BO-GP

Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations.

The BO-GP codes are developed using GPy and GPyOpt. The optimizer is non-intrusive and can be linked to any CFD solver.

Reference:

Y. Morita, S. Rezaeiravesh, N. Tabatabaeia, R. Vinuesaa, K. Fukagata, P. Schlatter, Applying Bayesian Optimization with Gaussian Process Regression to Computational Fluid Dynamics Problems, Journal of Computational Physics, 2021.

Exmaple: Turbulent boundary layer (TBL) with non-zero pressure gradient.

See Section 5 in the above reference. The flow is simulated using OpenFOAM.

Questions/Remarks:

Questions can be forwarded to [email protected], [email protected], and [email protected].

List of included files and folders:

• driver_BOGP.py: main driver for running the example, i.e. BO-GP of pessure-gradient TBL simulated by OpenFOAM.

• gpOptim/: Bayesian optimization codes based on Gaussian processes, using GPy and GPyOpt.

  • workDir/
    • gpList.dat
  • gpOpt.py

• OFcase/: OpenFOAM case folder

  • system/
    • yTopParams.in (written in main_pre.py, used by blockMeshDict & controlDict).
    • blockMeshDict
    • controlDict
    • decomposeParDict
    • fvSchemes
    • fvSolution
  • 0/
    • U,p,k,omega,nut
    • *_IC files (use inflow.py to make these files).
  • constant/
    • polyMesh/ (not included)
    • transportProperties
  • jobscript
  • OFrun.sh

• OFpost/: Post-processing the results of OFcase.

  • main_post.py

• OFpre/: Pre-processing the OFcase

  • main_pre.py: creating yTopParams.in using the latest parameter sample.
  • inflow/inflow_gen.py: Creating inflow conditions for RANS of TBL with pressure gradient using DNS data for the TBL with zero-pressure gradient.

• figs/: To save figures produced when running the optimization.

  • make_movie.sh: make movie in png/ from pdf files.

• data/: Created when running the BO-GP.

• storage/: Created when running the BO-GP.

Settings & inputs (to run the example):

• In driver_BOGP_example.py: U_infty, delta99_in, Nx, Ny, Nz, t, loop params, path, beta_t etc.
• /gpOptim/gpOpt.py: number of parameters, range of parameters, tolerance, GP kernel, xi, etc.

Requirements:

1. python3.X
2. numpy
3. matplotlib
4. GPy
5. GpyOpt
6. OpenFOAM v.7 (or v.6)
7. bl_data/ in OFpre/inflow/ (DNS data from here)

How to test the example for different settings:

• To change the structure of the geometry

  • create the new inflow from precursor using OFpre/inflow/inflow_gen.py (precursor results required)
  • update the blockMeshDict
  • update the driver accordingly

• To change the number of prosessors used for the OpenFOAM simulation

  • update nProcessors in the driver
  • update decomposeParDict
  • update jobScript

• To change the parameterization of the upper wall

  • change qBound in gpOpt.py
  • update blockMeshDict

• To change beta_t (target pressure-gradient parameter beta)

  • change beta_t in the driver

• When you clone this repository and get errors, please try run:

  • mkdir data
  • mkdir storage
  • mkdir OFcase/constant/polyMesh/