Cantilever beam tutorial

The simple example of the cantilever beam allows us to illustrate how to use the OpenTURNSWrapper class. This example is based on a quick numerical model, which was proposed in the documentation of an OpenTURNS module (see https://openturns.github.io/otwrapy/master/beam_wrapper.html).

In this case, the numerical model is an executable that computes the deviation of a beam under bending stress according to the following expression:

where E is the Young modulus (Pa), F is the Loading (N), L is the Length of beam (m) and I is the Moment of inertia (m^4).

To evaluate this numerical model, one can run the following shell command:

$ beam -x beam_input.xml

where beam_input.xml is the input file containing the four parameter. Note that an example of this file can be created by manually replacing the tokens @F@, @E@, @L@, @I@ by numerical values in the file template/beam_input_template.xml. The output of the code is an xml file _beam_outputs_.xml containing the deviation and its derivates.

1- Prepare your environment in CRONOS

• Connect to CRONOS, create your Python environment called myenv using conda-forge:

  NNI@dspxxxxxxx:~$ ssh cronos
  [NNI-crfront1-pts48] ~ $ module load Miniforge3
  [NNI-crfront1-pts48] ~ $ conda create -n myenv openturns dask-jobqueue 
  [NNI-crfront1-pts48] ~ $ conda activate myenv
  

  The creation of your environment (second line), does not need to be repeated at each connection.

• Clone the git repository in your “scratch” space and create your branch:

  (myenv) [NNI-crfront1-pts48] ~$ cd /scratch/users/{NNI}/
  (myenv) [NNI-crfront1-pts48] ~$ git clone /https://gitlab.pleiade.edf.fr/projet-incertitudes/openturns/openturns/actions-openturns/hpc-phimeca.git
  (myenv) [NNI-crfront1-pts48] ~$ cd /scratch/users/{NNI}/hpc-phimeca/othpc/
  (myenv) [NNI-crfront1-pts48] ~$ pip install -e .
  

2- Files required for the cantilever beam

The requirements for this example include:

• Template input file (here, template/beam_input_template.xml);

• Executable file (here, template/beam);

• Input design of experiments to be evaluated (here, input_doe/doe.csv).

• Output folder for my results (here, my_results)

  In the case of the cantilever beam, this is its content:

  ├── cantilever_beam
  |   ├── input_doe
  |   |    ├── doe.csv 
  |   ├── template
  |   |    ├── beam.exe
  |   |    ├── beam_input_template.xml
  |   ├── my_results 
  

3- How to run an othpc script?

A- Define the simulation model

import os
import othpc
import openturns as ot
from cantilever_beam import CantileverBeam

input_template_file = "template/beam_input_template.xml"
executable_file = "template/beam"
my_results_directory = "my_results"
cb = CantileverBeam(input_template_file, executable_file, my_results_directory, n_cpus=2)

Note that the argument n_cpus is set here to 2 (by default n_cpus=1). Therefore, the evaluation of a sample of input points is distributed by multiprocessing on 2 CPUs.

B- Define an OpenTURNS function distributing its evaluations

dw = othpc.SubmitFunction(cb, tasks_per_job=2, cpus_per_job=2, timeout_per_job=5)
dwfun = ot.Function(dw)

As the evaluations of the CantileverBeam class are distributed over 2 CPUs here (see remark above), two evaluation are done per SLURM job.

C- Define input design of experiments with size N=10 and evaluate it on the HPC

X = ot.Sample.ImportFromCSVFile("input_doe/doe.csv", ",")
Y = dwfun(X)
print(Y)

D- Create a summary table gathering inputs and corresponding evaluated outputs

othpc.make_summary_file("my_results", summary_file="summary_table.csv")

3- Resulting file tree

After running the previous Python script, one gets the following file-tree results. In the folder my_results, 10 subfolders have been created with a unique hash, corresponding to each evaluation. In the logs folder, 5 subfolders were created, corresponding to all the SLURM jobs submitted (since the argument tasks_per_job=2 here).

  ├── cantilever_beam
  |   ├── input_doe
  |   |    ├── doe.csv 
  |   ├── template
  |   |    ├── beam.exe
  |   |    ├── beam_input_template.xml
  |   ├── logs
  |   │   ├── 54474902
  |   │   ├── 54474903
  |   │   ├── 54474904
  |   │   ├── 54474906
  |   │   └── 54474907
  |   ├── my_results
  |   │   ├── simu_2025-05-13_17-37_69us6w9c
  |   │   ├── simu_2025-05-13_17-37_71krv2ic
  |   │   ├── simu_2025-05-13_17-37_ejk7pqsl
  |   │   ├── simu_2025-05-13_17-37_p4jyjbp2
  |   │   ├── simu_2025-05-13_17-37_t687ohjt
  |   │   ├── simu_2025-05-13_17-37_xv_pbjmo
  |   │   ├── simu_2025-05-13_17-38_0ueo2xry
  |   │   ├── simu_2025-05-13_17-38_95e6rhvd
  |   │   ├── simu_2025-05-13_17-38_q0czcyr3
  |   │   ├── simu_2025-05-13_17-38_qombpgv0
  |   │   └── summary_table.csv