Numerical investigating the effect of Al2O3-water nanofluids on the thermal efficiency of flat plate solar collectors

Abstract

Nanofluids have recently been utilized in experimental studies to enhance the performance of flat plate solar collectors (FPSC). The reported results for the nanofluids’ effect on this solar collector are ambiguous and sometimes contradictory. Furthermore, there is no reliable model to analyze the impact of nanofluids’ properties on the FPSC thermal performance. Therefore, this research develops a straightforward approach to predict the thermal efficiency of nanofluid-based FPSC. Pearson’s analysis confirmed that the three-quarters root of the FPSC’s thermal efficiency is the best transformation for simulating the considered problem. The machine learning models are then applied to relate the transformed thermal efficiency to the absorbed energy, energy loss, reduced temperature, the tilt angle of a flat plate, and nanoparticles’ size. Prediction performance of artificial neural networks (ANN), least-squares support vector regression (LS-SVR), adaptive neuro-fuzzy inference system (ANFIS), and available correlations have been compared to distinguish the highest accurate tool for the considered task. The results demonstrate that the LS-SVR has higher accuracy than other correlations for numerically analyzing the thermal efficiency of the FPSC. This highest accurate paradigm predicts 545 experimental datasets with the absolute average relative deviation (AARD) of 2.77%, mean squared errors (MSE) of 0.00039, and coefficient of determination (R2) of 0.99311.

Keywords

• Solar radiation
• Flat plate collectors
• Alumina–water nanofluids
• Thermal efficiency
• Smart computing techniques