Bayesian optimisation of RANS simulation with ensemble-based variational method in convergent-divergent channel

References

• Gronskis A, Heitz D, Mémin E. Inflow and initial conditions for direct numerical simulation based on adjoint data assimilation. J Comput Phys. 2013;242:480–497. doi: 10.1016/j.jcp.2013.01.051
   Web of Science ®Google Scholar
• DPG Foures, PJ Schmid. A data-assimilation method for reynolds-averaged Navier–Stokes-driven mean flow reconstruction. J Fluid Mech. 2014;759:404–431. doi: 10.1017/jfm.2014.566
   Web of Science ®Google Scholar
• Symon S, Dovetta N, McKeon BJ, et al. Data assimilation of mean velocity from 2D piv measurements of flow over an idealized airfoil. Exp Fluids. 2017;58(5):61. doi: 10.1007/s00348-017-2336-8
   Web of Science ®Google Scholar
• Colburn CH, Cessna JB, Bewley TR. State estimation in wall-bounded flow systems. part 3. The ensemble Kalman filter. J Fluid Mech. 2011;682:289–303. doi: 10.1017/jfm.2011.222
   Web of Science ®Google Scholar
• Kato H, Obayashi S. Approach for uncertainty of turbulence modeling based on data assimilation technique. Comput Fluids. 2013;85:2–7. doi: 10.1016/j.compfluid.2012.09.002
   Web of Science ®Google Scholar
• Kato H, Yoshizawa A, Ueno G, et al. A data assimilation methodology for reconstructing turbulent flows around aircraft. J Comput Phys. 2015;283:559–581. doi: 10.1016/j.jcp.2014.12.013
   Web of Science ®Google Scholar
• Xiao H, Wu J-L, Wang J-X, et al. Quantifying and reducing model-form uncertainties in reynolds-averaged Navier–Stokes simulations: a data-driven, physics-informed bayesian approach. J Comput Phys. 2016;324:115–136. doi: 10.1016/j.jcp.2016.07.038
   Web of Science ®Google Scholar
• Mons V, Chassaing J-C, Gomez T, et al. Reconstruction of unsteady viscous flows using data assimilation schemes. J Comput Phys. 2016;316:255–280. doi: 10.1016/j.jcp.2016.04.022
   Web of Science ®Google Scholar
• Menter FR. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J. 1994;32(8):1598–1605. doi: 10.2514/3.12149
   Web of Science ®Google Scholar
• Wu X. Inflow turbulence generation methods. Annu Rev Fluid Mech. 2017;49:23–49. doi: 10.1146/annurev-fluid-010816-060322
   Web of Science ®Google Scholar
• Marquillie M, Laval J-P, Dolganov R. Direct numerical simulation of a separated channel flow with a smooth profile. J Turbul. 2008;9:N1. doi: 10.1080/14685240701767332
   Web of Science ®Google Scholar
• Jesus A, Azevedo JL, Laval J-P. Large eddy simulations and rans computations of adverse pressure gradient flows. In: 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. 2013. p. 267.
   Google Scholar
• Kuban L, Laval J-P, Elsner W, et al. Les modeling of converging-diverging turbulent channel flow. J Turbul. 2012;13:N11. doi: 10.1080/14685248.2012.661062
   Web of Science ®Google Scholar
• Laval J-P, Braud C, Fournier G, et al. Large-eddy simulations of control of a separated flow over a 2D bump by means of pulsed jets. J Turbul. 2010;11:N52. doi: 10.1080/14685248.2010.522579
   Web of Science ®Google Scholar
• Charrière B, Goncalves E. Numerical investigation of periodic cavitation shedding in a venturi. Int J Heat Fluid Flow. 2017;64:41–54. doi: 10.1016/j.ijheatfluidflow.2017.01.011
   Web of Science ®Google Scholar
• Coutier-Delgosha O, Reboud JL, Delannoy Y. Numerical simulation of the unsteady behaviour of cavitating flows. Int J Numer Methods Fluids. 2003;42(5):527–548.
   Web of Science ®Google Scholar
• Khlifa I, Vabre A, Hočevar M, et al. Fast x-ray imaging of cavitating flows. Exp Fluids. 2017;58(11):157. doi: 10.1007/s00348-017-2426-7
   Web of Science ®Google Scholar
• Singh AP, Duraisamy K. Using field inversion to quantify functional errors in turbulence closures. Phys Fluids. 2016;28(4):045110. doi: 10.1063/1.4947045
   Web of Science ®Google Scholar
• Wilcox DC. Turbulence modeling for CFD. Vol. 2. La Canada (CA): DCW industries; 1998.
   Google Scholar
• Yang Y, Robinson C, Heitz D, et al. Enhanced ensemble-based 4DVar scheme for data assimilation. Comput Fluids. 2015;115:201–210. doi: 10.1016/j.compfluid.2015.03.025
   Web of Science ®Google Scholar
• Singh AP, Medida S, Duraisamy K. Machine-learning-augmented predictive modeling of turbulent separated flows over airfoils. AIAA Journal. 2017;55:2215–2227. doi: 10.2514/1.J055595
   Web of Science ®Google Scholar