DIMENSIONALLY CONSTRAINED ENERGY CONFINEMENT ANALYSIS OF W7-AS DATA

A recently assembled W7-AS stellarator database has been subject to di mensionally constrained confinement analysis. The analysis employs Bay esian inference. Dimensional information is taken from the Conner-Tayl or (CT) similarity transformation theory, which provides six possible physical scenarios with associated dimensional conditions. Bayesian th eory allows the calculation of the probability for each model and it i s found that the present W7-AS data are most probably described by the collisionless high-beta case. Probabilities for all models and the as sociated exponents of a power law scaling function are presented.