Micro-electro-mechanical acoustofluidic mixing system: A response surface-metaheuristic machine learning fusion framework

Abstract

Micro-mixing is an essential step in various biological and chemical analyses and synthesis processes. In the present study, an acoustofluidic micromixer is designed, modeled, and its functionality has been investigated. Intelligent acoustofluidic is proposed as an evaluation-optimization method, based on Machine Learning algorithms. The first step involves designing a parametric geometry with multiple sharp edges. Following that, mathematical modeling of the system is carried out using Response Surface Methodology (RSM) and computational fluid dynamics (CFD). Geometrical parameters including the sharp edge tip angle and the channel thickness, along with the peak-to-peak actuation voltage were investigated. It was revealed that, due to the inherent nonlinearity of acoustic streaming, each design parameter could significantly impact the flow patterns and mixing performance of the acoustofluidic mixer. The mathematical surrogate model obtained from data-based modeling procedure is then employed for the artificial intelligence multi-objective optimization algorithms, i.e. Differential Evolution (DE) and Non-dominated Sorting Genetic Algorithm two (NSGA-II). The two objective functions are the mixing index and the mechanical energy consumption index (ME). The derived optimum solutions had low Mehcanical Energy (ME) values while yielding homogeneity in a wide range of applied voltages. This ensures the controllability of the proposed system, which is an invaluable feature in synthesis processes to determine the critical physical properties of the nanoparticles. Through the algorithm evaluation process, which was conducted to distinguish the more accurate and robust optimization method, it was revealed that the DE algorithm outperformed NSGA-II in generating optimum solutions with superior functionalities. In the final section, three optimum models, operating under three different Vp-p values, 10, 20, 30, were selected, and their acoustofluidic characteristics were analyzed. It was concluded that utilizing baffles with three sharp edges at low Vp-p values results in a 96% Mixing Index and 48% decrease in the power consumption of the system. However, at high Vp-p values, baffles with one sharp edge demonstrated superior mixing functionality, with a MI of 98%. Moreover, it is revealed that utilizing baffles with three-sharp edges results in an 80% increase of Mixing Index of the acoustofluidic micromixer.