DNS of Compressible Turbulent Boundary Layers with Adverse Pressure Gradients

Christoph Wenzel (), Johannes M. F. Peter (), Björn Selent (), Matthias B. Weinschenk (), Ulrich Rist () and Markus J. Kloker ()
Additional contact information
Christoph Wenzel: Universität Stuttgart, Institut für Aero- und Gasdynamik
Johannes M. F. Peter: Universität Stuttgart, Institut für Aero- und Gasdynamik
Björn Selent: Universität Stuttgart, Institut für Aero- und Gasdynamik
Matthias B. Weinschenk: Universität Stuttgart, Institut für Aero- und Gasdynamik
Ulrich Rist: Universität Stuttgart, Institut für Aero- und Gasdynamik
Markus J. Kloker: Universität Stuttgart, Institut für Aero- und Gasdynamik

A chapter in High Performance Computing in Science and Engineering ' 18, 2019, pp 229-242 from Springer

Abstract: Abstract First direct-numerical-simulation results of compressible subsonic adverse-pressure-gradient turbulent boundary-layers are presented with self-similar boundary-layer profiles in their streamwise evolution, which represents the most general canonical form of the adverse pressure gradient case. Only few results are available in literature for this problem even in the incompressible regime, since the achievement of such canonical flows requires very costly iterative procedures in order to find the correct pressure distribution which has to be prescribed at the top of the simulation domain. Additionally, preliminary result are presented for the detection of turbulent superstructures in the unsteady flow field, which can be denoted to be the most dominant global events in wall-bounded turbulent flows. All results have been calculated with a new version of our numerical in-house code NS3D, which now also allows MPI decomposition in the spanwise direction of the simulation domain. A scaling study shows a maximum increase in the efficiency of up to 300% compared to the previous code version where only OpenMP decomposition has been available for the spanwise decomposition.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-13325-2_14

Ordering information: This item can be ordered from
http://www.springer.com/9783030133252

DOI: 10.1007/978-3-030-13325-2_14

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().