Correlation proposing and ANN modelling of thermohydraulic performance for delta-shaped winglet-type solar water heating system
- Authors
- Khargotra, Rohit; Kumar, Sushil; Singh, Tej; Lee, Dae Ho; Kumar, Raj
- Issue Date
- Jun-2024
- Publisher
- Springer
- Keywords
- ANNs; Delta-shaped winglets; Heat transfer enhancement; Hindrance promoters; Solar water heating system
- Citation
- Journal of Thermal Analysis and Calorimetry, v.149, no.12, pp 6459 - 6481
- Pages
- 23
- Journal Title
- Journal of Thermal Analysis and Calorimetry
- Volume
- 149
- Number
- 12
- Start Page
- 6459
- End Page
- 6481
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92110
- DOI
- 10.1007/s10973-024-13221-5
- ISSN
- 1388-6150
1588-2926
- Abstract
- The efficient use of renewable energy sources becomes necessary due to rise in energy demand and dwindling traditional energy sources. In present work, solar water heating system (SWHS) is modified to improve its thermohydraulic performance. The absorber tube of the SWHS is modified by inserting the delta-shaped winglets (DSW) of different configurations inside it. Following variations of DSW geometrical parameters are considered in this study: pitch ratio (PRd) from 0.5 to 2.0, blockage ratio (BRd) from 0.1 to 0.25, attack angle (α) from 15˚ to 60˚, spacer length Sw from 0 to 600 mm and Reynolds number from 200 to 1800. The results of the experiments reveal that out of considered geometrical parameters, DSW of geometrical parameters; PRd of 0.5, BRd of 0.2, α of 45˚and Sw of 0 mm affect Nusselt number (Nu) and friction factor (f) optimally. The empirical correlations for Nu, f and thermohydraulic performance (ηw) are developed from experimental values. Modelling for predictions of Nu, f and ηw using artificial neural networks (ANNs) is also executed. The values of Nu and f obtained experimentally and from developed correlation are within bias errors of 10.9% for Nu and 9.5% for f, respectively. Novel aspects of this research include the utilisation of DSW hindrance promoters in the absorber tube for performance enhancement, development of empirical correlations, and predictions of outcomes using artificial neural networks. Correlation generation and modelling using the ANNs technique are essential aspects of this study that help to optimise the design parameters of similar type of SWHS. © Akadémiai Kiadó, Budapest, Hungary 2024.
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