pyleecan.Classes.LossModelProximity module¶
Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Loss/LossModelProximity
- class LossModelProximity(k_p=None, name='', group='', is_show_fig=False, coeff_dict=None, init_dict=None, init_str=None)[source]¶
Bases:
LossModelProximity Loss Model Class
- VERSION = 1¶
- comp_coeff()¶
Compute the Proximity loss coefficient [W/kg]
- Parameters:
self (LossModelProximity) –
- comp_loss()¶
Calculate proximity loss density assuming it is given by:
P = k_p * f^2 * B^2
- Parameters:
self (LossModelProximity) – a LossModelProximity object
- Returns:
Pcore_density (ndarray) – Core loss density function of frequency and elements [W/m3]
freqs (ndarray) – frequency vector [Hz]
- save(save_path='', is_folder=False, type_handle_old=2, type_compression=0)¶
Save the object to the save_path
- Parameters:
self – A pyleecan object
save_path (str) – path to the folder to save the object
is_folder (bool) – to split the object in different files: separate simulation machine and materials (json only)
type_handle_old (int) – How to handle old file in folder mode (0:Nothing, 1:Delete, 2:Move to “Backup” folder)
type_compression (int) – Available only for json, 0: no compression, 1: gzip
- get_logger()¶
Get the object logger or its parent’s one
- Parameters:
obj – A pyleecan object
- Returns:
logger – Pyleecan object dedicated logger
- Return type:
logging.Logger
- compare(other, name='self', ignore_list=None, is_add_value=False)[source]¶
Compare two objects and return list of differences
- as_dict(type_handle_ndarray=0, keep_function=False, **kwargs)[source]¶
Convert this object in a json serializable dict (can be use in __init__). type_handle_ndarray: int
How to handle ndarray (0: tolist, 1: copy, 2: nothing)
- keep_functionbool
True to keep the function object, else return str
Optional keyword input parameter is for internal use only and may prevent json serializability.
- property k_p¶
Proximity loss coefficient
- Type:
float