This paper proposes a physics-based methodology for the analysis of optical flows displaying complex patterns. Turbulent motion, such as that exhibited by fluid substances, can be modelled using fluid dynamics principles. Together with supplemental equations, such as the conservation of mass, and well formulated boundary conditions, the Navier-Stokes equations can be used to model complex fluid motion estimated from image sequences. In this paper, we propose to use a robust kernel which adapts to the local data geometry in the diffusion stage of the Navier-Stokes formulation. The proposed kernel is Gaussian and embeds the Hessian of the local data as its covariance matrix. The local Hessian models the variation of the flow in a certain neighbourhood. Moreover, we use a robust statistics mechanism in order to eliminate the outliers from the estimation process. The proposed methodology is applied on artificial vector fields and in image sequences showing atmospheric and solar phenomena.