# Deviation of a cantilever beam¶

This example has been presented in the ESREL 2007 conference in the paper [Dutfoy2009]. It is described in the OpenTURNS examples.

This example is a simple beam, restrained at one side and stressed by a concentrated bending load F at the other side.

## 1- Problem statement¶

### a- Inputs¶

Stochastic variables:

Name |
Description |
Distribution |
---|---|---|

E |
Young’s modulus |
Beta(r=0.93, t=3.2, a=2.8e7, b=4.8e7) |

F |
Charge applied |
LogNormal(mu=30000., sigma=9000., gamma=15000) |

L |
Length |
Uniform(a=250, b=260) |

I |
Section modulus |
Beta(r=2.5, t=4., a=3.1e2, b=4.5e2)) |

The input variables and are dependent. The dependence structure is modelised by a Normal copula (the only copula available for now in the GUI). The Spearman correlation matrix of the input random vector is :

### b- Output¶

The deviation of the free end of the beam equals to:

## 2- Define the model¶

### 2-1 Create the study¶

Click on in the tool bar to create a new study.

### 2-2 Create the analytical physical model¶

To define the physical model, click on the button **Symbolic model**
of the window shown above.

The following window appears and a physicalModel item is added in the study tree:

Click on the **Model definition** box of the model diagram to create the
following window.

Use the **Add** buttons below the tables to add as many lines as number of variables
in the physical model.

Fill the tables and rename variables to correspond to the physical model.
Click on the **Evaluate** button below the output variables table in order to check
if the formula is not badly defined.

The value of the output must be if:

E |
F |
L |
I |
---|---|---|---|

3e7 |
3e4 |
255 |
400 |

### 2-3 Create the probabilistic model¶

To define the probabilistic model, choose **Probabilistic model** in the
context menu of the sub item **Definition** of the model in the study tree.
Only one probabilistic model by physical model can be defined.

The probabilistic model is defined by associating a distribution to input variables
(**Marginals** tab) and specifying dependence between them if necessary (**Dependence** tab).

The **Marginals** tab lists automatically all the input variables defined
in the physical model window.
By default, all the lines are unchecked (then all the inputs
variables are deterministic) and the right side shows the variable value
(the one defined in the model window).

To make a variable stochastic, check its line. Then the combobox in the second
column of the table is available and the current text is **Normal**.
By default, the Normal distribution is used with a mean value
equal to the value defined in the physical model window and with a standard
deviation equal to

Check all the lines

Choose the right distribution for each input (

**Distribution**column)Change the distribution parameters values (right side) (Refer to the Inputs section).

On the left of the **Dependence** tab, all the stochastic input variables are listed.
By default, no dependence is set between these variables.

- To add dependence between the variables and :
Select and in the list

- Click on the right arrow:
these variables are disabled in the first table (a variable can belong to only one group)

- [I, L] appears in the second table:
the default copula is the Normal copula defined by a correlation matrix equal to the identity matrix. So, at this step, and are still independent.

on the right side: set the correlation matrix

## 3- Central tendency analysis¶

### 3-1 Taylor Expansions¶

For more details on the Linear Taylor Expansions, you can consult the OpenTURNS documentation.

#### 3-1-1 Definition¶

To perform a central tendency analysis with the Taylor expansions for the
estimation of moments, choose **New central tendency** in the
context menu of the probabilistic model item in the study tree.

Check the radio button **Taylor expansions** in the wizard which appears.

Click on **Finish** button. A new item with a default name appears in the study
tree and a window is created.

Click on **Run** button to launch the analysis. When the analysis is finished
a result window is created.

#### 3-1-2 Results¶

The results window contains a table.

### 3-2 Monte Carlo¶

For more details on the Monte Carlo method, you can consult the OpenTURNS documentation.

#### 3-2-1 Definition¶

To perform a central tendency analysis with the Monte Carlo method,
click on the **Central tendency** box of the model diagram.

Check the radio button **Monte Carlo** in the wizard which appears.

Click on the **Continue** button to parametrize the Monte Carlo method.

To see advanced parameters, expand the **Advanced parameters** group.

Click on **Finish** button. A new item with a default name appears in the study
tree and a window is created.

Click on **Run** button to launch the analysis. When the analysis is finished
a result window is created.

