Surrogate- and possibility-based design optimization for convective polymerase chain reaction devices

Abstract

This paper presents a surrogate- and possibility-based design optimization (SPBDO) methodology for robust design of convective PCR. Parametric computational fluid dynamics (CFD) models are built and simulated at sampled locations within the design space to capture the effect of design configurations on thermofluidic transport and convection–diffusion-reaction in the convective PCR. A support vector machine-based classifier model is trained to retain only practically relevant data for enhanced surrogate modeling accuracy. Surrogate models are constructed by Kriging interpolation and multivariate polynomial regression methods to establish the mapping between design configurations and DNA doubling time (indicative of reactor performance). Then a process to combine the sequential method of PBDO and the surrogate model is developed, and a trade study is carried out to evaluate the impact of possibility of failure ($\alpha$-value) and the balance between performance and design reliability. Our study demonstrates that the proposed SPBDO represents an effective method to consider robustness in PCR design for POC applications, especially when the uncertainty information or possibilistic characteristics of design variables is limited.