TURBULENCE IN ASTROPHYSICS - STARS

Citation
Vm. Canuto et J. Christensendalsgaard, TURBULENCE IN ASTROPHYSICS - STARS, Annual review of fluid mechanics, 30, 1998, pp. 167-198
Citations number
121
Categorie Soggetti
Phsycs, Fluid & Plasmas",Mechanics
ISSN journal
00664189
Volume
30
Year of publication
1998
Pages
167 - 198
Database
ISI
SICI code
0066-4189(1998)30:<167:TIA-S>2.0.ZU;2-6
Abstract
Turbulence is ubiquitous in astrophysics, ranging from cosmology, inte rstellar medium to stars, supernovae, accretion disks, etc. Large scal es and small viscosities combine to form large Reynolds numbers. Becau se it is not possible in a single article to review all the above scen arios, we limit ourselves to stars, in which thermal instabilities giv e rise to turbulent convection as the dominant heat transport mechanis m. (Accretion disks, where shear instabilities dominate the outward tr ansport of angular momentum, will be the subject of a second article, planned for Volume 31.) Because of the lack of a satisfactory theory, turbulence constitutes a bottleneck that prevents astrophysical models from being fully predictive. Because continued use of phenomenologica l turbulence expressions would make astrophysical models perennially u npredictive, a way must be found to make astrophysical models as progn ostic as possible. In addition to the difficulties brought about by tu rbulence, astrophysical settings introduce ''malicious conditions,'' o f which the most refractory to a satisfactory quantification are compr essibility (caused by the large density excursions that characterize c onvective zones in stars) and rotation. Basic understanding of how the y affect turbulence in general is still rather sketchy. Reasons for th e choice of stars and accretion disks as prototype examples are the fo llowing: The underlying instabilities are very basic; laboratory and d irect numerical simulations data help constrain theoretical models; an d new observational data, especially from helioseismology, help discri minate among different models with unprecedented accuracy.