#### 3-2-2 Results¶

There are 8 tabs in the result window. The first tab must contain the following values :

## 4- Min/Max study with deterministic design of experiments¶

For more details on the Min/Max approach, you can consult the OpenTURNS documentation.

### 4-1 Definition¶

To perform a Min/Max study, choose **Design of experiments** in the
context menu of the probabilistic model item in the study tree.

Check the radio button **Deterministic** in the wizard which appears and click on
**Continue** button.

In the next table, you can set the grid parameters. By default, all lines are unchecked: the design of experiments contains only point. Check the **Name** column to make all the inputs variable.

The minimum and the maximum values are computed automatically from the range of the distribution of the variables. The number of used values per variable is by default 2.

Click on **Finish** button. A new item with a default name appears in the study
tree and a window is created.

### 4-2 Results¶

#### 4-2-1 Input variables¶

The result window shows the input sample of the design of experiments and an analysis of this sample.

The points are generated according to the structure of a box design of experiments. This deterministic design of experiments has 16 points obtained by regularly discretizing the pavement:

.

Click on **Evaluate** in the context menu of the design of experiments item.
Click on the **Finish** button of the window which appears.

To launch the analysis click on the **Run** button of the new window.

#### 4-2-2 Min/Max values¶

When the computation is finished, a new window is created.
The **Table** tab contains the input and output values.

The first tab must contain the following values:

## 5- Sensitivity analysis¶

### 5-1 Sobol indices¶

For more details on the computation of the Sobol indices, check the OpenTURNS documentation.

#### 5-1-1 Definition¶

To perform a sensitivity analysis with the Sobol method, the input variables must
be independent (In the **Dependence** tab
of the probabilistic model window replace -0.2 by 0). Choose **Sensitivity** in the
context menu of the probabilistic model item in the study tree.

Check the radio button **Sobol** in the wizard which appears.

Click on **Continue** button. On the new page, you can parametrize the Sobol
method. To access advanced parameters, expand the **Advanced parameters** group.

Define at least one criterion to stop the algorithm.

In the current example, add a third criterion by selecting the **Maximum calls**
check button.

Changing **Replication size** will update the max number of calls by iteration:
Indeed the algorithm build two input samples with a size equal to the block size value
and combines these samples to build *nbInputs* other samples
(*nbInputs* is the number of input variables).
Thus, the maximum number of calls by iteration is computed with the formula:
.

If the Replication size is 1000: the maximum number of calls by iteration is 6000.

In that case the algorithm will perform two iterations. Indeed, at the second iteration the maximum number of calls will not be reached yet. The effective maximum total number of calls will be 12000.

Click on **Finish** button. A new item with a default name appears in the study
tree and a results window is created.

#### 5-1-2 Results¶

The result window shows a table with the first and total order indices for each variable. Values must correspond to the values of the table below.

The interaction between the variables are mentioned below the table. It is the sum of second order indices. It can be visualize on the graphic by the distances between the first order indices and the total order indices.

The warnings inform the user that a total order index is smaller than the first order index. When increasing the sample size, these warnings disappear.

On the **Summary** tab the value of the effective stopping criteria is written in
a table.

### 5-2 SRC indices¶

For more details on the computation of the SRC indices ( Standard Regression Coefficients), you can consult the OpenTURNS documentation.

#### 5-2-1 Definition¶

To perform a sensitivity analysis with the SRC method, the input variables must
be independent (In the **Dependence** tab of the probabilistic model window replace
-0.2 by 0), then choose **Sensitivity** in the
context menu of the probabilistic model item in the study tree.

Check the radio button **SRC** in the wizard which appears.

Click on **Continue** button. On the new page, you can parametrize the SRC
method. To access advanced parameters, expand the **Advanced parameters** group.

Set the block size to 300. In that case the algorithm will generate a sample with 34 iterations (33 iterations with a size of 300 and the last iteration with a size of 100).

Click on **Finish** button. A new item with a default name appears in the study
tree and a results window is created.

#### 5-2-2 Results¶

The result window contains a table with the SRC indices values for each variable. These values are plotted in a graph.

## 6- Threshold exceedance¶

To perform the following analyses use again a Gaussian copula
(In the **Dependence** tab
of the probabilistic model window replace 0 by -0.2).

