MHD mode identification of tokamak plasmas from Mirnov signals

Identification of coherent waves from fluctuating tokamak plasmas is import ant for the understanding of magnetohydrodynamics (MHD) behaviour of the pl asma and its control. Toroidicity, plasma shaping, uneven distances between the resonant surfaces and detectors, and non-circular conducting wall geom etry have made mode identification difficult and complex, especially in ter ms of the conventional toroidal and poloidal mode numbers, which we call (m , n)-identification. Singular value decomposition (SVD), without any assump tion of the basis vectors, determines its own basis vectors representing th e fluctuation data in the directions of maximum coherence, Factorization of a synchronized set of spatially distributed data leads to eigenvectors of time- and spatial-covariance matrices, with the energy content of each eige nvector. SVD minimizes the number of significant basis vectors, reducing no ise, and minimizes the data storage required to restore the fluctuation dat a. For sinusoidal signals, SVD is essentially the same as spectral analysis , When the mode has non-smooth structures the advantage of not having to tr eat all its spectral components is significant in analysing mode dynamics a nd in data storage. From time SVD vectors, we can see the evolution of each coherent structure. Therefore, sporadic or intermittent events can be reco gnized, while such events would be ignored with spectral analysis. We present the use of SVD to analyse tokamak magnetic fluctuation data, tim e evolution of MHD modes, spatial structure of each time vector, and the en ergy content of each mode, if desired, the spatial SVD vectors can be least -square fit to specific numerical predictions for the (m, n) identification . A phase-fitting method for (m, n) mode identification is presented for co mparison. Applications of these methods to mode locking analysis are presen ted.