Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-25T12:30:15.062Z Has data issue: false hasContentIssue false
  • English
  • Français

Star Formation with Adaptive Mesh Refinement Radiation Hydrodynamics

Published online by Cambridge University Press:  27 April 2011

Mark R. Krumholz
Mark R. Krumholz*
Affiliation:
Dept. of Astronomy & Astrophysics, University of California, Santa Cruz, Interdisciplinary Sciences Building, Santa Cruz, CA 95064, USA email: krumholz@ucolick.org
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

I provide a pedagogic review of adaptive mesh refinement (AMR) radiation hydrodynamics (RHD) methods and codes used in simulations of star formation, at a level suitable for researchers who are not computational experts. I begin with a brief overview of the types of RHD processes that are most important to star formation, and then I formally introduce the equations of RHD and the approximations one uses to render them computationally tractable. I discuss strategies for solving these approximate equations on adaptive grids, with particular emphasis on identifying the main advantages and disadvantages of various approximations and numerical approaches. Finally, I conclude by discussing areas ripe for improvement.

Keywords

hydrodynamicsmethods: numericalradiative transferstars: formation
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 6 , Symposium S270: Computational Star Formation , May 2010 , pp. 187 - 194
Copyright
Copyright © International Astronomical Union 2011

References

Abel, T. & Wandelt, B. D. 2002, MNRAS, 330, L53CrossRefGoogle Scholar
Bate, M. R. 2009, MNRAS, 392, 1363CrossRefGoogle Scholar
Commerçon, B., Hennebelle, P., Audit, E., Chabrier, G., & Teyssier, R. 2010, A&A, 510, L3+Google Scholar
Dale, J. E., Bonnell, I. A., Clarke, C. J., & Bate, M. R. 2005, MNRAS, 358, 291CrossRefGoogle Scholar
Fromang, S., Hennebelle, P., & Teyssier, R. 2006, A&A, 457, 371Google Scholar
Fryxell, B., et al. 2000, ApJS, 131, 273CrossRefGoogle Scholar
Goldsmith, P. F. 2001, ApJ, 557, 736CrossRefGoogle Scholar
González, M., Audit, E., & Huynh, P. 2007, A&A, 464, 429Google Scholar
Gritschneder, M., Naab, T., Walch, S., Burkert, A., & Heitsch, F. 2009, ApJL, 694, L26CrossRefGoogle Scholar
Hayashi, C. 1966, ARAA, 4, 171CrossRefGoogle Scholar
Hayes, J. C. & Norman, M. L. 2003, ApJS, 147, 197CrossRefGoogle Scholar
Howell, L. H. & Greenough, J. A. 2003, JCP, 184, 53Google Scholar
Jijina, J. & Adams, F. C. 1996, ApJ, 462, 874CrossRefGoogle Scholar
Kahn, F. D. 1974, A&A, 37, 149Google Scholar
Klein, R. I. 1999, J. Comp. App. Math., 109, 123CrossRefGoogle Scholar
Krumholz, M. R. 2006, ApJL, 641, L45CrossRefGoogle Scholar
Krumholz, M. R., Cunningham, A. J., Klein, R. I., & McKee, C. F. 2010, ApJ, 713, 1120CrossRefGoogle Scholar
Krumholz, M. R., Klein, R. I., & McKee, C. F. 2007 a, ApJ, 656, 959CrossRefGoogle Scholar
Krumholz, M. R., Klein, R. I., McKee, C. F., & Bolstad, J. 2007 b, ApJ, 667, 626CrossRefGoogle Scholar
Krumholz, M. R., Klein, R. I., McKee, C. F., Offner, S. S. R., & Cunningham, A. J. 2009, Science, 323, 754CrossRefGoogle Scholar
Krumholz, M. R. & McKee, C. F. 2008, Nature, 451, 1082CrossRefGoogle Scholar
Krumholz, M. R., Stone, J. M., & Gardiner, T. A. 2007 c, ApJ, 671, 518CrossRefGoogle Scholar
Kuiper, R., Klahr, H., Dullemond, C., Kley, W., & Henning, T. 2010, A&A, 511, A81+Google Scholar
Larson, R. B. 1969, MNRAS, 145, 271CrossRefGoogle Scholar
Larson, R. B. 2005, MNRAS, 359, 211CrossRefGoogle Scholar
Levermore, C. D. & Pomraning, G. C. 1981, ApJ, 248, 321CrossRefGoogle Scholar
Masunaga, H., Miyama, S. M., & Inutsuka, S. 1998, ApJ, 495, 346CrossRefGoogle Scholar
Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. 2007, ApJS, 170, 228CrossRefGoogle Scholar
Mihalas, D. & Klein, R. I. 1982, JCP, 46, 97Google Scholar
Mihalas, D. & Weibel-Mihalas, B. 1999, Foundations of Radiation Hydrodynamics (Mineola, New York: Dover)Google Scholar
Murray, S. D., Castor, J. I., Klein, R. I., & McKee, C. F. 1994, ApJ, 435, 631CrossRefGoogle Scholar
Nakano, T. 1989, ApJ, 345, 464CrossRefGoogle Scholar
Nakano, T., Hasegawa, T., & Norman, C. 1995, ApJ, 450, 183CrossRefGoogle Scholar
Norman, M. L., Bryan, G. L., Harkness, R., Bordner, J., Reynolds, D., O'Shea, B., & Wagner, R. 2008, in Petascale Computing: Algorithms and Applications, ed. Bader, D. A., Computational Science Series (Chapman & Hall/CRC), 83–101, arXiv:0705.1556Google Scholar
Offner, S. S. R., Klein, R. I., McKee, C. F., & Krumholz, M. R. 2009, ApJ, 703, 131CrossRefGoogle Scholar
Peters, T., Banerjee, R., Klessen, R. S., Mac Low, M., Galván-Madrid, R., & Keto, E. R. 2010, ApJ, 711, 1017CrossRefGoogle Scholar
Reynolds, D. R., Hayes, J. C., Paschos, P., & Norman, M. L. 2009, Journal of Computational Physics, 228, 6833CrossRefGoogle Scholar
Rijkhorst, E., Plewa, T., Dubey, A., & Mellema, G. 2006, A&A, 452, 907Google Scholar
Urban, A., Martel, H., & Evans, N. J. 2010, ApJ, 710, 1343CrossRefGoogle Scholar
Wolfire, M. G. & Cassinelli, J. P. 1987, ApJ, 319, 850CrossRefGoogle Scholar
Yorke, H. W. & Bodenheimer, P. 1999, ApJ, 525, 330CrossRefGoogle Scholar
Yorke, H. W. & Kruegel, E. 1977, A&A, 54, 183Google Scholar
Yorke, H. W. & Sonnhalter, C. 2002, ApJ, 569, 846CrossRefGoogle Scholar