### 6-1 Limit state¶

To create the limit state function which enables the definition of the failure
event, choose **Limit state** in the context menu of the
probabilistic model item in the study tree.

After clicking, a new item with a default name appears in the study tree and the following window appears:

We consider the event where the deviation exceeds . Choose the right operator in the combobox and set the value of the threshold in order to obtain the following limit state window:

### 6-2 Monte Carlo¶

For more details on the computation of the failure probability by the method of Monte Carlo, you can consult the OpenTURNS documentation.

#### 6-2-1 Definition¶

To perform the Monte Carlo simulation, choose **Threshold exceedance** in the
context menu of the limit state item in the study tree.

Select the **Monte Carlo** method and click on **Continue** button.
The new page enables to change the parameters of the analysis.

The user has to define at least one criterion to stop the algorithm.

Add the third criterion by selecting the check button **Maximum calls**.
The maximum number of calls is 10000. Set the block size to 300.

In that case the algorithm will perform 34 iterations with 300 calls to the model function.

Effective maximum total number of calls: 10200

Click on **Finish** button. A new item with a default name appears in the study
tree and a results window is created.

#### 6-2-2 Results¶

The result window contains the following table:

The values of the output computed during the simulation are stored and plotted in the second tab of the window:

The convergence graph is in the third tab:

This graph shows the value of the probability estimate at each iteration.

### 6-3 FORM¶

For more details on the computation of the failure probability by the method of FORM, you can consult the OpenTURNS documentation.

#### 6-3-1 Definition¶

To perform the FORM (First Order Reliability Method) analysis, choose **Threshold exceedance** in the
context menu of the limit state item in the study tree.

Select the **FORM** method and click on **Continue** button.
The new page enables to change the parameters of the analysis.

The starting point is defined by default with the means of the distributions of the stochastic inputs.

#### 6-3-2 Results¶

The result window includes the following tables.

When the maximum number of iterations has been reached, a warning icon appears nearby the iterations number value: it warns the user that the optimization result may not be accurate enough.

The **Design point** tab indicates the value of the design point in the standard space and in
the physical space. The table contains the importance factors which are displayed in
the pie chart.

For more details on the Importance factors, you can consult the OpenTURNS documentation.

The **Sensitivity** tab indicates the sensitivity factors.
For more details on the Sensitivity factors,
you can consult the OpenTURNS documentation.

### 6-4 FORM-Importance sampling¶

For more details on the computation of the failure probability by the method of Importance sampling, you can consult the OpenTURNS documentation.

#### 6-4-1 Definition¶

To perform the FORM-IS (First Order Reliability Method-Importance sampling) analysis,
choose **Threshold exceedance** in the context menu of the limit state item in the study tree.

Select the **FORM-Importance sampling** method and click on **Continue** button.
The following page allows to change the parameters of the Importance sampling analysis.
It’s the same page as the one for the Monte Carlo method.

Click on **Continue** button.
The following page enables setting the parameters of the FORM analysis.
It’s the same page as the one for the FORM method.

The analysis consists in performing firstly a FORM analysis, then the computed design point is used to initialize the Importance sampling analysis.

#### 6-4-2 Results¶

The FORM-IS result window contains the same tabs as the Monte Carlo result window
as well as a **FORM result**
tab, which displays the tabs of a FORM result window.

We can see in the following table, the design point from the FORM analysis result.

The following histogram shows that, by contrast of the Monte Carlo method, the sampling is centered on the threshold of the event failure with the Importance sampling method.

## 7- Construction of response surfaces¶

A response surface is built from samples. So we first create a design of experiments.

### 7-1 Design of experiments¶

Create a design of experiments by choosing **New design of experiments** in the
context menu of the **Designs of experiments** item.

Select **Probabilistic** and click on **Continue** button.

The methods LHS and Quasi-Monte Carlo are not available because the model contains dependent stochastic input variables.

Keep the default values. Click on **Finish** button

Choose **Evaluate** in the context menu of the new design of experiments item.
Launch the evaluation by clicking on the **Run** button of the window which
appears.

### 7-2 Functional chaos¶

For more details on the computation of a metamodel by the method of Functional chaos, you can consult the OpenTURNS documentation.

The functional chaos allows to compute the Sobol indices. Beware that these indices
cannot be used for correlated stochastic variables. In order to use these indices,
replace the value -0.2 by 0 in the **Dependence** tab of the probabilistic model window.

