Data-driven model development for large-eddy simulation of turbulence using gene-expression programing
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Li:2021:PoF,
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author = "Haochen Li2 and Yaomin Zhao and Jianchun Wang and
Richard D. Sandberg",
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title = "Data-driven model development for large-eddy
simulation of turbulence using gene-expression
programing",
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journal = "Physics of Fluids",
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year = "2021",
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volume = "33",
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number = "12",
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month = dec,
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note = "Special Collection: Artificial Intelligence in Fluid
Mechanics",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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DOI = "doi:10.1063/5.0076693",
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abstract = "We apply the gene-expression programing (GEP) method
to develop subgrid-scale models for large-eddy
simulations (LESs) of turbulence. The GEP model is
trained based on Galilean invariants and tensor basis
functions, and the training data are from direct
numerical simulation (DNS) of incompressible isotropic
turbulence. The model trained with GEP has been
explicitly tested, showing that the GEP model can not
only provide high correlation coefficients in a priori
tests but also show great agreement with filtered DNS
data when applied to LES. Compared to commonly used
models like the dynamic Smagorinsky model and the
dynamic mixed model, the GEP model provides
significantly improved results on turbulence statistics
and flow structures. Based on an analysis of the
explicitly given model equation, the enhanced
predictions are related to the fact that the GEP model
is less dissipative and that it introduces high-order
terms closely related to vorticity distribution.
Furthermore, the GEP model with the explicit equation
is straightforward to be applied in LESs, and its
additional computational cost is substantially smaller
than that of models trained with artificial neural
networks with similar levels of predictive accuracies
in a posteriori tests.",
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notes = "HEDPS, Center for Applied Physics and Technology, and
College of Engineering, Peking University, Beijing
100871, China",
- }
Genetic Programming entries for
Haochen Li2
Yaomin Zhao
Jianchun Wang
Richard D Sandberg
Citations