In recent years, additive manufacturing has gained importance in a wide range of research applications such as medicine, biotechnology, engineering, etc. It has become one of the most innovative and high-performance manufacturing technologies of the moment. This review aims to show and discuss the characteristics of different existing additive manufacturing technologies for the construction of micromixers, which are devices used to mix two or more fluids at microscale. The present manuscript discusses all the choices to be made throughout the printing life cycle of a micromixer in order to achieve a high-quality microdevice. Resolution, precision, materials, and price, amongst other relevant characteristics, are discussed and reviewed in detail for each printing technology. Key information, suggestions, and future prospects are provided for manufacturing of micromixing machines based on the results from this review.
In the recent years, microscale applications are gaining increasing importance. Despite their advances, the technology and resources needed to develop new designs may be a drawback for reduced scale engineering testing. To overcome this, computational methods are an efficient tool to predict how a real-life system may behave prior physical construction. The present work aims to investigate numerically effective models to predict the conditions at which a micro heat exchanger (MHE) is able to promote mixing by vortex shedding mechanics. In spite of vortex-shedding is a well-known mechanism in flow physics, it is not possible to know a priori whether a configuration (for a given geometry and flow velocity) may or may not lead to this desired vortex detachment to enhace mixing. Thus, Machine Learning methods are used for prediction, trained with finite-volume numerical simulations of different MHE devices selected based on their performance. A classification model is used to predict which configurations lead to vortex shedding. Also, a correlation regression model is developed to predict the critical Reynolds number. When the critical Reynolds number is surpassed for a given geometry, vortex shedding appears and its intensity controls the thermal mixing efficiency of the microdevice. These predictors could be useful in the search of optimal configurations by optimisation algorithms, since in the sampling process could be used to define constrains.
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