Incompact3d: A powerful tool to tackle turbulence problems with up to O(10 5 ) computational cores. Understanding the nature of complex turbulent flows remains one of the most challenging problems in classical physics. Significant progress has been made recently using high performance computing, and computational fluid dynamics is now a credible alternative to experiments and theories in order to understand the rich physics of turbulence. In this paper, we present an efficient numerical tool called Incompact3d that can be coupled with massive parallel platforms in order to simulate turbulence problems with as much complexity as possible, using up to O(10 5 ) computational cores by means of direct numerical simulation (DNS). DNS is the simplest approach conceptually to investigate turbulence, featuring the highest temporal and spatial accuracy and it requires extraordinary powerful resources. This paper is an extension of Laizet et al.(Comput. Fluids 2010; 39(3):471 – 484) where the authors proposed a strategy to run DNS with up to 1024 computational cores

References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Xiao, Heng; Wu, Jin-Long; Laizet, Sylvain; Duan, Lian: Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations (2020)
  2. Angeli, D.; Stalio, E.: A fast algorithm for direct numerical simulation of turbulent convection with immersed boundaries (2019)
  3. Moise, Pradeep; Mathew, Joseph: Bubble and conical forms of vortex breakdown in swirling jets (2019)
  4. Wu, Jin-Long; Sun, Rui; Laizet, Sylvain; Xiao, Heng: Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling (2019)
  5. Wu, Jinlong; Xiao, Heng; Sun, Rui; Wang, Qiqi: Reynolds-averaged Navier-Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (2019)
  6. Zhou, Yi; Nagata, Koji; Sakai, Yasuhiko; Watanabe, Tomoaki: Extreme events and non-Kolmogorov (-5/3) spectra in turbulent flows behind two side-by-side square cylinders (2019)
  7. Chandramouli, Pranav; Heitz, Dominique; Laizet, Sylvain; Mémin, Etienne: Coarse large-eddy simulations in a transitional wake flow with flow models under location uncertainty (2018)
  8. Yao, Jie; Chen, Xi; Hussain, Fazle: Drag control in wall-bounded turbulent flows via spanwise opposed wall-jet forcing (2018)
  9. Capuano, F.; Mastellone, A.; De Angelis, E. M.: A conservative overlap method for multi-block parallelization of compact finite-volume schemes (2017)
  10. Dairay, Thibault; Lamballais, Eric; Laizet, Sylvain; Vassilicos, John Christos: Numerical dissipation vs. subgrid-scale modelling for large eddy simulation (2017)
  11. Francisco, Ezequiel P.; Espath, L. F. R.; Silvestrini, J. H.: Direct numerical simulation of bi-disperse particle-laden gravity currents in the channel configuration (2017)
  12. Abdelsamie, Abouelmagd; Fru, Gordon; Oster, Timo; Dietzsch, Felix; Janiga, Gábor; Thévenin, Dominique: Towards direct numerical simulations of low-Mach number turbulent reacting and two-phase flows using immersed boundaries (2016)
  13. Gronskis, Alejandro; Artana, Guillermo: A simple and efficient direct forcing immersed boundary method combined with a high order compact scheme for simulating flows with moving rigid boundaries (2016)
  14. He, Ping: A high order finite difference solver for massively parallel simulations of stably stratified turbulent channel flows (2016)
  15. Motheau, E.; Abraham, J.: A high-order numerical algorithm for DNS of low-Mach-number reactive flows with detailed chemistry and quasi-spectral accuracy (2016)
  16. Bauer, F.; Tardu, S.; Doche, O.: Efficiency of high accuracy DRP schemes in direct numerical simulations of incompressible turbulent flows (2015)
  17. Fuka, V.: PoisFFT -- a free parallel fast Poisson solver (2015)
  18. Gronskis, A.; Heitz, D.; Mémin, E.: Inflow and initial conditions for direct numerical simulation based on adjoint data assimilation (2013)
  19. Laizet, Sylvain; Li, Ning: Incompact3d: A powerful tool to tackle turbulence problems with up to (O(10^5)) computational cores (2011)
  20. Laizet, Sylvain; Vassilicos, John Christos: DNS of fractal-generated turbulence (2011)

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