Non-diffusive transport: Difference between revisions
(Created page with 'It has long been known that the standard model for transport in magnetically confined plasmas (Neoclassical transport) often fails to provide an accurate description of exper…') |
(No difference)
|
Revision as of 09:14, 20 July 2009
It has long been known that the standard model for transport in magnetically confined plasmas (Neoclassical transport) often fails to provide an accurate description of experimental results: it tends to underestimate transport by one order of magnitude, typically. This is a very disappointing situation with a view to constructing a fusion reactor, since worse confinement means that an eventual reactor will need to be bigger and more expensive. Therefore, the search for the cause of this failure (and methods to restore transport to its Neoclassical value) is one of the main issues of fusion research.
The standard Neoclassical model is a diffusive model, which means that transport is characterised by typical scale lengths, both for space and time, so that the effective diffusion coefficient is essentially the mixing length value: , where is the typical step size and the typical waiting time.
In recent years, it has been suggested that the plasma may contain phenomena that invalidate this picture. There may turbulent eddies in which particle become trapped for some time, and there certainly are transport barriers, associated with rational magnetic surfaces. This could cause the waiting time distribution to beecome non-exponential. Likewise, the phenomenon of streamers, appearing in many models of plasma turbulence, could carry particles across long distances in the radial direction, and the distribution of particle steps could then also be deformed and develop long tails. Nobody knows exactly how important these phenomena are in the global transport picture.
Whatever the case, a well-established methodology exists to describe this deviation from standard diffusive transport (with characteristic scales): the Continuous Time Random Walk (CTRW) model.