StiffenedPanel

class StiffenedPanel

Data class for the stiffened panel model.

Attributes:
dimint

The dimension of the problem, dim=10

modelSymbolicFunction

Model of the critical shearing load. The model has input dimension 10 and output dimension 1. More precisely, we have \vect{X} = (E, \nu, h_c, \ell, f_1, f_2, t, a, b_0, p) and Y = (N_{xy})_{cr}.

ETruncatedNormal

Young modulus distribution (Pa), ot.TruncatedNormal(110.0e9, 55.0e9, 99.0e9, 121.0e9)

nuUniform

Poisson coefficient (-) distribution ot.Uniform(0.3675, 0.3825)

h_cUniform

Distance between the mean surface of the hat and the foot of the Stiffener (m) distribution ot.Uniform(0.0285, 0.0315)

ellUniform

Length of the stiffener flank (m) distribution ot.Uniform(0.04655, 0.05145)

f_1Uniform

Width of the stiffener foot (m) distribution ot.Uniform(0.0266, 0.0294)

f_2Uniform

Width of the stiffener hat (m) distribution ot.Uniform(0.00627, 0.00693)

tUniform

Thickness of the panel and the stiffener (m) distribution ot.Uniform(8.02e-5, 8.181e-5)

aUniform

Width of the panel (m) distribution ot.Uniform(0.6039, 0.6161)

b_0Uniform

Distance between two stiffeners (m) distribution ot.Uniform(0.04455, 0.04545)

pUniform

Half-width of the stiffener (m) distribution ot.Uniform(0.03762, 0.03838)

distributionJointDistribution

The joint distribution of the input parameters.

Examples

>>> from openturns.usecases import stiffened_panel
>>> # Load the stiffened panel model
>>> panel = stiffened_panel.StiffenedPanel()
>>> print("Inputs:", panel.model.getInputDescription())
Inputs: [F,L,a,De,di,E]
>>> print("Outputs:", panel.model.getOutputDescription())
[Deflection,Left angle,Right angle]
__init__()

Examples using the class

Estimate a buckling probability

Estimate a buckling probability