Dissipative hydrodynamics of relativistic shock waves in a quark gluon plasma: Comparing and benchmarking alternate numerical methods

A. Gabbana, S. Plumari, G. Galesi, V. Greco, D. Simeoni, S. Succi, R. Tripiccione

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

This paper presents numerical cross comparisons and benchmark results for two different kinetic numerical methods, capable of describing relativistic dissipative fluid dynamics in a wide range of kinematic regimes, typical of relevant physics applications, such as transport phenomena in quark-gluon plasmas. We refer to relativistic lattice Boltzmann versus Monte Carlo test-particle methods. Lacking any realistic option for accurate validation vis-á-vis experimental data, we check the consistency of our results against established simulation packages available in the literature. We successfully compare the results of the two aforementioned numerical approaches for momentum integrated quantities like the hydrostatic and dynamical pressure profiles, the collective flow, and the heat flux. These results corroborate the confidence of the robustness and correctness of these computational methods and on the accurate calibration of their numerical parameters with respect to the physical transport coefficients. Our numerical results are made available as Supplemental Material, with the aim of establishing a reference benchmark for other numerical approaches.

Original languageEnglish
Article number064904
JournalPhysical Review C
Volume101
Issue number6
DOIs
Publication statusPublished - Jun 2020
Externally publishedYes

Funding

D.S. has been supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 765048. S.S. acknowledges funding from the European Research Council under the European Union's Horizon 2020 framework programme (No. P/2014-2020)/ERC Grant Agreement No. 739964 (COPMAT). Numerical work has been performed on the COKA computing cluster at Universita di Ferrara, the computing cluster GR4-LNS at the INFN-LNS, and the QGPDyn cluster at the University of Catania.

FundersFunder number
Università di Ferrara
European Union's Horizon 2020 - Research and Innovation Framework Programme739964
University of Catania

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