pyleecan.Classes.LossModelBertotti module

Method code available at https://github.com/Eomys/pyleecan/tree/master/pyleecan/Methods/Loss/LossModelBertotti

class LossModelBertotti(k_hy=None, k_ed=None, k_ex=None, name='', group='', is_show_fig=False, coeff_dict=None, init_dict=None, init_str=None)[source]

Bases: LossModel

Bertotti Loss Model Class

VERSION = 1
comp_coeff(material)

Enables to compute the coefficients of the loss model with a curve fitting on loss data stored in the material (stored in self.k_hy, self.k_ed, self.k_ex)

Parameters:

  • self (LossModelBertotti) – A loss model to compute Bertotti losses

  • material (Material) – A material object, corresponding to the material used in the electrical machine. This material object must contain loss data as an ImportMatrixVal object. This matrix must contain 3 rows, correspoding to the excitation frequency (Hz), the peak magnetic flux density (T), and the loss density (W/kg) in this order.

comp_loss()

Calculate loss density in iron core given by group “stator core” or “rotor core” assuming power density is given by a Bertotti model

Pcore = k_hy * f * B^2 + k_ed * (f B)^2 + k_ex * (f B)^1.5

Parameters:

self (LossModelBertotti) – a LossModelBertotti 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.

copy()[source]

Creates a deepcopy of the object

property k_hy

Hysteresis loss coefficient

Type:

float

property k_ed

Eddy current loss coefficient

Type:

float

property k_ex

Excess loss coefficient

Type:

float