwind effects by this model

property affects_ws : bool¶

Flag for wind speed wake models

Returns:¶

dws: If True, this model affects wind speed

property has_uv : bool¶

This model uses wind vector data

Returns:¶

has_uv: Flag for wind vector data

property has_vector_wind_superp : bool¶

This model uses a wind vector superposition

Returns:¶

has_vector_wind_superp: Flag for wind vector superposition

induction = 'Madsen'¶

property initialized : bool¶

Initialization flag.

Returns:¶

initialized: True if the model has been initialized.

property model_id : int¶

Unique id based on the model type.

Returns:¶

int: Unique id of the model object

name = 'Model'¶

other_superpositions¶

pre_rotor_only = False¶

property running : bool¶

Flag for currently running models

Returns:¶

running: True if currently running

superp : dict[str, Any]¶

vec_superp : Any | None = None¶

wind_superposition = None¶