[JohnnyMercu]

Hi everyone,

I hope my post won't be off topic, here is my question :
I aml currently using Dakota 6.13 with OpenFoam 2012 in order to optimize parameters, and I am trying to build a surrogate-based optimization. The thing is I want to run several OpenFOAM in parallel to reduce the calculation time. Does anyone have already done something similar ? The OpenFOAM simulation I use runs on 6 processors and here is my dakota input file :

environment
 tabular_data

method
 id_method = 'SBGO'
 surrogate_based_global
   model_pointer = 'SURROGATE'
   method_pointer = 'SOGA'
   max_iterations = 15
   replace_points
 output verbose

method 
 id_method = 'SOGA'
 soga 
    seed = 10983
  max_function_evaluations = 30000
  #initialization_type unique_random
 output silent

model 
 id_model = 'SURROGATE'
 surrogate global
    dace_method_pointer = 'SAMPLING'
    #correction additive zeroth_order
    gaussian_process dakota

method
 id_method = 'SAMPLING'
 sampling
   samples = 50
   seed 531
   sample_type lhs
   model_pointer = 'TRUTH'

model
 id_model = 'TRUTH'
 single
   interface_pointer = 'TRUE_FN'

variables
  active uncertain
  uniform_uncertain 4 
   descriptors     'came_start' 'stiffness1'  'stiffness2' 'stiffness3'
   lower_bounds           -210          200           1200         2600
   upper_bounds           -150          400           1600         3000

interface
 id_interface = 'TRUE_FN'
 analysis_drivers = 'dakota3.sh'
   fork
    work_directory named 'workdir/run'
    directory_tag directory_save
    parameters_file 'params.in'
    results_file 'results.out'
    link_files 'dakotaParameter.orig' 'transfert.py' 'ecriture_xml_v3.py'

responses
  objective_functions=1 
   descriptors 'res' 
   no_gradients 
   no_hessians

   sense 'max'

Best regards,

Johnny