Multi-objective optimization of nanofluid flow in flat tubes using CFD, Artificial Neural Networks and genetic algorithms
In this article, multi-objective optimization of Al 2 O 3-water nanofluid parameters in flat tubes
is performed using Computational Fluid Dynamics (CFD) techniques, Artificial Neural
Networks (ANN) and Non-dominated Sorting Genetic Algorithms (NSGA II). At first, nanofluid
flow is solved numerically in various flat tubes using CFD techniques and heat transfer
coefficient (h¯) and pressure drop (ΔP) in tubes are calculated. In this step, two phase
mixture model is applied for nanofluid flow analysis and the flow regime is also laminar. In …
is performed using Computational Fluid Dynamics (CFD) techniques, Artificial Neural
Networks (ANN) and Non-dominated Sorting Genetic Algorithms (NSGA II). At first, nanofluid
flow is solved numerically in various flat tubes using CFD techniques and heat transfer
coefficient (h¯) and pressure drop (ΔP) in tubes are calculated. In this step, two phase
mixture model is applied for nanofluid flow analysis and the flow regime is also laminar. In …
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