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

Hamad, Hazim S. and Kapur, Nik and Khatir, Zinedine and Querin, O.M. and Thompson, Harvey M. and Wang, Yongxing and Wilson, Mark (2020) Computational fluid dynamics analysis and optimisation of polymerase chain reaction thermal flow systems. Applied Thermal Engineering, 183 (116122). ISSN 1359-4311

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Computational Fluid Dynamics Analysis and Optimisation of Polymerase Chain Reaction Thermal Flow Systems-ATE_2020_accepted.pdf - Accepted Version
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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.

Item Type: Article
Identification Number:
Date: 11 October 2020
Uncontrolled Keywords: PCR, Computational fluid dynamics, Machine learning, Multi-objective optimisation
Subjects: B800 Medical Technology
G900 Others in Mathematical and Computing Sciences
H100 General Engineering
H300 Mechanical Engineering
H800 Chemical, Process and Energy Engineering
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Zinedine Khatir
Date Deposited: 29 Oct 2020 11:54
Last Modified: 29 Oct 2020 11:54

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