hookeai.model_architectures.rc_base_model.model.recurrent_model.VonMisesMixedVMAP¶

class VonMisesMixedVMAP(strain_formulation, problem_type, model_parameters, is_su_float64=True, device_type='cpu')[source]¶

Bases: ConstitutiveModel

Von Mises constitutive model with isotropic and kinematic hardening.

Compatible with vectorized mapping.

_name¶

Constitutive model name.

Type:: str

_strain_type¶

Material constitutive model strain formulation: infinitesimal strain formulation (‘infinitesimal’), finite strain formulation (‘finite’) or finite strain formulation through kinematic extension (‘finite-kinext’).

Type:: {‘infinitesimal’, ‘finite’, ‘finite-kinext’}

_model_parameters¶

Material constitutive model parameters.

Type:: dict

_n_dim¶

Problem number of spatial dimensions.

Type:: int

_comp_order_sym¶

Strain/Stress components symmetric order.

Type:: list

_comp_order_nsym¶

Strain/Stress components nonsymmetric order.

Type:: list

_is_su_float64¶

If True, then state update is locally computed in floating-point double precision. If False, then default floating-point precision is assumed.

Type:: bool

_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

get_required_model_parameters()[source]¶: Get required material constitutive model parameters.

state_init(self)[source]¶: Get initialized material constitutive model state variables.

state_update(self, inc_strain, state_variables_old)[source]¶: Perform material constitutive model state update.

_elastic_step(cls, e_trial_strain_mf, trial_stress_mf, acc_p_strain_old, back_stress_old_mf)[source]¶: Perform elastic step.

_plastic_step(cls, is_elastic_step, e_trial_strain_mf, relative_eq_trial_stress, e_consistent_tangent_mf, flow_vector_mf, acc_p_strain_old, back_stress_old_mf, G, hardening_law, hardening_parameters, kinematic_hardening_law, kinematic_hardening_parameters, su_conv_tol, su_max_n_iterations):: Perform plastic step.

_nr_iteration(cls, inc_p_mult, residual, G, H, kin_hard_slope)[source]¶: Newton-Raphson iteration (return-mapping).

Constitutive model constructor.

Parameters:

strain_formulation ({'infinitesimal', 'finite'}) – Problem strain formulation.
problem_type (int) – Problem type: 2D plane strain (1), 2D plane stress (2), 2D axisymmetric (3) and 3D (4).
model_parameters (dict) – Material constitutive model parameters.
is_su_float64 (bool, default=True) – If True, then state update is locally computed in floating-point double precision. If False, then default floating-point precision is assumed.
device_type ({'cpu', 'cuda'}, default='cpu') – Type of device on which torch.Tensor is allocated.

List of Public Methods

`get_device`	Get device on which torch.Tensor is allocated.
`get_model_parameters`	Get material constitutive model parameters.
`get_name`	Get material constitutive model name.
`get_required_model_parameters`	Get required material constitutive model parameters.
`get_strain_type`	Get material constitutive model strain formulation.
`set_device`	Set device on which torch.Tensor is allocated.
`state_init`	Get initialized material constitutive model state variables.
`state_update`	Perform material constitutive model state update.

Methods

__init__(strain_formulation, problem_type, model_parameters, is_su_float64=True, device_type='cpu')[source]¶

Constitutive model constructor.

Parameters:

strain_formulation ({'infinitesimal', 'finite'}) – Problem strain formulation.
problem_type (int) – Problem type: 2D plane strain (1), 2D plane stress (2), 2D axisymmetric (3) and 3D (4).
model_parameters (dict) – Material constitutive model parameters.
is_su_float64 (bool, default=True) – If True, then state update is locally computed in floating-point double precision. If False, then default floating-point precision is assumed.
device_type ({'cpu', 'cuda'}, default='cpu') – Type of device on which torch.Tensor is allocated.

classmethod _elastic_step(e_trial_strain_mf, trial_stress_mf, acc_p_strain_old, back_stress_old_mf)[source]¶

Perform elastic step.

Parameters:

e_trial_strain_mf (torch.Tensor(1d)) – Elastic trial strain (matricial form).
trial_stress_mf (torch.Tensor(1d)) – Trial stress (matricial form).
acc_p_strain_old (torch.Tensor(0d)) – Last convergence accumulated plastic strain.
back_stress_old_mf (torch.Tensor(1d)) – Last converged back-stress (matricial form).

Returns:

elastic_step_output – Elastic step concatenated output data.

Return type:

torch.Tensor(1d)

classmethod _nr_iteration(inc_p_mult, residual, G, H, kin_hard_slope)[source]¶

Newton-Raphson iteration (return-mapping).

Parameters:

inc_p_mult (torch.Tensor(0d)) – Incremental plastic multiplier.
residual (torch.Tensor(0d)) – Residual.
G (torch.Tensor(0d)) – Shear modulus.
H (torch.Tensor(0d)) – Hardening modulus.
kin_hard_slope (torch.Tensor(0d)) – Kinematic hardening modulus.

