Design optimization of solar collectors with hybrid nanofluids: An integrated ansys and machine learning study

Abstract

The current study discussed the integration between two computational approaches to evaluate the hydrothermal properties, such as pressure drop ( P), energy efficiency (ηeng), and absorbed energy (Qabs) of solar collectors using hybrid nanofluids. The physical problem was solved through a 3D model using Ansys 2021R1. After that, the three outputs were predicted through MATLAB R2022b using Optimize a Boosted Regression Ensemble by implementing Least-Squares Boosting (LSBoost) with Bayesian optimization (BO). Various working fluids were tested: distilled water (DW), Graphene oxide (GO-DW) nanofluids, and hybrid graphene oxide/silicon dioxide (GO/SiO2-DW) nanofluids in the mixing ratio (50:50), in the concentration range 0.01-1 vol%. The simulations covered a range of Reynolds numbers (300 ≤ Re ≤ 1500) and inlet temperatures (30 °C, 40 °C, 50 °C, and 60 °C). The results indicated that, the maximum variation compared to the first and second validations for mass flow rates and different nanofluid concentrations was between 3.96-4.71% and 4.32–6.45%. At an inlet temperature of 30 °C, GO-DW-1% exhibited the highest P, eng, and Qabs, while GO-SiO2-DW-1% closely followed with slightly lower energy values. The maximum errors between computational fluid dynamics and machine learning predictions for ( P), ( eng), and (Qabs) ranged from 2.40% to 6.32%, depending on the type of nanofluids (single or hybrid) and the volume concentration. To conclude, the current approach showed a promising result in the photothermal devices.

Document Type

Journal Article

Date of Publication

7-1-2024

Volume

271

Publication Title

Solar Energy Materials and Solar Cells

Publisher

Elsevier

School

School of Engineering

RAS ID

65628

Funders

Universiti Teknologi Malaysia / Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Saudi Arabia

Comments

Alawi, O. A., Kamar, H. M., Abdelrazek, A. H., Mallah, A. R., Mohammed, H. A., Homod, R. Z., & Yaseen, Z. M. (2024). Design optimization of solar collectors with hybrid nanofluids: An integrated ansys and machine learning study. Solar Energy Materials and Solar Cells, 271, article 112822. https://doi.org/10.1016/j.solmat.2024.112822

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Link to publisher version (DOI)

10.1016/j.solmat.2024.112822