Multi-objective optimization of nanofluid flow in flat tubes using CFD, Artificial Neural Networks and genetic algorithms

• Published in: Advanced powder technology : the international journal of the Society of Powder Technology, Japan Vol. 25; no. 5; pp. 1608 - 1617
• Main Authors: Safikhani, H., Abbassi, A., Khalkhali, A., Kalteh, M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2014
• Subjects: Flat tubes; GMDH; Multi-objective optimization; Nanofluid; NSGA II; Two phase mixture model; Multi-objective optimization; GMDH; Two phase mixture model; Nanofluid; Flat tubes; NSGA II
• ISSN: 0921-8831; 1568-5527
• DOI: 10.1016/j.apt.2014.05.014

[Display omitted] •Multi-objective optimization of nanofluid flow in flat tubes is investigated.•CFD data are used for ANN modeling of objective functions.•For CFD simulation of nanofluid flow, two phase mixture model is used.•The geometries are horizontal flat tubes with different flattening. In this article, multi-objective optimization of Al2O3-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 the next step, numerical data of the previous step will be applied for modeling h¯ and ΔP using Grouped Method of Data Handling (GMDH) type ANN. Finally, the modeling achieved by GMDH will be used for Pareto based multi-objective optimization of nanofluid parameters in horizontal flat tubes using NSGA II algorithm. It is shown that the achieved Pareto solution includes important design information on nanofluid parameters in flat tubes.