Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

Author:

Rostamzadeh-Renani Reza,Baghoolizadeh MohammadrezaORCID,Mohammad Sajadi S.,Pirmoradian Mostafa,Rostamzadeh-Renani Mohammad,Baghaei Sh.,Salahshour Soheil

Publisher

Elsevier BV

Subject

General Engineering

Reference59 articles.

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2. S.U. Choi, J.A. Eastman, Enhancing thermal conductivity of fluids with nanoparticles (No. ANL/MSD/CP-84938; CONF-951135-29). Argonne National Lab.(ANL), Argonne, IL (United States), 1995.

3. Prognostic modeling of polydisperse SiO2/Aqueous glycerol nanofluids’ thermophysical profile using an explainable artificial intelligence (XAI) approach;Sharma;Eng. Appl. Artif. Intel.,2023

4. Thermal performance enhancement of metal hydride reactor for hydrogen storage with graphene oxide nanofluid: Model prediction with machine learning;Kanti;Int. J. Hydrogen Energy,2023

5. The effect of pH on stability and thermal performance of graphene oxide and copper oxide hybrid nanofluids for heat transfer applications: Application of novel machine learning technique;Kumar Kanti;J. Energy Chem.,2023

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