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Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System

In this study, a hybridized neuro-genetic optimization methodology realized by embedding numerical simulations trained artificial neural networks (ANN) into a genetic algorithm (GA) is used to optimize the flow rectification efficiency of the diffuser element for a valveless diaphragm micropump appl...

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Detalles Bibliográficos
Autores principales: Lee, Hing Wah, Azid, Ishak Hj Abdul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3290506/
https://www.ncbi.nlm.nih.gov/pubmed/22400004
http://dx.doi.org/10.3390/s90907481
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author Lee, Hing Wah
Azid, Ishak Hj Abdul
author_facet Lee, Hing Wah
Azid, Ishak Hj Abdul
author_sort Lee, Hing Wah
collection PubMed
description In this study, a hybridized neuro-genetic optimization methodology realized by embedding numerical simulations trained artificial neural networks (ANN) into a genetic algorithm (GA) is used to optimize the flow rectification efficiency of the diffuser element for a valveless diaphragm micropump application. A higher efficiency ratio of the diffuser element consequently yields a higher flow rate for the micropump. For that purpose, optimization of the diffuser element is essential to determine the maximum pumping rate that the micropump is able to generate. Numerical simulations are initially carried out using CoventorWare® to analyze the effects of varying parameters such as diffuser angle, Reynolds number and aspect ratio on the volumetric flow rate of the micropump. A limited range of simulation results will then be used to train the neural network via back-propagation algorithm and optimization process commence subsequently by embedding the trained ANN results as a fitness function into GA. The objective of the optimization is to maximize the efficiency ratio of the diffuser element for the range of parameters investigated. The optimized efficiency ratio obtained from the neuro-genetic optimization is 1.38, which is higher than any of the maximum efficiency ratio attained from the overall parametric studies, establishing the superiority of the optimization method.
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spelling pubmed-32905062012-03-07 Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System Lee, Hing Wah Azid, Ishak Hj Abdul Sensors (Basel) Article In this study, a hybridized neuro-genetic optimization methodology realized by embedding numerical simulations trained artificial neural networks (ANN) into a genetic algorithm (GA) is used to optimize the flow rectification efficiency of the diffuser element for a valveless diaphragm micropump application. A higher efficiency ratio of the diffuser element consequently yields a higher flow rate for the micropump. For that purpose, optimization of the diffuser element is essential to determine the maximum pumping rate that the micropump is able to generate. Numerical simulations are initially carried out using CoventorWare® to analyze the effects of varying parameters such as diffuser angle, Reynolds number and aspect ratio on the volumetric flow rate of the micropump. A limited range of simulation results will then be used to train the neural network via back-propagation algorithm and optimization process commence subsequently by embedding the trained ANN results as a fitness function into GA. The objective of the optimization is to maximize the efficiency ratio of the diffuser element for the range of parameters investigated. The optimized efficiency ratio obtained from the neuro-genetic optimization is 1.38, which is higher than any of the maximum efficiency ratio attained from the overall parametric studies, establishing the superiority of the optimization method. Molecular Diversity Preservation International (MDPI) 2009-09-18 /pmc/articles/PMC3290506/ /pubmed/22400004 http://dx.doi.org/10.3390/s90907481 Text en © 2009 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Lee, Hing Wah
Azid, Ishak Hj Abdul
Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System
title Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System
title_full Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System
title_fullStr Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System
title_full_unstemmed Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System
title_short Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System
title_sort neuro-genetic optimization of the diffuser elements for applications in a valveless diaphragm micropumps system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3290506/
https://www.ncbi.nlm.nih.gov/pubmed/22400004
http://dx.doi.org/10.3390/s90907481
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