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• W2072013189 abstract "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." @default.
• W2072013189 created "2016-06-24" @default.
• W2072013189 creator A5026200894 @default.
• W2072013189 creator A5057198414 @default.
• W2072013189 date "2009-09-18" @default.
• W2072013189 modified "2023-09-27" @default.
• W2072013189 title "Neuro-Genetic Optimization of the Diffuser Elements for Applications in a Valveless Diaphragm Micropumps System" @default.
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• W2072013189 doi "https://doi.org/10.3390/s90907481" @default.
• W2072013189 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3290506" @default.
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