#### 7-2-1 Definition¶

Choose **Metamodel** in the context menu of the sub-item **Evaluation** of the
design of experiments item.

Select the **Functional chaos** method and click on **Continue** button.

Set the chaos degree to 4 and click on **Continue** and then on **Finish** button
in the next page.

Launch the analysis.

#### 7-2-2 Results¶

The first tab of the result window displays the metamodel. The relative error expresses the quality of the metamodel.

The moments retrieved from the polynomial basis correspond to the result of the central tendency analyses.

The windows shows the Sobol indices. We can see that the values are similar to the ones obtained with the sensitivity analysis.

- The analysis computes a surrogate model which can be retrieved and checked:
Click on the context menu of the metamodel item.

Choose

**Convert metamodel into physical model**. A new item**MetaModel_0**appears in the study tree.

- Click on its sub-item
**Definition**. A model definition window appears: Evaluate the model by clicking on the

**Evaluate**button. The output value is close to the value obtained with the analytical formula.

- Click on its sub-item

### 7-3 Kriging¶

For more details on the computation of a metamodel by the method of Kriging, you can consult the OpenTURNS documentation.

#### 7-3-1 Definition¶

Choose **Metamodel** in the context menu of the sub-item **Evaluation** of the
design of experiments item.

Select the **Kriging** method and click on **Continue** button.

Check the button **By K-Fold method**.
For more details on the
K-Fold
method, check the OpenTURNS documentation.
Beware the computation may be expensive: In the current example, the K-Fold method builds a metamodel five times.

Click on **Finish** button.

Launch the analysis.

#### 7-3-2 Results¶

The window contains a **Validation** tab, which presents:

the metamodel predictivity coefficient:

the residual: .

with , the sample size; , the real values and , the predicted values.

Here the Q2 value is nearly equal to 1, so we can conclude that the metamodel is valid.

The **Results** tab displays the optimized covariance model parameters and
the trend coefficients.

## 8- Data analysis¶

To perform the following analyses use again a Gaussian copula
(In the **Dependence** tab
of the probabilistic model window replace 0 by -0.2).

### 8-1 Data¶

We first create a sample for our example:

Create a design of experiments by choosing

**New design of experiments**in the context menu of the**Designs of experiments**item.

Select

**Probabilistic**and click on**Continue**button. Note the probabilistic experiment uses the distribution of the model to generate the sample (marginals and copula).

Set the sample size to 1000. Click on

**Finish**button.In the

**Table**tab of the window click on**Export**button.Save the sample in a file.

### 8-2 Data model¶

On the study window click on **Data model**.

A new item and a new window appear:

Click on the **Model definition** box of the diagram.

A window is created to define the model. Click on the **…** button and load
the file created in the previous part. Define the last variable as an input by finding the right item
in the combo box on the line **Type**.

### 8-3 Analysis¶

Choose **Data analysis** in the context menu of the sub-item **Definition** of the model.

Launch the analysis.

The following window appears.

In the **dependence** tab, we can see that the variables L and I are correlated:
this is in agreement with the distribution used to
generate this variable.

## 9- Inference¶

### 9-1 Definition¶

Choose **Inference** in the context menu of the sub-item **Definition** of the model.

- A window appears:
In the current example, we choose to select 3 variables (E,F,I) : uncheck L.

Add all the distributions for the other variables by choosing the

**All**item in the combo box**Add**.Click on the

**Finish**button.

Launch the analysis.

### 9-2 Results¶

The inference analysis recognized a Beta distribution for the variable E: this is in agreement with the distribution used to generate this variable.

## 10- Dependence inference¶

To explore dependence between variables, the user can use dependence inference analysis.

### 10-1 Definition¶

Choose **Dependence inference** in the context menu of the sub-item **Definition** of the model.

The window which appears, may have default defined groups.
There are detected from the Spearman’s matrix estimate. In the current example,
the variables **L** and **I** are dependent.

By default, the Normal copula is tested.
Add all the copulas by choosing the **All** item in the combo box **Add**.

Launch the analysis.

### 10-2 Results¶

The dependence inference analysis recognized a Normal copula for the group [L, I]: this is in agreement with the distribution used to generate this variable. The Spearman coefficient is not exactly equal to -0.2 because the sample is not large enough.