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https://doi.org/10.1103/physre...
Article . 2021 . Peer-reviewed
License: APS Licenses for Journal Article Re-use
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Artificial compressibility method for strongly anisothermal low Mach number flows

Authors: Dorian Dupuy; Adrien Toutant; Françoise Bataille;

Artificial compressibility method for strongly anisothermal low Mach number flows

Abstract

Artificial compressibility methods aim to reduce the stiffness of the compressible Navier-Stokes equations by artificially decreasing the velocity of acoustic waves in the fluid. This approach has originally been developed as an alternative to the incompressible Navier-Stokes equations as this avoids the resolution of a Poisson equation. This paper extends the method to anisothermal low Mach number flows, allowing the simulations of subsonic flows submitted to large temperature variations, including dilatational effects. The procedure is shown to be stable and accurate using a finite-difference method in a staggered grid system for the simulation of strongly anisothermal turbulent channel flow. The highly scalable nature of the approach is well suited to complex high-fidelity simulations and GPU processing.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average