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Experimental and machine-learning investigation of ZnO nanofluid flow in a triangular-absorber parabolic trough collector
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DOI：10.1142/S0217984926501083.png)
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Abstract
En 中文
Improving the thermohydraulic performance of solar thermal receivers is essential for enhancing the efficiency of concentrated solar energy systems. This study experimentally and numerically investigates the heat transfer and flow characteristics of a parabolic trough collector equipped with a triangular absorber tube using ZnO/water nanofluids as the working fluid. A total of 320 operating conditions were evaluated by varying the nanoparticle volume concentration (0-0.4 vol.%) and Reynolds number (3500-7000) to assess their combined influence on heat transfer enhancement, pressure drop, and overall performance. The results show that the incorporation of ZnO nanoparticles significantly enhances thermal performance. At a volume concentration of 0.4 vol.% and a Reynolds number of 7000, the Nusselt number increases by 94.4% compared with deionized water, while the pressure drops increase by 88.9%. Despite the rise in hydraulic losses, the performance evaluation criterion improves by 158.7%, indicating that the heat transfer enhancement outweighs the additional pumping power requirement. The triangular absorber geometry further intensifies turbulence and boundary-layer disruption, contributing to improved convective heat transfer. To complement the experimental analysis, machine learning models, including linear regression, multivariate adaptive regression splines, Gaussian process regression, and random forest, were developed to predict thermohydraulic performance. Among these, Gaussian process regression demonstrated the highest predictive accuracy across the investigated parameter range. The findings highlight the combined benefits of nanofluid application, geometric modification, and data-driven modeling for improving the efficiency and operational reliability of parabolic trough solar collectors.
Keywords:
Nanofluid heat transfer
ZnO nanoparticle suspension
triangular absorber geometry
thermohydraulic enhancement
convective transport mechanisms
machine-learning modeling
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2.2
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6.6K
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