foxes.models.wake_models.wind.bastankhah16¶

Classes¶

`Bastankhah2016`	The Bastankhah 2016 wake model
`Bastankhah2016Model`	Common calculations for the wake model and the wake

Module Contents¶

class foxes.models.wake_models.wind.bastankhah16.Bastankhah2016(superposition: str, alpha: float = 0.58, beta: float = 0.077, induction: str = 'Madsen', **wake_k: Any)[source]¶

Bases: foxes.models.wake_models.dist_sliced.DistSlicedWakeModel

The Bastankhah 2016 wake model

Notes¶

Reference: “Experimental and theoretical study of wind turbine wakes in yawed conditions” Majid Bastankhah, Fernando Porté-Agel https://doi.org/10.1017/jfm.2016.595

Parameters:

superposition: The wind deficit superposition
ct_max: The maximal value for ct, values beyond will be limited to this number, by default 0.9999
alpha: model parameter used to determine onset of far wake region
beta: model parameter used to determine onset of far wake region
induction: The induction model
wake_k: Parameters for the WakeK class

calc_wakes_x_yz(algo: foxes.core.algorithm.Algorithm, mdata: foxes.core.data.MData, fdata: foxes.core.data.FData, tdata: foxes.core.data.TData, downwind_index: int, x: numpy.ndarray, yz: numpy.ndarray) → tuple[dict[str, numpy.ndarray], numpy.ndarray][source]¶

Calculate wake deltas.

Parameters:¶

algo: The calculation algorithm
mdata: The model data
fdata: The farm data
tdata: The target point data
downwind_index: The index in the downwind order
x: The x values, shape: (n_states, n_targets)
yz: The yz values for each x value, shape: (n_states, n_targets, n_yz_per_target, 2)

Returns:¶

wdeltas: The wake deltas. Key: variable name str, value
st_sel: The state-target selection, for which the wake is non-zero, shape: (n_states, n_targets)

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¶

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) → WakeModel¶

Run-time wake 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]¶

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][source]¶

List of all sub-models

Returns:¶

smdls: Names of 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

alpha = 0.58¶

beta = 0.077¶

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.

model : Bastankhah2016Model | None = None¶

property model_id : int¶

Unique id based on the model type.

Returns:¶

int: Unique id of the model object

name = 'Model'¶

other_superpositions¶

property running : bool¶

Flag for currently running models

Returns:¶

running: True if currently running

superp : dict[str, Any]¶

vec_superp : Any | None = None¶

wake_k¶

wind_superposition = None¶

class foxes.models.wake_models.wind.bastankhah16.Bastankhah2016Model(alpha: float, beta: float, induction: str)[source]¶

Bases: foxes.core.Model

Common calculations for the wake model and the wake frame, such that code repetitions can be avoided.

Notes¶

Reference: “Experimental and theoretical study of wind turbine wakes in yawed conditions” Majid Bastankhah, Fernando Porté-Agel https://doi.org/10.1017/jfm.2016.595

Parameters:

alpha: model parameter used to determine onset of far wake region
beta: model parameter used to determine onset of far wake region
induction: The induction model

calc_data(algo: foxes.core.algorithm.Algorithm, mdata: foxes.core.data.MData, fdata: foxes.core.data.FData, tdata: foxes.core.data.TData, downwind_index: int, x: numpy.ndarray, gamma: numpy.ndarray, k: numpy.ndarray) → None[source]¶

Calculate common model data, store it in mdata.

Parameters:¶

algo: The calculation algorithm
mdata: The model data
fdata: The farm data
tdata: The target point data
downwind_index: The index in the downwind order
x: The x values, shape: (n_states, n_targets)
gamma: The YAWM angles in radiants, shape: (n_states, n_targets)
k: The k parameter values, shape: (n_states, n_targets)

clean(mdata: foxes.core.data.MData) → None[source]¶: Clean all data

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.

get_cached_data(key: str, mdata: foxes.core.data.MData) → Any[source]¶

Return data entry

Parameters:¶

key: The data key
mdata: The model data

Returns:¶

data: The data

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.

has_data(mdata: foxes.core.data.MData, downwind_index: int, x: numpy.ndarray) → bool[source]¶

Check if data exists

Parameters:¶

mdata: The model data
downwind_index: For each state, one turbine index for the wake causing turbine. Shape: (n_states,)
x: The x values, shape: (n_states, n_points)

Returns:¶

check: True if data exists

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.

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][source]¶

List of all sub-models

Returns:¶

smdls: All sub models

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.

AMPL_FAR = 'ampl_far'¶

AMPL_NEAR = 'ampl_near'¶

CHECK = 'check'¶

DELTA_FAR = 'delta_far'¶

DELTA_NEAR = 'delta_near'¶

MDATA_KEY = 'Bastankhah2016Model'¶

NEAR = 'near'¶

PARS = 'pars'¶

R_PC = 'r_pc'¶

R_PC_S = 'r_pc_s'¶

SIGMA_Y_FAR = 'sigma_y_far'¶

SIGMA_Z_FAR = 'sigma_z_far'¶

ST_SEL = 'st_sel'¶

X0 = 'x0'¶

induction¶

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'¶

property pars : dict[str, float | str]¶

Dictionary of the model parameters

Returns:¶

dict: Dictionary of the model parameters

property running : bool¶

Flag for currently running models

Returns:¶

running: True if currently running