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 (ΔP), 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 ΔP, ηeng, 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 (ΔP), (ηeng), 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.