Application of Gene Expression Programming to a-posteriori LES modeling of a Taylor Green Vortex

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{journals/jcphy/ReissmannHSK21,

• author = "Maximilian Reissmann and Josef Hasslberger and Richard D. Sandberg and Markus Klein",
• title = "Application of Gene Expression Programming to a-posteriori {LES} modeling of a {Taylor} Green Vortex",
• journal = "Journal of Computational Physics",
• year = "2021",
• volume = "424",
• pages = "109859",
• keywords = "genetic algorithms, genetic programming, gene expression programming, evolutionary algorithm, in-the-loop optimisation, a-posteriori les, Taylor green vortex",
• ISSN = "0021-9991",
• bibdate = "2020-12-17",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jcphy/jcphy424.html#ReissmannHSK21",
• URL = "https://www.sciencedirect.com/science/article/pii/S0021999120306331",
• DOI = "doi:10.1016/j.jcp.2020.109859",
• abstract = "Gene Expression Programming (GEP), a branch of machine learning, is based on the idea to iteratively improve a population of candidate solutions using an evolutionary process built on the survival-of-the-fittest concept. The GEP approach was initially applied with encouraging results to the modeling of the unclosed tensors in the context of RANS (Reynolds Averaged Navier-Stokes) turbulence modelling. In a subsequent study it was demonstrated that the GEP concept can also be successfully used for modelling the unknown Sub-Grid Stress (SGS) tensor in the context of Large Eddy Simulations (LES). This was done in an a-priori analysis, where an existing Direct Numerical Simulation (DNS) database was explicitly filtered to evaluate the unknown stresses and to assess the performance of model candidates suggested by GEP. This paper presents the next logical step, i.e. the application of GEP to a-posteriori LES model development. Because a-posteriori analysis, using in-the-loop optimisation, is considered the ultimate way to test SGS models, this can be considered an important milestone for the application of machine learning to LES based turbulence modelling. GEP is here used to train LES models for simulating a Taylor Green Vortex (TGV) and results are compared with existing standard models. It is shown that GEP finds a model that outperforms known models from literature as well as the no-model LES. Although the performance of this best model is maintained for resolutions and Reynolds numbers different from the training data, this is not automatically guaranteed for all other models suggested by the algorithm.",

}

Genetic Programming entries for Maximilian Reissmann Josef Hasslberger Richard D Sandberg Markus Klein

Citations