Chaos and multifractals in the solar wind Original Research Article

By using the false-nearest-neighbours method, we have argued that the deterministic component of solar wind plasma dynamics should be low-dimensional. In fact, the results we have obtained using the method of topological embedding indicate that the behaviour of the solar wind can be approximately described by a low-dimensional chaotic attractor in the inertial manifold, which is a subspace of system phase space. We have also shown that the multifractal spectrum of the solar wind attractor is consistent with that for the multifractal measure of the self-similar generalized weighted Cantor set with two different scaling parameters and one probability measure parameter responsible for nonuniform compression in phase space and multifractality. The values of the parameters fitted also demonstrate that the complex solar wind system could only be weakly non-conservative (small dissipation) and quantify nonlinear dynamics; some parts of the attractor in phase space are visited much more frequently than other parts. In addition, to quantify the multifractality of space plasma intermittent turbulence, we consider that generalized Cantor set also in the context of scaling properties of solar wind turbulence. We investigate the resulting multifractal spectrum of a one-dimensional phenomenological model of turbulence cascade depending on its parameters, especially for asymmetric scaling. In particular, we have shown that intermittent pulses are stronger for the cascade model with two different scaling parameters. Even thought solar wind turbulence appears to be rather space filling, a better agreement with the data is obtained, especially for the negative index of generalized dimensions. Therefore we argue that there is a need to use a two-scale asymmetric cascade model. We hope that this generalized multifractal model will be a useful tool for analysis of intermittent turbulence in space plasmas. We thus believe that fractal analysis of chaotic systems could lead us to a deeper understanding of their nature, and maybe even to predict their seemingly unpredictable behaviour.