Computational fluid dynamics analysis and optimisation of polymerase chain reaction thermal flow systems

Abstract

A novel Computational Fluid Dynamics-enabled multi-objective optimisation methodology for Polymerase Chain Reaction flow systems is proposed and used to explore the effect of geometry, material and flow variables on the temperature uniformity, pressure drop and heating power requirements, in a prototype three-zone thermal flow system. A conjugate heat transfer model for the three-dimensional flow and heat transfer is developed and solved numerically using COMSOL Multiphysics® and the solutions obtained demonstrate how the design variables affect each of the three performance parameters. These show that choosing a substrate with high conductivity and small thickness, together with a small channel area, generally improves the temperature uniformity in each zone, while channel area and substrate conductivity have the key influences on pressure drop and heating power respectively. The multi-objective optimisation methodology employs accurate surrogate modelling facilitated by Machine Learning via fully-connected Neural Networks to create Pareto curves which demonstrate clearly the compromises that can be struck between temperature uniformity throughout the three zones and the pressure drop and heating power required.