Prediction of mixing efficiency in induced charge electrokinetic micromixer for non-Newtonian fluids using hybrid computational fluid dynamics-artificial neural network approach

A novel hybrid computational fluid dynamics-artificial neural network approach is implemented to predict the mixing efficiency of a T-shaped induced charge electrokinetic micromixer for non-Newtonian fluids. 12,500 data observations produced from computational fluid dynamics—benchmarked against experimental results—are used to develop an optimized deep neural network model for the prediction of mixing efficiency. The optimized neural network model with tansig transfer function in hidden layers has an architecture of 7-85-85-1 and it predicts the mixing efficiency of the induced charge electrokinetic micromixer with the maximum deviation of 2.74 %. Global sensitivity of the artificial neural network model is assessed using Shapley values and it is found that length of the conducting link is the most influencing parameter for designing induced charge electrokinetic micromixer. If more than one conducting links are employed, the pitch transverse to fluid flow is more critical than pitch along the fluid flow direction in mixing zone. Pseudoplastic fluids, marked by pronounced micro-vortices, exhibit superior mixing efficiency, and accelerated mixing at higher electric field strengths compared to dilatant fluids, achieving a mixing efficiency exceeding 99 %. The optimized artificial neural network model predicts mixing efficiency significantly faster compared to computational fluid dynamics and conclusively demonstrates its ability to expedite the design process for electrokinetic micromixers.