Control Prediction and LeaRning in Mixing processes

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Mixing is the science describing the evolution of the concentration of a substance (tracers, chemicals, heat, bacteria...) in a continuum substrate that is possibly deforming. It is also a necessary process or phenomenon taking place at each scales, from molecular to planetary, in all non-equilibrium human and natural activities. Most approaches to mixing used in science and engineering are based on mean field approaches or phenomenological mixing models , which focus on dynamics through effective coefficients such as mixing micro-scales, diffusivities, or on purely descriptive characterization of mixing through entropy measures for example. A predictive approaches that account for the many facets of the dynamics of mixing in a broad variety of applications and fields is however emerging: It consists in visualizing a mixture as a set of elongated stripes and sheets, understand how they are stretched and dispersed by the flow, a step we call Stirring. This first step provides the necessary tools to couple molecular diffusion, leading to the complete statistical description of the mixing process i.e. the full concentration distribution. In that sense and as opposed to traditional approaches, this disruptive vision has prompted new numerical and experimental methods and offers a transformative vision for Mixing to envisage its Impact in a diversity of fields and Learn from the stirring medium itself. A new generation of scientists and engineers is required that is aware of these fundamental issues and equipped with new visions and tool sets for mixing in order to address the increasing need of understanding and predicting mixing processes in environmental and industrial applications. The CoPerMix training network proposes to address this challenge by setting up an innovative and entrepreneurial training programme that renews drastically the methods and approaches to the subject and incorporates this strategic new vision of Mixing in prominent academic curricula.