Geometry API: Turbine geometry processor

Module containing the processing functions for the geometry data.

Bases: object

Class to process the geometry data of a single OWT.

Parameters:

api – API object used to call get_* methods.
materials – Pandas dataframe with the materials data.
sub_assemblies – Dictionary of the subassemblies.
tw_sub_assemblies – Pandas dataframe with the tower subassemblies data for a given turbine.
tp_sub_assemblies – Pandas dataframe with the transition piece subassemblies data for a given turbine.
mp_sub_assemblies – Pandas dataframe with the monopile subassemblies data for a given turbine.
tower_base – Elevation of the OWT tower base in mLAT.
pile_head – Elevation of the pile head in mLAT.
water_depth – Water depth in mLAT.
pile_toe – Elevation of the pile toe in mLAT.
rna – Pandas dataframe with the RNA data.
tower – Pandas dataframe with the tower data.
transition_piece – Pandas dataframe with the transition piece data.
monopile – Pandas dataframe with the monopile data.
tw_lumped_mass – Pandas dataframe with the lumped masses data for the tower.
tp_lumped_mass – Pandas dataframe with the lumped masses data for the transition piece.
mp_lumped_mass – Pandas dataframe with the lumped masses data for the monopile.
tp_distributed_mass – Pandas dataframe with the distributed masses data for the transition piece.
mp_distributed_mass – Pandas dataframe with the distributed masses data for the monopile.
grout – Pandas dataframe with the grout data.
full_structure – Pandas dataframe with the full structure data.
tp_skirt – Pandas dataframe with the transition piece skirt data.
substructure – Pandas dataframe with the substructure data.

sub_assemblies: Dict[str, SubAssembly]

pile_toe: float64 | None

rna: DataFrame | None

tower: DataFrame | None

transition_piece: DataFrame | None

monopile: DataFrame | None

tw_lumped_mass: DataFrame | None

tp_lumped_mass: DataFrame | None

mp_lumped_mass: DataFrame | None

tp_distributed_mass: DataFrame | None

mp_distributed_mass: DataFrame | None

grout: DataFrame | None

full_structure: DataFrame | None

tp_skirt: DataFrame | None

substructure: DataFrame | None

Create an instance of the OWT class with the required parameters.

Parameters:

api – API object used to call get_* methods.
materials – Pandas dataframe with the materials data.
subassemblies – Pandas dataframe with the subassemblies data for a given turbine.
location – Pandas dataframe with the location data for a given turbine.
tower_base – Optional: elevation of the OWT tower base in mLAT.
pile_head – Optional: elevation of the pile head in mLAT.

Returns:

None

api: Any

materials: DataFrame

tw_sub_assemblies: DataFrame | None

tp_sub_assemblies: DataFrame | None

mp_sub_assemblies: DataFrame | None

water_depth: float64

tower_base: float64 | None

pile_head: float64 | None

set_df_structure(idx: str) → DataFrame

Calculate and/or converts geometrical data of subassemblies from the database.

Parameters:: idx – Possible index to identify corresponding subassembly.
Returns:: Data frame containing geometry data from database wth z in mLAT system.

process_structure_geometry(idx: str) → DataFrame

Calculate and/or converts geometrical data of subassemblies from the database to use as input for FE models.

Parameters:: idx – Possible index to identify corresponding subassembly.
Returns:: Dataframe consisting of the required data to build FE models.

process_rna() → None

Set dataframe containing the required properties to model the RNA system.

Returns:: None

set_df_appurtenances(idx: str) → DataFrame

Set dataframe containing the required properties to model concentrated masses from database subassemblies.

Parameters:: idx – Index to identify corresponding subassembly with possible values: ‘TW’, ‘TP’, ‘MP’.
Returns:: Data frame containing lumped masses data from database with Z coordinates in mLAT system.

process_lumped_masses(idx: str) → DataFrame

Create dataframe containing the required properties to model lumped mass appurtenances. Note that if the preprocessor package does not find any appurtenances it’ll return an empty dataframe.

Parameters:: idx – Index to identify corresponding subassembly with possible values: ‘TW’, ‘TP’, ‘MP’.
Returns:: Dataframe.

set_df_distributed_appurtenances(idx: str) → DataFrame

Set dataframe containing the required properties to model distributed lumped masses from database.

Parameters:: idx – Index to identify corresponding subassembly with possible values: ‘TW’, ‘TP’, ‘MP’.
Returns:: Dataframe containing distributed lumped masses data from database. Z coordinates in mLAT system.

process_distributed_lumped_masses(idx: str) → DataFrame

Create dataframe containing the required properties to model uniformly distributed appurtenances. Note that if the preprocessor package does not find any appurtenances it’ll return an empty dataframe.

