pymoto.Stress

class pymoto.Stress(domain: VoxelDomain, e_modulus: float = 1.0, poisson_ratio: float = 0.3, plane: str = 'strain')

Calculate the average stresses per element

Input Signal:

Output Signal:

__init__(domain: VoxelDomain, e_modulus: float = 1.0, poisson_ratio: float = 0.3, plane: str = 'strain')

Initialize stress evaluation module

Parameters:

notation (Use Voigt strain)
e_modulus (float, optional) – Young’s modulus. Defaults to 1.0.
poisson_ratio (float, optional) – Poisson ratio. Defaults to 0.3.
plane (str, optional) – Plane “strain” or “stress”. Defaults to “strain”.

Methods

`__init__`(domain[, e_modulus, poisson_ratio, ...])	Initialize stress evaluation module
`connect`(sig_in[, sig_out])	Connect without automatic adding to a function network
`get_input_sensitivities`([as_list])
`get_input_states`([as_list])
`get_output_sensitivities`([as_list])
`get_output_states`([as_list])
`reset`()	Reset the state of the sensitivities (they are set to zero or to None)
`response`()	Calculate the response from sig_in and output this to sig_out
`sensitivity`()	Calculate sensitivities using backpropagation

Attributes

`n_in`	Get the number of input signals
`n_out`	Get the number of output signals
`sig_in`
`sig_out`

connect(sig_in: Signal | Iterable[Signal], sig_out: Signal | Iterable[Signal] = None): Connect without automatic adding to a function network

property n_out: int

Get the number of output signals

Note: Cannot be used in the initial __call__()

reset(): Reset the state of the sensitivities (they are set to zero or to None)

sensitivity()

Calculate sensitivities using backpropagation

Based on the sensitivity we get from sig_out, reverse the process and output the new sensitivities to sig_in