SORM¶

class
SORM
(*args)¶ Second Order Reliability Method (SORM).
Refer to SORM.
 Available constructors:
 SORM(nearestPointAlgorithm, event, physicalStartingPoint)
Parameters:  nearestPointAlgorithm :
OptimizationAlgorithm
Optimization algorithm used to research the design point.
 event :
Event
Failure event.
 physicalStartingPoint : sequence of float
Starting point of the optimization algorithm, declared in the physical space.
See also
Analytical
,AnalyticalResult
,FORM
,StrongMaximumTest
,SORMResult
Notes
See
Analytical
for the description of the first steps of the SORM analysis.The Second Order Reliability Method (SORM) consists in approximating the limit state surface in Uspace at the design point by a quadratic surface. SORM is usually more accurate than FORM e.g. in case when the event boundary is highly curved.
Let us denote by the dimension of the random vector and the main curvatures of the limit state function at the design point in the standard space.
Several approximations of the failure probability are available in the library, and detailed here in the case where the origin of the standard space does not belong to the failure domain:
Breitung’s formula:
the marginal cumulative density function of the spherical distributions in the standard space and is the HasoferLind reliability index, defined as the distance of the design point to the origin of the standard space.
Hohen Bichler’s formula is an approximation of the previous equation:
where is the cumulative distribution function of the standard 1D normal distribution and is the standard Gaussian probability density function.
Tvedt’s formula:
where is the real part of the complex number and the complex number such that .
The evaluation of the failure probability is stored in the data structure
SORMResult
recoverable with thegetResult()
method.Examples
>>> import openturns as ot >>> myFunction = ot.SymbolicFunction(['E', 'F', 'L', 'I'], ['F*L^3/(3*E*I)']) >>> myDistribution = ot.Normal([50.0, 1.0, 10.0, 5.0], [1.0]*4, ot.IdentityMatrix(4)) >>> vect = ot.RandomVector(myDistribution) >>> output = ot.CompositeRandomVector(myFunction, vect) >>> event = ot.Event(output, ot.Less(), 3.0) >>> # We create an OptimizationAlgorithm algorithm >>> solver = ot.AbdoRackwitz() >>> algo = ot.SORM(solver, event, [50.0, 1.0, 10.0, 5.0]) >>> algo.run() >>> result = algo.getResult()
Attributes: thisown
The membership flag
Methods
getAnalyticalResult
()Accessor to the result. getClassName
()Accessor to the object’s name. getEvent
()Accessor to the event of which the probability is calculated. getId
()Accessor to the object’s id. getName
()Accessor to the object’s name. getNearestPointAlgorithm
()Accessor to the optimization algorithm used to find the design point. getPhysicalStartingPoint
()Accessor to the starting point of the optimization algorithm. getResult
()Accessor to the result of SORM. getShadowedId
()Accessor to the object’s shadowed id. getVisibility
()Accessor to the object’s visibility state. hasName
()Test if the object is named. hasVisibleName
()Test if the object has a distinguishable name. run
()Evaluate the failure probability. setEvent
(event)Accessor to the event of which the probability is calculated. setName
(name)Accessor to the object’s name. setNearestPointAlgorithm
(solver)Accessor to the optimization algorithm used to find the design point. setPhysicalStartingPoint
(physicalStartingPoint)Accessor to the starting point of the optimization algorithm. setResult
(sormResult)Accessor to the result of SORM. setShadowedId
(id)Accessor to the object’s shadowed id. setVisibility
(visible)Accessor to the object’s visibility state. 
__init__
(*args)¶ Initialize self. See help(type(self)) for accurate signature.

getAnalyticalResult
()¶ Accessor to the result.
Returns:  result :
AnalyticalResult
Result structure which contains the results of the optimisation problem.
 result :

getClassName
()¶ Accessor to the object’s name.
Returns:  class_name : str
The object class name (object.__class__.__name__).

getEvent
()¶ Accessor to the event of which the probability is calculated.
Returns:  event :
Event
Event of which the probability is calculated.
 event :

getId
()¶ Accessor to the object’s id.
Returns:  id : int
Internal unique identifier.

getName
()¶ Accessor to the object’s name.
Returns:  name : str
The name of the object.

getNearestPointAlgorithm
()¶ Accessor to the optimization algorithm used to find the design point.
Returns:  algorithm :
OptimizationAlgorithm
Optimization algorithm used to research the design point.
 algorithm :

getPhysicalStartingPoint
()¶ Accessor to the starting point of the optimization algorithm.
Returns:  point :
Point
Starting point of the optimization algorithm, declared in the physical space.
 point :

getResult
()¶ Accessor to the result of SORM.
Returns:  result :
SORMResult
Structure containing all the results of the SORM analysis.
 result :

getShadowedId
()¶ Accessor to the object’s shadowed id.
Returns:  id : int
Internal unique identifier.

getVisibility
()¶ Accessor to the object’s visibility state.
Returns:  visible : bool
Visibility flag.

hasName
()¶ Test if the object is named.
Returns:  hasName : bool
True if the name is not empty.

hasVisibleName
()¶ Test if the object has a distinguishable name.
Returns:  hasVisibleName : bool
True if the name is not empty and not the default one.

run
()¶ Evaluate the failure probability.
Notes
Evaluate the failure probability and create a
SORMResult
, the structure result which is accessible with the methodgetResult()
.

setEvent
(event)¶ Accessor to the event of which the probability is calculated.
Parameters:  event :
Event
Event of which the probability is calculated.
 event :

setName
(name)¶ Accessor to the object’s name.
Parameters:  name : str
The name of the object.

setNearestPointAlgorithm
(solver)¶ Accessor to the optimization algorithm used to find the design point.
Parameters:  algorithm :
OptimizationAlgorithm
Optimization algorithm used to research the design point.
 algorithm :

setPhysicalStartingPoint
(physicalStartingPoint)¶ Accessor to the starting point of the optimization algorithm.
Parameters:  point : sequence of float
Starting point of the optimization algorithm, declared in the physical space.

setResult
(sormResult)¶ Accessor to the result of SORM.
Parameters:  result :
SORMResult
Structure containing all the results of the SORM analysis.
 result :

setShadowedId
(id)¶ Accessor to the object’s shadowed id.
Parameters:  id : int
Internal unique identifier.

setVisibility
(visible)¶ Accessor to the object’s visibility state.
Parameters:  visible : bool
Visibility flag.

thisown
¶ The membership flag