Heat Transfer and Friction Factor Analysis of ND/Water Nanofluid in Parabolic Trough Collector: Experimental and Bayesian Regularization Optimization
Lingala Syam SUNDAR, Hiren Kumar MEWADA
Abstract. Experimental estimations have been made for Nusselt number (Nu), heat transfer coefficient (h_nf ), friction factor (f_nf ), and pressure drop (〖∆P〗_nf ), of nanodiamond (ND)/water nanofluids flow in a parabolic trough collector (PTC). Nanodiamond (ND) were considered for the preparation of nanofluids because ND contains larger thermal conductivity compared to other nanoparticles. Experiments were conducted at the flow rate between 0.0166 kg/s and 0.0667 kg/s, and at various particle loadings from 0.04% to 0.2%, and carried out during the day from 10:00 to 16:00 hrs. ASHRAE 93 standards served as the basis for all measures. The findings showed that, in comparison to base fluid, the Nu, h_nf, h_nf, and 〖∆P〗_nf are rose by 15.83%, 27.57%, 9.15%, and 6.12% at a flow rate of 0.066 kg/s and at 0.2% volume loadings. A machine learning technique called Bayesian regularization optimization (BRO) uses a probabilistic framework to train models in order to avoid overfitting and enhance generalization. The BRO machine learning technique makes predictions using the Nu, h_nf, h_nf, and 〖∆P〗_nf. The BRO trained data results for Nu, h_nf, h_nf, and 〖∆P〗_nf indicates, the root mean square error of 0.6973, 27.73, 0.00001, and 0.0899 with correlation coefficient R2 of 0.99854, 0.99897, 0.9999, and 0.99999, respectively.
Keywords
Nanofluids, Heat Transfer, Friction Factor, Enhancement, Machine Learning
Published online 4/25/2026, 12 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Lingala Syam SUNDAR, Hiren Kumar MEWADA, Heat Transfer and Friction Factor Analysis of ND/Water Nanofluid in Parabolic Trough Collector: Experimental and Bayesian Regularization Optimization, Materials Research Proceedings, Vol. 64, pp 680-691, 2026
DOI: https://doi.org/10.21741/9781644904091-85
The article was published as article 85 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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