Scaling law: Difference between revisions
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The typical scaling law expression for a (dependent) variable ''y'' as a function of some (independent) system variables ''x<sub>1</sub>'', ''x<sub>2</sub>'',... is: | The typical scaling law expression for a (dependent) variable ''y'' as a function of some (independent) system variables ''x<sub>1</sub>'', ''x<sub>2</sub>'',... is: | ||
:<math>y = \alpha_0 x_1^{\alpha_1} x_2^{\alpha_1} ...</math> | :<math>y = e^{\alpha_0} x_1^{\alpha_1} x_2^{\alpha_1} ...</math> | ||
Here, the α<sub>i</sub> are the scaling parameters. | Here, the α<sub>i</sub> are the scaling parameters. | ||
By taking the logarithm of this expression, it becomes linear and simple (multivariate) linear regression tools can be used. | By taking the logarithm of this expression, it becomes linear and simple (multivariate) linear regression tools can be used to determine the parameters from a set of data. | ||
However, a proper analysis requires: | However, a proper analysis requires: | ||
* using ''dimensionless'' variables (easily achieved by normalizing all quantities appropriately) | * using ''dimensionless'' variables (easily achieved by normalizing all quantities appropriately) |
Revision as of 12:39, 11 September 2009
Scaling laws are an engineering tool to predict the performance of a system as a function of some significant parameters. [1] Its extended use in magnetic confinement physics reflects the fact that detailed transport calculations or predictions on first principles are difficult in this field. In the latter context, they are mainly used to
- predict the performance of new (larger) devices, such as ITER
- summarize large amounts of experimental data
- make performance comparisons between devices
- make educated guesses at local transport mechanisms
The typical scaling law expression for a (dependent) variable y as a function of some (independent) system variables x1, x2,... is:
Here, the αi are the scaling parameters. By taking the logarithm of this expression, it becomes linear and simple (multivariate) linear regression tools can be used to determine the parameters from a set of data. However, a proper analysis requires:
- using dimensionless variables (easily achieved by normalizing all quantities appropriately)
- guaranteeing the (linear) statistical independence of the independent variables (applying, e.g., Principal Component Analysis)
Confinement time scaling
The main performance parameter that is subjected to scaling law analysis is the energy confinement time, τE. The following are some of the most-used scalings for tokamaks: [2]
- L-mode scaling (H89P)
- ELMy H-mode scaling (HH98(y,2))
For stellarators, a similar scaling has been derived. [3] [4]
Power degradation
One of the remarkable and initially unexpected properties of magnetically confined plasmas is the reduction of the energy confinement time τE as the heating power P is increased. Typically:
where α has a value of 0.6 ± 0.1. The reason for this behaviour has not been fully clarified. However, it seems obvious that an increase of P will lead to an increase of (temperature and density) gradients, and thus an increase of "free energy" available to instabilities and turbulence. This then leads to an increase of transport, producing the observed confinement degradation. This phenomenon is therefore due to plasma self-organisation.
References
- ↑ O.J.W.F. Kardaun, Classical methods of statistics: with applications in fusion-oriented plasma physics, Springer Science & Business (2005) ISBN 3540211152
- ↑ ITER Physics Expert Groups on Confinement Modelling and Transport, Confinement Modelling and Database, and ITER Physics Basis Editors, Nucl. Fusion 39 (1999) 2137
- ↑ ISS-IPP and ISS-NIFS homepages
- ↑ A. Dinklage et al, Physical model assessment of the energy confinement time scaling in stellarators, Nuclear Fusion 47, 9 (2007) 1265-1273