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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

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  • Wu, Huawei
  • Bagherzadeh, Seyed Amin
  • D’Orazio, Annunziata
  • Habibollahi, Navid
  • Karimipour, Arash
  • Goodarzi, Marjan
  • Bach, Quang-Vu

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.

Suggested Citation

  • Wu, Huawei & Bagherzadeh, Seyed Amin & D’Orazio, Annunziata & Habibollahi, Navid & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "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 ther," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119313883
    DOI: 10.1016/j.physa.2019.122409
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    References listed on IDEAS

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    1. Alipour, Pedram & Toghraie, Davood & Karimipour, Arash & Hajian, Mehdi, 2019. "Modeling different structures in perturbed Poiseuille flow in a nanochannel by using of molecular dynamics simulation: Study the equilibrium," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 13-30.
    2. Alipour, Pedram & Toghraie, Davood & Karimipour, Arash, 2019. "Investigation the atomic arrangement and stability of the fluid inside a rough nanochannel in both presence and absence of different roughness by using of accurate nano scale simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 639-660.
    3. Jiang, Yu & Bahrami, Mehrdad & Bagherzadeh, Seyed Amin & Abdollahi, Ali & Sulgani, Mohsen Tahmasebi & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "Propose a new approach of fuzzy lookup table method to predict Al2O3/deionized water nanofluid thermal conductivity based on achieved empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    4. Bagherzadeh, Seyed Amin & Sulgani, Mohsen Tahmasebi & Nikkhah, Vahid & Bahrami, Mehrdad & Karimipour, Arash & Jiang, Yu, 2019. "Minimize pressure drop and maximize heat transfer coefficient by the new proposed multi-objective optimization/statistical model composed of “ANN + Genetic Algorithm” based on empirical data of CuO/pa," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    5. Jiang, Yu & Sulgani, Mohsen Tahmasebi & Ranjbarzadeh, Ramin & Karimipour, Arash & Nguyen, Truong Khang, 2019. "Hybrid GMDH-type neural network to predict fluid surface tension, shear stress, dynamic viscosity & sensitivity analysis based on empirical data of iron(II) oxide nanoparticles in light crude oil mixt," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    6. Bahrami, Mehrdad & Akbari, Mohammad & Bagherzadeh, Seyed Amin & Karimipour, Arash & Afrand, Masoud & Goodarzi, Marjan, 2019. "Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 159-168.
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