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Propose a new approach of fuzzy lookup table method to predict Al2O3/deionized water nanofluid thermal conductivity based on achieved empirical data

Author

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  • Jiang, Yu
  • Bahrami, Mehrdad
  • Bagherzadeh, Seyed Amin
  • Abdollahi, Ali
  • Sulgani, Mohsen Tahmasebi
  • Karimipour, Arash
  • Goodarzi, Marjan
  • Bach, Quang-Vu

Abstract

The mixture of Al2O3/deionized water nanofluid thermal conductivity is experimentally examined at various temperatures and mass fractions. Then, a new prediction approach of fuzzy lookup table method (FLTM) is developed to estimate the mixture thermal conductivity. The thermal conductivity of Al2O3/deionized water nanofluid is measured by several experiments; and then the statistical/numerical approach of fuzzy lookup table method is presented. It is seen that more temperature and nanoparticles concentration correspond to more nanofluid thermal conductivity. It is also observed that the proposed model can be utilized to predict the output at the training data-set in order to verify its precision. Moreover, the model outputs error percentages respect to the measured thermal conductivity for dissimilar temperatures and nanoparticle concentrations are small which means the resultant model can be interpreted as an acceptable approach due to small values of errors and less computation costs.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307083
    DOI: 10.1016/j.physa.2019.121177
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    Citations

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    Cited by:

    1. Peng, Yeping & Khaled, Usama & Al-Rashed, Abdullah A.A.A. & Meer, Rashid & Goodarzi, Marjan & Sarafraz, M.M., 2020. "Potential application of Response Surface Methodology (RSM) for the prediction and optimization of thermal conductivity of aqueous CuO (II) nanofluid: A statistical approach and experimental validatio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. 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).
    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. 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).
    5. 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).
    6. 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).

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