Automated optimization of double heater convective polymerase chain reaction devices based on CFD simulation database and artificial neural network model.
This paper presents a framework for automated optimization of double-heater convective PCR (DH-cPCR) devices by developing a computational fluid dynamics (CFD) simulation database and artificial neural network (ANN) model. The optimization parameter space that includes the capillary tube geometries...
| Publicado en: | Biomedical Microdevices Vol. 23; no. 2; pp. 1 - 15 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150260007&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150260007 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13872176 ODN jtl: Biomedical Microdevices issn: 13872176 maglogo: N pubinfo: dt: Jun2021 vid: 23 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150260007 10.1007/s10544-021-00551-6 150260007 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated optimization of double heater convective polymerase chain reaction devices based on CFD simulation database and artificial neural network model. aug: au: Hong, Seong Hyeon Shu, Jung-Il Wang, Yi Baysal, Oktay affil: University of South Carolina, 29208, Columbia, SC, USA sug: ab: This paper presents a framework for automated optimization of double-heater convective PCR (DH-cPCR) devices by developing a computational fluid dynamics (CFD) simulation database and artificial neural network (ANN) model. The optimization parameter space that includes the capillary tube geometries and the heater sizes of DH-cPCR is established, and a database consisting of nearly 10,000 CFD simulations is constructed. The database is then used to train a two-stage ANN models that select practically relevant data for modeling and predict PCR device performance. The trained ANN model is then combined with the gradient-based and the heuristics optimization approaches to search for optimal device configuration that possesses the shortest DNA doubling time. The entire design process including model meshing and configuration, parallel CFD computation, database organization, and ANN training and utilization is fully automated. Case studies confirm that the proposed framework can successfully find the optimal device configuration with an error of less than 0.3 s, and hence, representing a cost-effective and rapid solution of DH-cPCR device design. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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