Machine learning-augmented fluid dynamics simulations for micromixer educational module

Abstract

Micromixers play an imperative role in chemical and biomedical systems. Designing compact micromixers for laminar flows owning a low Reynolds number is more challenging than flows with higher turbulence. Machine learning models can enable the optimization of the designs and capabilities of microfluidic systems by receiving input from a training library and producing algorithms that can predict the outcomes prior to the fabrication process to minimize development cost and time. Here, an educational interactive microfluidic module is developed to enable the design of compact and efficient micromixers at low Reynolds regimes for Newtonian and non-Newtonian fluids. The optimization of Newtonian fluids designs was based on a machine learning model, which was trained by simulating and calculating the mixing index of 1890 different micromixer designs. This approach utilized a combination of six design parameters and the results as an input data set to a two-layer deep neural network with 100 nodes in each hidden layer. A trained model was achieved with R2 = 0.9543 that can be used to predict the mixing index and find the optimal parameters needed to design micromixers. Non-Newtonian fluid cases were also optimized using 56700 simulated designs with eight varying input parameters, reduced to 1890 designs, and then trained using the same deep neural network used for Newtonian fluids to obtain R2 = 0.9063. The framework was subsequently used as an interactive educational module, demonstrating a well-structured integration of technology-based modules such as using artificial intelligence in the engineering curriculum, which can highly contribute to engineering education.