IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v535y2019ics0378437119313883.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119313883
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.122409?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Zhixiong & Shahrajabian, Hamzeh & Bagherzadeh, Seyed Amin & Jadidi, Hamid & Karimipour, Arash & Tlili, Iskander, 2020. "Effects of nano-clay content, foaming temperature and foaming time on density and cell size of PVC matrix foam by presented Least Absolute Shrinkage and Selection Operator statistical regression via s," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. Wei, Li & Arasteh, Hossein & abdollahi, Ali & Parsian, Amir & Taghipour, Abdolmajid & Mashayekhi, Ramin & Tlili, Iskander, 2020. "Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    3. Peng, Yeping & Parsian, Amir & Khodadadi, Hossein & Akbari, Mohammad & Ghani, Kamal & Goodarzi, Marjan & Bach, Quang-Vu, 2020. "Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3 – Cu nanoparticles dispersed in ethy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Tian, Zhe & Arasteh, Hossein & Parsian, Amir & Karimipour, Arash & Safaei, Mohammad Reza & Nguyen, Truong Khang, 2019. "Estimate the shear rate & apparent viscosity of multi-phased non-Newtonian hybrid nanofluids via new developed Support Vector Machine method coupled with sensitivity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    5. Alsarraf, Jalal & Moradikazerouni, Alireza & Shahsavar, Amin & Afrand, Masoud & Salehipour, Hamzeh & Tran, Minh Duc, 2019. "Hydrothermal analysis of turbulent boehmite alumina nanofluid flow with different nanoparticle shapes in a minichannel heat exchanger using two-phase mixture model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 275-288.
    6. Khaje khabaz, Moahamad & Eftekhari, S. Ali & Hashemian, Mohamad & Toghraie, Davood, 2020. "Optimal vibration control of multi-layer micro-beams actuated by piezoelectric layer based on modified couple stress and surface stress elasticity theories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    7. Al-Rashed, Abdullah A.A.A., 2019. "Optimization of heat transfer and pressure drop of nano-antifreeze using statistical method of response surface methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 531-542.
    8. Gediminas Skarbalius & Algis Džiugys & Edgaras Misiulis & Robertas Navakas & Paulius Vilkinis & Justas Šereika & Nerijus Pedišius, 2021. "Molecular Dynamics Study on Water Flow Behaviour inside Planar Nanochannel Using Different Temperature Control Strategies," Energies, MDPI, vol. 14(20), pages 1-13, October.
    9. Afrouzi, Hamid Hassanzadeh & Hosseini, Mirolah & Toghraie, Davood & Mehryaar, Ehsan & Afrand, Masoud, 2020. "Thermo-hydraulic characteristics investigation of nanofluid heat transfer in a microchannel with super hydrophobic surfaces under non-uniform magnetic field using Incompressible Preconditioned Lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    10. Simeng Yan & Naisheng Guo & Xin Jin & Zhaoyang Chu & Sitong Yan, 2023. "The Study on Mathematical Simulation and Analysis of the Molecular Discrete System of the Sulfurated Eucommia Ulmoides Gum," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    11. Afrouzi, Hamid Hassanzadeh & Ahmadian, Majid & Moshfegh, Abouzar & Toghraie, Davood & Javadzadegan, Ashkan, 2019. "Statistical analysis of pulsating non-Newtonian flow in a corrugated channel using Lattice-Boltzmann method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    12. Roy Setiawan & Reza Daneshfar & Omid Rezvanjou & Siavash Ashoori & Maryam Naseri, 2021. "Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artificial intelligence," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17606-17627, December.
    13. Shahsavar, Amin & Bagherzadeh, Seyed Amin & Mahmoudi, Boshra & Hajizadeh, Ahmad & Afrand, Masoud & Nguyen, Truong Khang, 2019. "Robust Weighted Least Squares Support Vector Regression algorithm to estimate the nanofluid thermal properties of water/graphene Oxide–Silicon carbide mixture," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1418-1428.
    14. Mohammed Algarni & Mashhour A. Alazwari & Mohammad Reza Safaei, 2021. "Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    15. Jose J. Aguilar-Fuertes & Francisco Noguero-Rodríguez & José C. Jaen Ruiz & Luis M. García-RAffi & Sergio Hoyas, 2021. "Tracking Turbulent Coherent Structures by Means of Neural Networks," Energies, MDPI, vol. 14(4), pages 1-15, February.
    16. Ahmadi, Mohammad Hossein & Baghban, Alireza & Sadeghzadeh, Milad & Hadipoor, Masoud & Ghazvini, Mahyar, 2020. "Evolving connectionist approaches to compute thermal conductivity of TiO2/water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    17. Zheng, Yuanzhou & Shadloo, Mostafa Safdari & Nasiri, Hossein & Maleki, Akbar & Karimipour, Arash & Tlili, Iskander, 2020. "Prediction of viscosity of biodiesel blends using various artificial model and comparison with empirical correlations," Renewable Energy, Elsevier, vol. 153(C), pages 1296-1306.
    18. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
    19. Ammar A. Melaibari & Yacine Khetib & Abdullah K. Alanazi & S. Mohammad Sajadi & Mohsen Sharifpur & Goshtasp Cheraghian, 2021. "Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials," Sustainability, MDPI, vol. 13(20), pages 1-17, October.
    20. Hu, Chunhua & Lai, Shaoyong & Lai, Chong, 2020. "Investigations to the price evolutions of goods exchange with CES utility functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119313883. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.