# Estimate a probability with LHS¶

In this basic example we are going to estimate a probability by means of a simulation algorithm.

This model is a simple beam, restrained at one side and stressed by a traction load F at the other side.

The geometry is supposed to be deterministic: the diameter D is fixed to D=20 mm.

It is considered that failure occurs when the beam plastifies, i.e. when the axial stress gets bigger than the yield stress:

Therefore, the state limit G used here is:

Two independent random variables R and S are considered:

R (the strength):

S (the load):

Stochastic model:

F ~ Normal(75e3, 5e3) [N]

R ~ LogNormal(300, 30) [N]

Theoretical results:

This two dimensional stochastic problem can be solved by calculating directly the failure probability:

If R and S are independant, then:

The numerical application gives:

```
[1]:
```

```
from __future__ import print_function
import openturns as ot
```

```
[2]:
```

```
# create the joint distribution of the parameters
distribution_R = ot.LogNormalMuSigma(300.0, 30.0, 0.0).getDistribution()
distribution_F = ot.Normal(75e3, 5e3)
marginals = [distribution_R, distribution_F]
distribution = ot.ComposedDistribution(marginals)
```

```
[3]:
```

```
# create the model
model = ot.SymbolicFunction(['R', 'F'], ['R-F/(pi_*100.0)'])
```

```
[4]:
```

```
# create the event we want to estimate the probability
vect = ot.RandomVector(distribution)
G = ot.CompositeRandomVector(model, vect)
event = ot.ThresholdEvent(G, ot.Less(), 0.0)
```

```
[5]:
```

```
# create a Monte Carlo algorithm
algo = ot.LHS(event)
algo.setMaximumCoefficientOfVariation(0.05)
algo.setMaximumOuterSampling(int(1e5))
algo.run()
```

```
[6]:
```

```
# retrieve results
result = algo.getResult()
probability = result.getProbabilityEstimate()
print('Pf=', probability)
```

```
Pf= 0.029342988609791055
```