Surrogate-based optimization with adaptive sampling for microfluidic concentration gradient generator design

This paper presents a surrogate-based optimization (SBO) method with adaptive sampling for designing microfluidic concentration gradient generators (mu CGGs) to meet prescribed concentration gradients (CGs). An efficient physics-based component model (PBCM) is used to generate data for Kriging-based surrogate model construction. In a comparative analysis, various combinations of regression and correlation models in Kriging, and different adaptive sampling (infill) techniques are inspected to enhance model accuracy and optimization efficiency. The results show that the first-order polynomial regression and the Gaussian correlation models together form the most accurate model, and the lower bound (LB) infill strategy in general allows the most efficient global optimum search. The CGs generated by optimum designs match very well with prescribed CGs, and the discrepancy is less than 12% even with an inherent limitation of the mu CGG. It is also found that SBO with adaptive sampling enables much more efficient and accurate design than random sampling-based surrogate modeling and optimization, and is more robust than the gradient-based optimization for searching the global optimum.