Multi-scale high-performance fluid flow: Simulations through porous media. (January 2017)

• Record Type: Journal Article
• Authors: Perović, Nevena; Frisch, Jérôme; Salama, Amgad; Sun, Shuyu; Rank, Ernst; Mundani, Ralf-Peter
• Is Part Of: Advances in engineering software. Volume 103(2017)
• Page Start: 85
• Page End: 98
• Publication Date: 2017-01
• Subjects: High-performance computing -- Porous media -- Interactive data exploration -- Multi-grid-like solver -- Parallel computing -- Multi-scale approach -- Hierarchical data structure; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2016.07.016
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Generation of randomly distributed geometrically complex porous media samples. Hierarchical data structure and fast multigrid-like solver. Computation on high-performance computing clusters (more than 100.000 processes). Multi-scale approach based on coupling of Darcy and Navier-Stokes equations. Interactive data exploration during the runtime of the simulation. Abstract: Computational fluid dynamic (CFD) calculations on geometrically complex domains such as porous media require high geometric discretisation for accurately capturing the tested physical phenomena. Moreover, when considering a large area and analysing local effects, it is necessary to deploy a multi-scale approach that is both memory-intensive and time-consuming. Hence, this type of analysis must be conducted on a high-performance parallel computing infrastructure. In this paper, the coupling of two different scales based on the Navier–Stokes equations and Darcy's law is described followed by the generation of complex geometries, and their discretisation and numerical treatment. Subsequently, the necessary parallelisation techniques and a rather specific tool, which is capable of retrieving data from the supercomputing servers and visualising them during the computation runtime (i.e. in situ) are described. All advantages and possible drawbacks of this approach, together with the preliminary results and sensitivity analyses are discussed in detail.