Many industrial sectors, like the personal care one, make wide use of mixing processes that involve complex fluids. However, modeling the rheology of these fluids is still challenging due to their non-Newtonian behavior, which depends also on the local composition. Computational tools such as dissipative particle dynamics (DPD) have been already used to calculate the equilibrium properties of these systems. Moreover, different works have been focused on the calculation of transport properties from these mesoscale DPD simulations. Multiscale approaches have been proposed to couple rheological information from DPD with computational fluid dynamics (CFD) simulations. The CFD technique reproduces the macroscale piece of equipment, implementing a rheology model built using the Gaussian process regression, a mathematical tool related to machine learning. In this work, such a framework is tested on an industrial process, to assess its performance on a realistic application. The investigated system is a solution at a high concentration of sodium lauryl ether sulfate in water under laminar fluid dynamics regime. The results show that the mixture correctly exhibits a shear-thinning behavior and presents viscosity values in good agreement with rheology experiments. While the feasibility of the coupling approach is shown, further studies on DPD are needed to improve the accuracy and the predictability of the methodology.

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