Returns:

inc_p_mult – Incremental plastic multiplier.

Return type:

torch.Tensor(0d)

classmethod _plastic_step(is_elastic_step, e_trial_strain_mf, relative_eq_trial_stress, e_consistent_tangent_mf, flow_vector_mf, acc_p_strain_old, back_stress_old_mf, G, hardening_law, hardening_parameters, kinematic_hardening_law, kinematic_hardening_parameters, su_conv_tol, su_max_n_iterations)[source]¶

Perform plastic step.

Parameters:

is_elastic_step (torch.Tensor(0d)) – If True, then avoid return mapping computations and compute elastic response. This flag avoids non-admissible values stemming from invalid return-mapping problem and consequent runtime errors when computing gradients with autograd.
e_trial_strain_mf (torch.Tensor(1d)) – Elastic trial strain (matricial form).
relative_eq_trial_stress (torch.Tensor(1d)) – Relative equivalent trial stress.
e_consistent_tangent_mf (torch.Tensor(2d)) – Elastic consistent tangent modulus (matricial form).
flow_vector_mf (torch.Tensor(1d)) – Flow vector.
acc_p_strain_old (torch.Tensor(0d)) – Last convergence accumulated plastic strain.
back_stress_old_mf (torch.Tensor(1d)) – Last converged back-stress (matricial form).
G (torch.Tensor(0d)) – Shear modulus.
hardening_law (function) – Hardening law.
hardening_parameters (dict) – Hardening law parameters.
kinematic_hardening_law (function) – Kinematic hardening law.
kinematic_hardening_parameters (dict) – Kinematic hardening law parameters.
su_conv_tol (float) – State update convergence tolerance.
su_max_n_iterations (int) – State update maximum number of iterations.

Returns:

plastic_step_output – Plastic step concatenated output data.

Return type:

torch.Tensor(1d)

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_model_parameters()¶

Get material constitutive model parameters.

Returns:: model_parameters – Material constitutive model parameters.
Return type:: dict

get_name()¶

Get material constitutive model name.

Returns:: name – Material constitutive model name.
Return type:: str

static get_required_model_parameters()[source]¶

Get required material constitutive model parameters.

Model parameters:

‘elastic_symmetry’ : Elastic symmetry (str, {‘isotropic’, ‘transverse_isotropic’, ‘orthotropic’, ‘monoclinic’, ‘triclinic’});
‘elastic_moduli’ : Elastic moduli (dict, {‘Eijkl’: float});
‘euler_angles’Euler angles (degrees) sorted according with Bunge
convention (tuple[float]).
‘hardening_law’ : Isotropic hardening law (function)
‘hardening_parameters’ : Isotropic hardening law parameters (dict)
‘kinematic_hardening_law’ : Kinematic hardening law (function)
‘kinematic_hardening_parameters’Kinematic hardening law
parameters (dict)

Returns:: model_parameters_names – Material constitutive model parameters names (str).
Return type:: tuple[str]

get_strain_type()¶

Get material constitutive model strain formulation.

Returns:: strain_type – Material constitutive model strain formulation: infinitesimal strain formulation (‘infinitesimal’), finite strain formulation (‘finite’) or finite strain formulation through kinematic extension (‘finite-kinext’).
Return type:: {‘infinitesimal’, ‘finite’, ‘finite-kinext’}

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.

state_init()[source]¶

Get initialized material constitutive model state variables.

Constitutive model state variables:

e_strain_mf
- Infinitesimal strains: Elastic infinitesimal strain tensor (matricial form).
- Symbol: \(\boldsymbol{\varepsilon^{e}}\)
acc_p_strain
- Accumulated plastic strain.
- Symbol: \(\bar{\varepsilon}^{p}\)
strain_mf
- Infinitesimal strains: Infinitesimal strain tensor (matricial form).
- Symbol: \(\boldsymbol{\varepsilon}\)
stress_mf
- Infinitesimal strains: Cauchy stress tensor (matricial form).
- Symbol: \(\boldsymbol{\sigma}\)
back_stress_mf
- Infinitesimal strains: Back-stress tensor (matricial form).
- Symbol: \(\boldsymbol{\beta}\)
is_plastic
- Plastic step flag.
is_su_fail
- State update failure flag.

Returns:: state_variables_init – Initialized material constitutive model state variables.
Return type:: dict

state_update(inc_strain, state_variables_old)[source]¶

Perform material constitutive model state update.

Parameters:

inc_strain (torch.Tensor(2d)) – Incremental strain second-order tensor.
state_variables_old (dict) – Last converged constitutive model material state variables.

Returns:

state_variables (dict) – Material constitutive model state variables.
consistent_tangent_mf (torch.Tensor(2d)) – Material constitutive model consistent tangent modulus stored in matricial form.