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Cluster turbulence

  • Michael L. Norman
  • Greg L. Bryan
Conference paper
Part of the Lecture Notes in Physics book series (LNP, volume 530)

Abstract

We report on results of recent, high resolution hydrodynamic simulations of the formation and evolution of X-ray clusters of galaxies carried out within a cosmological framework. We employ the highly accurate piecewise parabolic method (PPM) on fixed and adaptive meshes which allow us to resolve the flow field in the intracluster gas. The excellent shock capturing and low numerical viscosity of PPM represent a substantial advance over previous studies using SPH. We find that in flat, hierarchical cosmological models, the ICM is in a turbulent state long after turbulence generated by the last major merger should have decayed away. Turbulent velocities are found to vary slowly with cluster radius, being ∼ 25% of σ vir in the core, increasing to ∼ 60% at the virial radius. We argue that more frequent minor mergers maintain the high level of turbulence found in the core where dynamical times are short. Turbulent pressure support is thus significant throughout the cluster, and results in a somewhat cooler cluster (T/T vir ∼ .8) for its mass. Some implications of cluster turbulence are discussed.

Keywords

Dark Matter Velocity Dispersion Cluster Core Bulk Motion Accretion Shock 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Anninos, P. & Norman, M. L. 1996. ApJ, 459, 12.CrossRefADSGoogle Scholar
  2. Böhringer, H., Nulsen, P., Braun, R. & Fabian, A. 1995. MNRAS, 274, L67.Google Scholar
  3. Briel, U. et al. 1991. A&A, 246, L10.Google Scholar
  4. Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R. & Ostriker, J. P. 1995. Comp. Phys. Comm., 89, 149.zbMATHCrossRefADSGoogle Scholar
  5. Bryan, G. L. & Norman, M. L. 1997. in Computational Astrophysics, PASP Conference Series Vol. 123, eds. D. Clarke & M. West, (PASP: San Francisco), 363.Google Scholar
  6. Bryan, G. L. & Norman, M. L. 1998. to appear in Structured Adaptive Mesh Refinement Grid Methods, ed. N. Chrisichoides, IMA Conference Series, in press (astro-ph/9710187).Google Scholar
  7. Bryan, G. L. & Norman, M. L. 1998a. ApJ, in press (astro-ph/9710107).Google Scholar
  8. Bryan, G. L. & Norman, M. L. 1998b. NewA, submitted.Google Scholar
  9. Dressler, A. & Schectman, S. 1988. AJ, 95, 985.CrossRefADSGoogle Scholar
  10. Evrard, A., Mohr, J., Fabricant, D. & Geller, M. 1993. ApJ, 419, L9.Google Scholar
  11. Frenk, C. S. et al. 1998. ApJ, in press.Google Scholar
  12. Geller, M. & Beers, 1982. PASP, 94, 421.CrossRefADSGoogle Scholar
  13. Lacey, C. & Cole, S. 1993. MNRAS, 262, 627.ADSGoogle Scholar
  14. Mac Low, M.-M., Klessen, R., Burkert, A. & Smith, M. D. 1998. Phys. Rev. Lett., submitted (astro-ph/9712013).Google Scholar
  15. Mohr, J., Evrard, A., Fabricant, D. & Geller, M. 1995. ApJ, 447, 8.CrossRefADSGoogle Scholar
  16. Navarro, J., Frenk, C. & White, S. 1995. MNRAS, 275, 720.ADSGoogle Scholar
  17. Ostriker, J. P. 1993. ARAA, 31, 689.ADSCrossRefGoogle Scholar
  18. Roettiger, K., Burns, J. O. & Loken, C. 1996. ApJ, 473, 651.CrossRefADSGoogle Scholar
  19. Roettiger, K., Stone, J. M. & Mushotsky, R. 1998. ApJ, 493, 62.CrossRefADSGoogle Scholar
  20. Schindler, S. & Müller, E. 1993.Google Scholar
  21. Stone, J. M. & Norman, M. L. 1992. ApJ, 389, L17.Google Scholar
  22. Tsai, J. C. & Buote, D. A. 1996. MNRAS, 282, 77.ADSGoogle Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • Michael L. Norman
    • 1
    • 2
  • Greg L. Bryan
    • 3
  1. 1.Astronomy Department and NCSAUniversity of IllinoisUrbanaUSA
  2. 2.Max-Planck-Institut für AstrophysikGarchingGermany
  3. 3.Princeton University ObservatoryPrincetonUSA

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