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Your search keyword '"device optimization"' showing total 2 results
Start Over Descriptor "device optimization" Journal biomedical microdevices
2 results on '"device optimization"'

1. Automated optimization of double heater convective polymerase chain reaction devices based on CFD simulation database and artificial neural network model.

Author

Hong, Seong Hyeon, Shu, Jung-Il, Wang, Yi, and Baysal, Oktay

Subjects
ARTIFICIAL neural networks, POLYMERASE chain reaction, COMPUTATIONAL fluid dynamics, HEATING, CAPILLARY tubes
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Automated optimization of double heater convective polymerase chain reaction devices based on CFD simulation database and artificial neural network model

Author

Seong Hyeon Hong, Yi Wang, Oktay Baysal, and Jung-Il Shu

Subjects
Convection, Cfd simulation, Computer science, Computation, Biomedical Engineering, 02 engineering and technology, Parameter space, Computational fluid dynamics, computer.software_genre, 01 natural sciences, Polymerase Chain Reaction, Polymerase chain reaction (PCR), Article, Machine learning, Computer Simulation, Molecular Biology, Point of care (POC), Database, Artificial neural network, business.industry, 010401 analytical chemistry, Device optimization, 021001 nanoscience & nanotechnology, Neural network, 0104 chemical sciences, Computational fluid dynamics (CFD), Hydrodynamics, Design process, Neural Networks, Computer, 0210 nano-technology, business, Heuristics, computer
Abstract

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.

Published
2021

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