publication . Preprint . Article . Other literature type . 2019

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Geneva, Nicholas; Zabaras, Nicholas;
Open Access English
  • Published: 01 Apr 2019
Abstract
Comment: 47 pages, 21 figures, Accepted to the Journal of Computational Physics
Subjects
arXiv: Physics::Fluid Dynamics
free text keywords: Physics - Fluid Dynamics, Physics - Computational Physics, Statistics - Machine Learning, Physics and Astronomy (miscellaneous), Computer Science Applications, Uncertainty quantification, Monte Carlo method, Turbulence, Applied mathematics, Reynolds-averaged Navier–Stokes equations, Mathematical optimization, Reynolds number, symbols.namesake, symbols, Turbulence modeling, Reynolds stress, Computational fluid dynamics, business.industry, business, Mathematics
Related Organizations
Funded by
NSF| Graduate Research Fellowship Program (GRFP)
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1313583
  • Funding stream: Directorate for Education & Human Resources | Division of Graduate Education
44 references, page 1 of 3

e 0

H n 8 e

1 2 n 6 0

q e S in :5 0 H C

7 0 l [1] S. B. Pope, Turbulent ows, Cambridge University Press, Cambridge, 2000. [2] P. Spalart, S. Allmaras, A one-equation turbulence model for aerodynamic ows,

in: 30th Aerospace Sciences Meeting and Exhibit, 1992, p. 439. doi:https:

//doi.org/10.2514/6.1992-439. [3] P. Godin, D. Zingg, T. Nelson, High-lift aerodynamic computations with one-

and two-equation turbulence models, AIAA Journal 35 (2) (1997) 237{243. doi:

https://doi.org/10.2514/2.113. [4] W. Jones, B. Launder, The prediction of laminarization with a two-equation

model of turbulence, International Journal of Heat and Mass Transfer 15 (2)

(1972) 301{314. doi:https://doi.org/10.1016/0017-9310(72)90076-2.

0017931072900762 [5] B. Launder, B. Sharma, Application of the energy-dissipation model

ters in Heat and Mass Transfer 1 (2) (1974) 131{137. doi:https:

//doi.org/10.1016/0094-4548(74)90150-7.

0094454874901507 [6] D. C. Wilcox, et al., Turbulence modeling for CFD, Vol. 2, DCW industries La

44 references, page 1 of 3
Abstract
Comment: 47 pages, 21 figures, Accepted to the Journal of Computational Physics
Subjects
arXiv: Physics::Fluid Dynamics
free text keywords: Physics - Fluid Dynamics, Physics - Computational Physics, Statistics - Machine Learning, Physics and Astronomy (miscellaneous), Computer Science Applications, Uncertainty quantification, Monte Carlo method, Turbulence, Applied mathematics, Reynolds-averaged Navier–Stokes equations, Mathematical optimization, Reynolds number, symbols.namesake, symbols, Turbulence modeling, Reynolds stress, Computational fluid dynamics, business.industry, business, Mathematics
Related Organizations
Funded by
NSF| Graduate Research Fellowship Program (GRFP)
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1313583
  • Funding stream: Directorate for Education & Human Resources | Division of Graduate Education
44 references, page 1 of 3

e 0

H n 8 e

1 2 n 6 0

q e S in :5 0 H C

7 0 l [1] S. B. Pope, Turbulent ows, Cambridge University Press, Cambridge, 2000. [2] P. Spalart, S. Allmaras, A one-equation turbulence model for aerodynamic ows,

in: 30th Aerospace Sciences Meeting and Exhibit, 1992, p. 439. doi:https:

//doi.org/10.2514/6.1992-439. [3] P. Godin, D. Zingg, T. Nelson, High-lift aerodynamic computations with one-

and two-equation turbulence models, AIAA Journal 35 (2) (1997) 237{243. doi:

https://doi.org/10.2514/2.113. [4] W. Jones, B. Launder, The prediction of laminarization with a two-equation

model of turbulence, International Journal of Heat and Mass Transfer 15 (2)

(1972) 301{314. doi:https://doi.org/10.1016/0017-9310(72)90076-2.

0017931072900762 [5] B. Launder, B. Sharma, Application of the energy-dissipation model

ters in Heat and Mass Transfer 1 (2) (1974) 131{137. doi:https:

//doi.org/10.1016/0094-4548(74)90150-7.

0094454874901507 [6] D. C. Wilcox, et al., Turbulence modeling for CFD, Vol. 2, DCW industries La

44 references, page 1 of 3
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publication . Preprint . Article . Other literature type . 2019

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Geneva, Nicholas; Zabaras, Nicholas;