Skip to content

A python toolset to augment RANS models with LES/DNS data, using Random or Mondrian forests.

License

Notifications You must be signed in to change notification settings

ascillitoe/mondrian_turbulence

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

57 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Data-driven turbulence modelling with random and Mondrian forests

This python package trains random forests and Mondrian forests on high fidelity LES/DNS data. The trained models can then be used to predict turbulence parameters for a new RANS flowfield. For more details see:

Ashley Scillitoe, Pranay Seshadri, Mark Girolami, Uncertainty quantification for data-driven turbulence modelling with mondrian forests, Journal of Computational Physics, 2021, 110116, ISSN 0021-9991, doi: 10.1016/j.jcp.2021.110116. arXiv: 2003.01968.

How to use

Instructions and examples coming soon!

Notes

  • Regressors and classifiers are implemented, however the classifer code is out of date and should be used with caution!
  • requirements.txt file to enable easy installation is in the works.

Key dependencies

Acknowledgments

This work was supported by wave 1 of The UKRI Strategic Priorities Fund under the EPSRC grant EP/T001569/1, particularly the Digital Twins in Aeronautics theme within that grant, and The Alan Turing Institute.

About

A python toolset to augment RANS models with LES/DNS data, using Random or Mondrian forests.

Topics

Resources

License

Stars

Watchers

Forks

Languages