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Extreme scale unstructured adaptive cfd for aerodynamic flow control

par Jansen, Kenneth;Rasquin, Michel ;Brown, Jed;Smith, Cameron;Shephard, Mark W.;Carothers, Chris
Référence Exascale Scientific Applications: Scalability and Performance Portability, CRC Press, page (319-344)
Publication Publié, 2017-01

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Résumé :

Understanding the flow of fluid, either liquid or gas, through and around solid bodies has challenged man since the dawn of scientific inquiry. Many of the great minds of science and mathematics have progressively built up a hierarchy of fluid models since fluid flow impacts our lives in so many fundamental ways—from the early days of flow of water in viaducts to cities, to today’s flight of planes. 320This chapter is concerned with the computational modeling of turbulent flow around aerodynamic bodies such as planes and wind turbines. In this case, viscous effects near the solid bodies create very thin boundary layers that yield highly anisotropic (gradients normal to the surface may be 106 larger than gradients along the surface) solutions to the governing nonlinear partial differential equations (PDEs): the Navier-Stokes equations. Furthermore, turbulent flows develop extremely broad ranges of length and timescales motivating the use of discretization methods capable of employing adaptivity and implicit time integration. The combination of these features—nonlinear, anisotropy, adaptivity, and implicit time integration—dramatically raise the complexity of the discretization posing large challenges to efficient scalable parallel implementation. However, through careful design, the more complex algorithms can provide great reductions in computational costs relative to simpler methods (e.g., Cartesian grids with explicit time integration) that are easier to mate efficiently to hardware. In this chapter, we not only describe our approach, but we also address the fact that while complex algorithms may never be as efficient flop-for-flop as simple methods, in the important measure of science-per-core-hour, they can still win big by making complex features such as adaptivity and implicit methods as efficient and scalable as possible.