This work aims to present a new statistical optimization approach of artificial neural network modified by multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for a non-Newtonian nanofluid composed of Fe3O4 nanoparticles dispersed in liquid paraffin. Hence the mixture pressure lose & convection coefficient are evaluated and then optimized so that to maximize the convection heat transfer and minimize the pressure drop. The results showed that the proposed model of multi objective optimization of GA Pareto optimal front, quantified the trade-offs to handle 2 fitness functions of the considered non-Newtonian pipe flow.

Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids / Wu, H.; Bagherzadeh, S. A.; D'Orazio, A.; Habibollahi, N.; Karimipour, A.; Goodarzi, M.; Bach, Q. -V.. - In: PHYSICA. A. - ISSN 0378-4371. - 535:(2019), pp. 1-11. [10.1016/j.physa.2019.122409]

Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids

D'Orazio A.;Karimipour A.;
2019

Abstract

This work aims to present a new statistical optimization approach of artificial neural network modified by multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for a non-Newtonian nanofluid composed of Fe3O4 nanoparticles dispersed in liquid paraffin. Hence the mixture pressure lose & convection coefficient are evaluated and then optimized so that to maximize the convection heat transfer and minimize the pressure drop. The results showed that the proposed model of multi objective optimization of GA Pareto optimal front, quantified the trade-offs to handle 2 fitness functions of the considered non-Newtonian pipe flow.
2019
Fe3O4; liquid paraffin; multi objective genetic algorithm; optimization; thermo-physical properties
01 Pubblicazione su rivista::01a Articolo in rivista
Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids / Wu, H.; Bagherzadeh, S. A.; D'Orazio, A.; Habibollahi, N.; Karimipour, A.; Goodarzi, M.; Bach, Q. -V.. - In: PHYSICA. A. - ISSN 0378-4371. - 535:(2019), pp. 1-11. [10.1016/j.physa.2019.122409]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1318657
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