Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

@inproceedings{Wang2017PhysicsinformedML,
  title={Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data},
  author={Jian-Xun Wang and Jin-long Wu and Heng Xiao},
  year={2017}
}
Turbulence modeling is a critical component in numerical simulations of industrial flows based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of efforts in the turbulence modeling community, universally applicable RANS models with predictive capabilities are still lacking. Large discrepancies in the RANS-modeled Reynolds stresses are the main source that limits the predictive accuracy of RANS models. Identifying these discrepancies is of significance to possibly… CONTINUE READING

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References

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Random Forests

  • Machine Learning
  • 2001
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Physics-informed machine learning for predictive turbulence modeling: Progress and perspectives

H. Xiao, J.-L. Wu, J.-X. Wang, E. G. Paterson
  • Proceedings of the 2017 AIAA SciTech, In press
  • 2017
VIEW 1 EXCERPT

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