Source code for sfepy.terms.terms_point

import numpy as nm

from sfepy.base.base import assert_
from sfepy.terms.terms import Term

[docs]class LinearPointSpringTerm(Term): r""" Linear springs constraining movement of FE nodes in a region; to use as a relaxed Dirichlet boundary conditions. :Definition: .. math:: \ul{f}^i = -k \ul{u}^i \quad \forall \mbox{ FE node } i \mbox{ in a region } :Arguments: - material : :math:`k` - virtual : :math:`\ul{v}` - state : :math:`\ul{u}` """ name = 'dw_point_lspring' arg_types = ('material', 'virtual', 'state') arg_shapes = {'material' : '.: 1', 'virtual' : ('D', 'state'), 'state' : 'D'} integration = 'point'
[docs] @staticmethod def function(out, stiffness, vec, diff_var): if diff_var is None: out[:, 0, :, 0] = - stiffness * vec else: dim = vec.shape[1] eye = nm.eye(dim, dim, dtype=nm.float64) eye.shape = (1, 1) + eye.shape out[...] = - stiffness * eye return 0
[docs] def get_fargs(self, mat, virtual, state, mode=None, term_mode=None, diff_var=None, **kwargs): vec = state.get_state_in_region(self.region) return mat, vec, diff_var
[docs]class ConcentratedPointLoadTerm(Term): r""" Concentrated point load term. The load value must be given in form of a special material parameter (name prefixed with '.'), e.g. (in 2D):: 'load' : ({'.val' : [0.0, 1.0]},) This term should be used with special care, as it bypasses the usual evaluation in quadrature points. It should only be used with nodal FE basis. The number of rows of the load must be equal to the number of nodes in the region and the number of columns equal to the field dimension. :Definition: .. math:: \ul{f}^i = \ul{\bar f}^i \quad \forall \mbox{ FE node } i \mbox{ in a region } :Arguments: - material : :math:`\ul{\bar f}^i` - virtual : :math:`\ul{v}`, """ name = 'dw_point_load' arg_types = ('material', 'virtual') arg_shapes = {'material' : '.: N', 'virtual' : ('N', None)} integration = 'point'
[docs] @staticmethod def function(out, mat): out[:, 0, :, 0] = mat return 0
[docs] def get_fargs(self, mat, virtual, mode=None, term_mode=None, diff_var=None, **kwargs): return nm.asarray(mat),