Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/956457 |
| Start date: | 01-01-2021 |
| End date: | 31-12-2024 |
| Total budget - Public funding: | 3 989 007,00 Euro - 3 989 007,00 Euro |
Cordis data
Original description
Mixing is the science describing the evolution of the concentration of a substance (tracers, chemicals, heat, bacteria...) in acontinuum 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.
Status
SIGNEDCall topic
MSCA-ITN-2020Update Date
28-04-2024
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