Parameters:: idx – Index to identify corresponding subassembly with possible values: ‘TP’, ‘MP’.
Returns:: Dataframe.

process_structure(option='full') → None

Set dataframe containing the required properties to model the tower geometry, including the RNA system.

Parameters:

option –

Option to process the data for a specific subassembly. Possible values:

”full”: To process all the data for all subassemblies.
”tower”: To process only the data for the tower subassembly.
”TP”: To process only the data for the transition piece subassembly.
”monopile”: To process only the data for the monopile foundation subassembly.

Returns:

None

static can_adjust_properties(row: Series) → Series

Recalculation of can properties based on section properties and can elevations: height [m], volume [m3], mass [t], rho [t/m].

Parameters:: row – Original can properties.
Returns:: Pandas series of recalculated can properties.

can_modification(df: DataFrame, altitude: float64 | None, position: str = 'bottom') → DataFrame

Change can properties based on the altitude.

Parameters:

df – Dataframe containing the can properties.
altitude – Altitude in mLAT.
position – Position of the can with respect to the altitude with possible values: “bottom” or “top”.

Returns:

Dataframe with the modified can properties.

assembly_tp_mp() → None

Process TP structural item to assembly with MP foundation ensuring continuity. TP skirt is processed as well.

Returns:: None

assembly_full_structure() → None

Process the full structure of the OWT: tower + tp combiantion with monopile.

Returns:: None

extend_dfs() → None

Extend the dataframes with the subassembly columns.

Returns:: None

transform_monopile_geometry(cutoff_point: floating = nan) → DataFrame

Returns a dataframe with the monopile geometry with the mudline as reference

Parameters:: cutoff_point – Depth from the mudline to cut the monopile geometry.
Returns:: Dataframe with the monopile geometry.

class owimetadatabase_preprocessor.geometry.processing.OWTs(turbines: List[str], owts: List[OWT])

Bases: object

Class to process the geometry data of multiple OWTs.

Parameters:

owts – List of OWT objects.
api – API object used to call get_* methods.
materials – Pandas dataframe with the materials data.
sub_assemblies – Dictionary of dictionaries of the subassemblies for each turbine.
tower_base – Dictionary of the elevation of the OWT tower base in mLAT for each turbine.
pile_head – Dictionary of the elevation of the pile head in mLAT for each turbine.
water_depth – Dictionary of the water depth in mLAT for each turbine.
tw_sub_assemblies – Dataframe of the tower subassemblies data from each turbine.
tp_sub_assemblies – Dataframe of the transition piece subassemblies data from each turbine.
mp_sub_assemblies – Dataframe of the monopile subassemblies data from each turbine.
pile_toe – Dataframe of the elevation of the pile toe in mLAT from each turbine.
rna – Dataframe of the RNA data from each turbine.
tower – Dataframe of the tower data from each turbine.
transition_piece – Dataframe of the transition piece data from each turbine.
monopile – Dataframe of the monopile data from each turbine.
tw_lumped_mass – Dataframe of the lumped masses data of the tower from each turbine.
tp_lumped_mass – Dataframe of the lumped masses data of the transition piece from each turbine.
mp_lumped_mass – Dataframe of the lumped masses data of the monopile from each turbine.
tp_distributed_mass – Dataframe of the distributed masses data of the transition piece from each turbine.
mp_distributed_mass – Dataframe of the distributed masses data of the monopile from each turbine.
grout – Dataframe of the grout data from each turbine.
full_structure – Dataframe of the full structure data from each turbine.
tp_skirt – Dataframe of the transition piece skirt data from each turbine.
substructure – Dataframe of the substructure data from each turbine.
all_turbines – Dataframe of the general geometry data from each turbine.
all_tubular_structures – Dataframe of the tubular structures data from each turbine.
all_distributed_mass – Dataframe of the distributed masses data from each turbine.
all_lumped_mass – Dataframe of the lumped masses data from each turbine.

__init__(turbines: List[str], owts: List[OWT]) → None

Create an instance of the OWTs class with the required parameters.

Parameters:

turbines – List of turbine titles.
owts – List of OWT objects.

Returns:

process_structures() → None

Set dataframes containing the required properties to model the tower geometry, including the RNA system.

Returns:: None

select_owt(turbine: str | int) → OWT

Select OWT object from the OWTs object.

Parameters:: turbine – Title of the turbine or itss index in the original list of turbine titles (from get method).
Returns:: OWT object.