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Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier--Stokes (RANS) equations.
The use of a contraction to improve the isotropy of grid-generated turbulence
G. Comte-Bellot and S. Corrsin · 1966
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Bounds for turbulent shear flow
F. Busse · 1970
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Bounds on flow quantities
L. N. Howard · 1972
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Progress in the development of a reynolds-stress turbulence closure
B. Launder, G. J. Reece, and W. Rodi · 1975
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A more general effective-viscosity hypothesis
S. Pope · 1975
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Realizability of Reynolds-stress turbulence models
U. Schumann · 1977
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Computational modeling of turbulent flows
J. L. Lumley · 1978
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PDF methods for turbulent reactive flows
S. Pope · 1985
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Turbulence statistics in fully developed channel flow at low reynolds number
J. Kim, P. Moin, and R. Moser · 1987
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A one-equation turbulence model for aerodynamic flows
P. Spalart and S. Allmaras · 1992
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ERCOFTAC classic database, 1993
J. Coupland · 1993
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Variational bounds on energy dissipation in incompressible flows: Shear flow
C. R. Doering and P. Constantin · 1994
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Realizability of second-moment closure via stochastic analysis
P. Durbin and C. Speziale · 1994
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Coarse-grained molecular dynamics and the atomic limit of finite elements
R. E. Rudd and J. Q. Broughton · 1998
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Nonlinear eddy viscosity and algebraic stress models for solving complex turbulent flows
T. Gatski and T. Jongen · 2000
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Turbulent Flows
S. B. Pope · 2000
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Turbulence modeling in rotating and curved channels: assessing the spalart-shur correction
M. L. Shur, M. K. Strelets, A. K. Travin, and P. R. Spalart · 2000
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Bayesian calibration of computer models
M. C. Kennedy and A. O’Hagan · 2001
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Algorithm developments for discrete adjoint methods
M. B. Giles, M. C. Duta, J.-D. M-uacute, ller, and N. A. Pierce · 2003
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Worlds of flow: A history of hydrodynamics from the Bernoullis to Prandtl
O. Darrigol · 2005
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Partially-averaged navier-stokes model for turbulence: A Reynolds-averaged Navier–Stokes to direct numerical simulation bridging method
S. S. Girimaji · 2006
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Turbulence modeling for CFD
D. C. Wilcox · 2006
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Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches
S. Banerjee, R. Krahl, F. Durst, and C. Zenger · 2007
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Reynolds stress closure for nonequilibrium effects in turbulent flows
P. E. Hamlington and W. J. Dahm · 2008
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A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
Y. Li, E. Perlman, M. Wan, Y. Yang, C. Meneveau, R. Burns, S. Chen, A. Szalay, and G. Eyink · 2008
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Uncertainty quantification for RANS turbulence model predictions
T. Oliver and R. Moser · 2009
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Distilling free-form natural laws from experimental data
M. Schmidt and H. Lipson · 2009
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Detached-eddy simulation
P. R. Spalart · 2009
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Parameter estimation and inverse problems , volume 90
R. C. Aster, B. Borchers, and C. H. Thurber · 2011
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Bayesian uncertainty analysis with applications to turbulence modeling
S. H. Cheung, T. A. Oliver, E. E. Prudencio, S. Prudhomme, and R. D. Moser · 2011
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Quantification of structural uncertainties in the k k – ω \omega turbulence model
E. Dow and Q. Wang · 2011
Cited alongside, same era.
Bayesian parameter estimation of a k k – ε \varepsilon model for accurate jet-in-crossflow simulations
J. Ray, S. Lefantzi, S. Arunajatesan, and L. Dechant · 2016
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Using field inversion to quantify functional errors in turbulence closures
A. P. Singh and K. Duraisamy · 2016
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A methodology to evaluate statistical errors in DNS data of plane channel flows
R. L. Thompson, L. E. B. Sampaio, F. A. V. de Bragança A., L. Thais, and G. Mompean · 2016
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A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship
J. Weatheritt and R. Sandberg · 2016
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A Bayesian calibration–prediction method for reducing model-form uncertainties with application in RANS simulations
J.-L. Wu, J.-X. Wang, and H. Xiao · 2016
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Modeling structural uncertainties in Reynolds-averaged computations of shock/boundary layer interactions
M. Emory, R. Pecnik, and G. Iaccarino · 2011
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Bayesian uncertainty quantification applied to RANS turbulence models
T. A. Oliver and R. D. Moser · 2011
Cited alongside, same era.
Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures
M. Emory, J. Larsson, and G. Iaccarino · 2013
Cited alongside, same era.
Ensemble Kalman methods for inverse problems
M. A. Iglesias, K. J. Law, and A. M. Stuart · 2013
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Application of supervised learning to quantify uncertainties in turbulence and combustion modeling
B. Tracey, K. Duraisamy, and J. Alonso · 2013
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The deviation from parallel shear flow as an indicator of linear eddy-viscosity model inaccuracy
C. Gorlé, J. Larsson, M. Emory, and G. Iaccarino · 2014
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Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed bayesian approach
H. Xiao, J.-L. Wu, J.-X. Wang, R. Sun, and C. J. Roy · 2016
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Some recent developments in turbulence closure modeling
P. A. Durbin · 2017
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Data-free and data-driven rans predictions with quantified uncertainty
W. N. Edeling, G. Iaccarino, and P. Cinnella · 2017
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Reynolds stress anisotropy in self-preserving turbulent shear flows
B. Eisfeld · 2017
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Searching for turbulence models by artificial neural network
M. Gamahara and Y. Hattori · 2017
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Eigenspace perturbations for uncertainty estimation of single-point turbulence closures
G. Iaccarino, A. A. Mishra, and S. Ghili · 2017
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A neural network approach for the blind deconvolution of turbulent flows
R. Maulik and O. San · 2017
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Uncertainty estimation for reynolds-averaged navier–stokes predictions of high–speed aircraft nozzle jets
A. A. Mishra and G. Iaccarino · 2017
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M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2017
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Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures
A. Vollant, G. Balarac, and C. Corre · 2017
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Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
J.-X. Wang, J.-L. Wu, and H. Xiao · 2017
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The development of algebraic stress models using a novel evolutionary algorithm
J. Weatheritt and R. D. Sandberg · 2017
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A random matrix approach for quantifying model-form uncertainties in turbulence modeling
H. Xiao, J.-X. Wang, and R. G. Ghanem · 2017
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A framework for characterizing structural uncertainty in large-eddy simulation closures
L. Jofre, S. P. Domino, and G. Iaccarino · 2018
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Data-driven Discovery of Closure Models
S. Pan and K. Duraisamy · 2018
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The common mechanism of turbulent skin-friction drag reduction with superhydrophobic longitudinal microgrooves and riblets
A. Rastegari and R. Akhavan · 2018
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Learning an eddy viscosity model using shrinkage and Bayesian calibration: A jet-in-crossflow case study
J. Ray, S. Lefantzi, S. Arunajatesan, and L. Dechant · 2018
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J.-L. Wu, R. Sun, S. Laizet, and H. Xiao · 2018
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Quantification of model uncertainty in RANS simulations: a review
H. Xiao and P. Cinnella. 2018 · 2018
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