sfepy.terms.termsBiot module

class sfepy.terms.termsBiot.BiotETHTerm(name, arg_str, integral, region, **kwargs)

This term has the same definition as dw_biot_th, but assumes an exponential approximation of the convolution kernel resulting in much higher efficiency. Can use derivatives.

Definition :

Call signature:

dw_biot_eth	(ts, material_0, material_1, virtual, state)
	(ts, material_0, material_1, state, virtual)

Arguments 1:

• ts : TimeStepper instance
• material_0 :
• material_1 : (decay at )
• virtual :
• state :

Arguments 2:

• ts : TimeStepper instance
• material_0 :
• material_1 : (decay at )
• state :
• virtual :

arg_shapes = {}

arg_types = (('ts', 'material_0', 'material_1', 'virtual', 'state'), ('ts', 'material_0', 'material_1', 'state', 'virtual'))

get_fargs(ts, mat0, mat1, vvar, svar, mode=None, term_mode=None, diff_var=None, **kwargs)

modes = ('grad', 'div')

name = 'dw_biot_eth'

class sfepy.terms.termsBiot.BiotStressTerm(name, arg_str, integral, region, **kwargs)

Evaluate Biot stress tensor.

It is given in the usual vector form exploiting symmetry: in 3D it has 6 components with the indices ordered as , in 2D it has 3 components with the indices ordered as .

Supports ‘eval’, ‘el_avg’ and ‘qp’ evaluation modes.

Definition :

Call signature:

ev_biot_stress

(material, parameter)

Arguments :

• material :
• parameter :

arg_shapes = {'material': 'S, 1', 'parameter': 1}

arg_types = ('material', 'parameter')

static function(out, val_qp, mat, vg, fmode)

get_fargs(mat, parameter, mode=None, term_mode=None, diff_var=None, **kwargs)

name = 'ev_biot_stress'

class sfepy.terms.termsBiot.BiotTHTerm(name, arg_str, integral, region, **kwargs)

Fading memory Biot term. Can use derivatives.

Definition :

Call signature:

dw_biot_th	(ts, material, virtual, state)
	(ts, material, state, virtual)

Arguments 1:

• ts : TimeStepper instance
• material :
• virtual :
• state :

Arguments 2:

• ts : TimeStepper instance
• material :
• state :
• virtual :

arg_shapes = {}

arg_types = (('ts', 'material', 'virtual', 'state'), ('ts', 'material', 'state', 'virtual'))

get_fargs(ts, mats, vvar, svar, mode=None, term_mode=None, diff_var=None, **kwargs)

modes = ('grad', 'div')

name = 'dw_biot_th'

class sfepy.terms.termsBiot.BiotTerm(name, arg_str, integral, region, **kwargs)

Biot coupling term with given in vector form exploiting symmetry: in 3D it has the indices ordered as , in 2D it has the indices ordered as . Corresponds to weak forms of Biot gradient and divergence terms. Can be evaluated. Can use derivatives.

Definition :

Call signature:

dw_biot	(material, virtual, state)
	(material, state, virtual)
	(material, parameter_v, parameter_s)

Arguments 1:

• material :
• virtual :
• state :

Arguments 2:

• material :
• state :
• virtual :

Arguments 3:

• material :
• parameter_v :
• parameter_s :

arg_shapes = {'state/grad': 1, 'state/div': 'D', 'material': 'S, 1', 'virtual/grad': ('D', None), 'parameter_s': 1, 'parameter_v': 'D', 'virtual/div': (1, None)}

arg_types = (('material', 'virtual', 'state'), ('material', 'state', 'virtual'), ('material', 'parameter_v', 'parameter_s'))

get_eval_shape(mat, vvar, svar, mode=None, term_mode=None, diff_var=None, **kwargs)

get_fargs(mat, vvar, svar, mode=None, term_mode=None, diff_var=None, **kwargs)

modes = ('grad', 'div', 'eval')

name = 'dw_biot'

set_arg_types()