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)

correlation_matrixCorrelationMatrix

The correlation matrix used for inputs dependence, mostly identity except for the term representing the interaction between variables f_1 and f_2 which is -0.8.

copulaNormalCopula

The (Normal) copula used to define the distribution of the input parameters.

distributionJointDistribution

The joint distribution of the input parameters.

independentDistributionJointDistribution

The joint distribution of the input parameters for the special case of independence.

Examples

>>> from openturns.usecases import stiffened_panel
>>> # Load the stiffened panel model
>>> panel = stiffened_panel.StiffenedPanel()
>>> print("Inputs:", panel.model.getInputDescription())
Inputs: [E,nu,h_c,ell,f_1,f_2,t,a,b_0,p]
>>> print("Outputs:", panel.model.getOutputDescription())
Outputs: [(N_{xy})_{cr}]
__init__()

Examples using the class

Estimate a buckling probability

Estimate a buckling probability