hookeai.simulators.fetorch.element.type.tetra10.FETetra10¶

class FETetra10(n_gauss=4, device_type='cpu')[source]¶

Bases: ElementType

FETorch finite element: 10-Node Tetrahedral.

_name¶

Name.

Type:: str

_n_node¶

Number of nodes.

Type:: int

_n_dof_node¶

Number of degrees of freedom per node.

Type:: int

_nodes_local_coords¶

Nodes local coordinates stored as torch.Tensor(2d) of shape (n_node, n_dof_node).

Type:: torch.Tensor(2d)

_n_gauss¶

Number of Gauss quadrature integration points.

Type:: int

_gp_coords¶

Gauss quadrature integration points (key, str[int]) local coordinates (item, torch.Tensor(1d)). Gauss integration points are labeled from 1 to n_gauss.

Type:: dict

_gp_weights¶

Gauss quadrature integration points (key, str[int]) weights (item, float). Gauss integration points are labeled from 1 to n_gauss.

Type:: dict

_device_type¶

Type of device on which torch.Tensor is allocated.

Type:: {‘cpu’, ‘cuda’}

_device¶

Device on which torch.Tensor is allocated.

Type:: torch.device

_set_node_local_coords(self)¶: Set nodes local coordinates.

eval_shapefun(self, local_coords)[source]¶: Evaluate shape functions at given local coordinates.

eval_shapefun_local_deriv(self, local_coords)[source]¶: Evaluate shape functions local derivates at given local coordinates.

_admissible_gauss_quadratures()[source]¶: Get admissible Gauss integration quadratures.

Constructor.

Parameters:

n_gauss (int, default=4) – Number of Gauss quadrature integration points.
device_type ({'cpu', 'cuda'}, default='cpu') – Type of device on which torch.Tensor is allocated.

List of Public Methods

`check_shape_functions_properties`	Check if element shape functions satisfy known properties.
`eval_shapefun`	Evaluate shape functions at given local coordinates.
`eval_shapefun_local_deriv`	Evaluate shape functions local derivates at given local coordinates.
`get_batched_gauss_integration_points`	Get Gaussian quadrature points batched coordinates and weights.
`get_device`	Get device on which torch.Tensor is allocated.
`get_gauss_integration_points`	Get Gaussian quadrature points local coordinates and weights.
`get_n_dof_node`	Get number of degrees of freedom per node.
`get_n_gauss`	Get number of Gauss quadrature integration points.
`get_n_node`	Get number of nodes.
`set_device`	Set device on which torch.Tensor is allocated.

Methods

__init__(n_gauss=4, device_type='cpu')[source]¶

Constructor.

Parameters:

n_gauss (int, default=4) – Number of Gauss quadrature integration points.
device_type ({'cpu', 'cuda'}, default='cpu') – Type of device on which torch.Tensor is allocated.

static _admissible_gauss_quadratures()[source]¶

Get admissible Gauss integration quadratures.

Returns:: admissible_n_gauss – Admissible Gauss integration quadratures (number of Gauss integration points).
Return type:: tuple[int]

_set_nodes_local_coords()[source]¶: Set nodes local coordinates.

check_shape_functions_properties()¶: Check if element shape functions satisfy known properties.

eval_shapefun(local_coords)[source]¶

Evaluate shape functions at given local coordinates.

Parameters:: local_coords (torch.Tensor(1d)) – Local coordinates of point where shape functions are evaluated.
Returns:: shape_fun – Shape functions evaluated at given local coordinates, sorted according with element nodes.
Return type:: torch.Tensor(1d)

eval_shapefun_local_deriv(local_coords)[source]¶

Evaluate shape functions local derivates at given local coordinates.

Parameters:: local_coords (torch.Tensor(1d)) – Local coordinates of point where shape functions local derivatives are evaluated.
Returns:: shape_fun_local_deriv – Shape functions local derivatives evaluated at given local coordinates, sorted according with element nodes. Derivative of the i-th shape function with respect to the j-th local coordinate is stored in shape_fun_local_deriv[i, j].
Return type:: torch.Tensor(2d)

get_batched_gauss_integration_points(device=None)¶

Get Gaussian quadrature points batched coordinates and weights.

Parameters:

device (torch.device, default=None) – Device on which torch.Tensor is allocated.

Returns:

gp_coords_tensor (torch.Tensor(2d)) – Gauss quadrature integration points local coordinates stored as torch.Tensor(2d) of shape (n_gauss, n_dim). Gauss integration points are sorted according with their label.
gp_weights_tensor (torch.Tensor(1d)) – Gauss quadrature integration points weights stored as torch.Tensor(1d) of shape (n_gauss,). Gauss integration points are sorted with their label.

get_device()¶

Get device on which torch.Tensor is allocated.

Returns:

device_type ({‘cpu’, ‘cuda’}) – Type of device on which torch.Tensor is allocated.
device (torch.device) – Device on which torch.Tensor is allocated.

get_gauss_integration_points()¶

Get Gaussian quadrature points local coordinates and weights.

Returns:

gp_coords (dict) – Gauss quadrature integration points (key, str[int]) local coordinates (item, torch.Tensor(1d)). Gauss integration points are labeled from 1 to n_gauss.
gp_weights (dict) – Gauss quadrature integration points (key, str[int]) weights (item, torch.Tensor(0d)). Gauss integration points are labeled from 1 to n_gauss.

get_n_dof_node()¶

Get number of degrees of freedom per node.

Returns:: n_dof_node – Number of degrees of freedom per node.
Return type:: int

get_n_gauss()¶

Get number of Gauss quadrature integration points.

Returns:: n_gauss – Number of Gauss quadrature integration points.
Return type:: int

get_n_node()¶

Get number of nodes.

Returns:: n_node – Number of nodes.
Return type:: int

set_device(device_type)¶

Set device on which torch.Tensor is allocated.

Parameters:

device_type ({'cpu', 'cuda'}) – Type of device on which torch.Tensor is allocated.
device (torch.device) – Device on which torch.Tensor is allocated.