IncompressibleNavierStokes.jl

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References ​

Bibliography ​

  1. D. Kochkov, J. A. Smith, A. Alieva, Q. Wang, M. P. Brenner and S. Hoyer. Machine learning-accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences 118 (2021).

  2. M. Kurz, P. Offenhäuser and A. Beck. Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations, arXiv (2022).

  3. B. List, L.-W. Chen and N. Thuerey. Learned Turbulence Modelling with Differentiable Fluid Solvers, arxiv:2202.06988 (2022).

  4. J. F. MacArt, J. Sirignano and J. B. Freund. Embedded training of neural-network sub-grid-scale turbulence models (2021).

  5. J. Li and P. M. Carrica. A simple approach for vortex core visualization, arXiv:1910.06998 (2019), arXiv:1910.06998 [physics.flu-dyn].

  6. J. Jeong and F. Hussain. On the identification of a vortex. J. Fluid. Mech. 285, 69–94 (1995).

  7. S. B. Pope. Turbulent Flows (Cambridge University Press, Cambridge, England, 2000).

  8. B. Sanderse and F. X. Trias. Energy-consistent discretization of viscous dissipation with application to natural convection flow (Jul 2023), arXiv:2307.10874v1 [physics.flu-dyn].

  9. M. H. Silvis, R. A. Remmerswaal and R. Verstappen. Physical consistency of subgrid-scale models for large-eddy simulation of incompressible turbulent flows. Physics of Fluids 29 (2017).

  10. F. X. TRIAS, M. SORIA, A. OLIVA and C. D. PÉREZ-SEGARRA. Direct numerical simulations of two- and three-dimensional turbulent natural convection flows in a differentially heated cavity of aspect ratio 4. Journal of Fluid Mechanics 586, 259–293 (2007).

  11. P. Orlandi. Fluid flow phenomena: a numerical toolkit. Vol. 55 (Springer Science & Business Media, 2000).

  12. O. San and A. E. Staples. High-order methods for decaying two-dimensional homogeneous isotropic turbulence. Computers & Fluids 63, 105–127 (2012).

  13. F. H. Harlow and J. E. Welch. * Numerical Calculation of Time‐Dependent Viscous Incompressible Flow of Fluid with Free Surface *. The Physics of Fluids 8, 2182–2189 (1965), arXiv:https://aip.scitation.org/doi/pdf/10.1063/1.1761178.

  14. R. W. Verstappen and A. E. Veldman. Symmetry-Preserving Discretization of Turbulent Flow. J. Comput. Phys. 187, 343–368 (2003).

  15. B. Sanderse, R. Verstappen and B. Koren. Boundary treatment for fourth-order staggered mesh discretizations of the incompressible Navier–Stokes equations. Journal of Computational Physics 257, 1472–1505 (2014). Physics-compatible numerical methods.

  16. R. Verstappen and A. Veldman. Direct Numerical Simulation of Turbulence at Lower Costs. Journal of Engineering Mathematics 32, 143–159 (1997).

  17. B. Sanderse and B. Koren. Accuracy analysis of explicit Runge–Kutta methods applied to the incompressible Navier–Stokes equations. Journal of Computational Physics 231, 3041–3063 (2012).

  18. B. Sanderse. Energy-conserving Runge–Kutta methods for the incompressible Navier–Stokes equations. Journal of Computational Physics 233, 100–131 (2013).