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Research arrow High-Schmidt Turbulent Mixing

Mixing Paint

Motivation: Turbulent mixing at high Schmidt numbers is a very complex and ubiquitous problem that is seen in mixing of identical or different fluids. Liquids typically possess a high Schmidt number – meaning they form long and thin strings in each other when turbulent mixing is attempted before they diffuse at a molecular level. These can be observed on everyday scale such as cream in coffee without stirring, water with honey, mixing paints etc. On a much larger scale, these phenomena are commonly encountered in geophysical and oceanic flows, industrial mixing of chemicals, etc.

When this mixing is coupled with turbulence (‘turbulent mixing’), the flow sustains a wide range of scalar (high Schmidt number) and velocity scales (high Reynolds numbers). Understanding this mixing coupled with turbulence is extremely important. For example, chemical reactions occur only after the two species molecularly mix at ‘Batchelor’ scales, and the rate of reaction in a mixing-limited reaction will depend on how efficiently the injectors turbulently mix the two (or more) reactants and catalysts. Further, in many cases, the economic and environmental feasibility of a chemical plant that uses a consecutive-competitive chemistry will heavily rely on being able to accurately predict the turbulent mixing between the reactants. Accurate numerical models that represent the molecular mixing are extremely important for effective design of such industrial systems.

Viscous Jet


Current Research: STAML is currently working on investigating the effect of viscosity gradients on the mixing phenomenon. Most turbulent mixing models investigate the effect of density gradients or homogenous fluids with a scalar marker to study the turbulent mixing. However, viscosity gradients are frequently ignored for the sake of analytical, experimental or numerical simplicity. In collaboration with industry and computational experts, the current work at the Mixing Jet Facility attempts to bridge this gap in knowledge to develop more accurate predictive models, and leading to more efficient injector designs to enhance the economics and reduce the environmental footprint of these industries.