Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator

The current research was carried out with the aim of numerically investigating the effect of employing a perforated twisted tube turbulator on the energy and exergy yields of a concentrating photovoltaic thermal (PVT) device. The simulations were performed in 4 different Reynolds numbers (Re) (i.e....

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Main Authors: Wang G., Hai T., Paw J.K.S., Pasupuleti J., Abdalla A.N.
Other Authors: 58839436200
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Published: Elsevier Ltd 2024
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spelling my.uniten.dspace-339892024-10-14T11:17:35Z Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator Wang G. Hai T. Paw J.K.S. Pasupuleti J. Abdalla A.N. 58839436200 36350315600 58168727000 11340187300 25646071000 Boosted regression tree Energy Exergy Photovoltaic thermal collector Solar energy Turbulator Energy efficiency Machine learning Photovoltaic effects Reynolds number Solar energy Solar panels Solar power generation Boosted regression trees Concentrating photovoltaic Energy Energy and exergy Photovoltaic thermal collector Photovoltaic thermals Reynold number Thermal collectors Thermal devices Turbulators Exergy The current research was carried out with the aim of numerically investigating the effect of employing a perforated twisted tube turbulator on the energy and exergy yields of a concentrating photovoltaic thermal (PVT) device. The simulations were performed in 4 different Reynolds numbers (Re) (i.e. 500, 1000, 1500 and 2000) and considering 4 different twist distance (TD) (i.e. L/25, L/50, L/70, and L/100) for the non-perforated turbulator and three different perforated turbulators (with 1, 2, and 3 holes) with TD = L/100. Among the examined cases, the best and worst performance belonged to the PVT device with perforated turbulator and without a turbulator, respectively. For the PVT device with non-perforated turbulator, the lowest PV panel temperature, the highest water outlet temperature, and the highest energy and exergy efficiencies occurred at the highest Re (i.e. 2000) and the lowest TD (i.e. L/100). Also, it was revealed that among the examined perforated turbulators, the best performance belongs to the turbulator with 3 holes in each pitch. In this case, the temperature of the PV panel, the overall energy efficiency and the overall exergy efficiency of the PVT device are respectively 3 �C lower, 7.43% higher and 3.21% higher than the case without turbulator. As another novelty, a new ensemble machine learning model, namely boosted regression tree (BRT) was developed to simulation of the overall energy and exergy efficiencies based on the Reynolds number and volume fraction. The outcomes revealed the promising accuracy for both targets in terms various statistical metrics. � 2023 Elsevier Ltd Final 2024-10-14T03:17:35Z 2024-10-14T03:17:35Z 2023 Article 10.1016/j.enganabound.2023.06.033 2-s2.0-85164670953 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164670953&doi=10.1016%2fj.enganabound.2023.06.033&partnerID=40&md5=cf6b204d2331549b0d0bbc6c04a0ee6a https://irepository.uniten.edu.my/handle/123456789/33989 155 754 765 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Boosted regression tree
Energy
Exergy
Photovoltaic thermal collector
Solar energy
Turbulator
Energy efficiency
Machine learning
Photovoltaic effects
Reynolds number
Solar energy
Solar panels
Solar power generation
Boosted regression trees
Concentrating photovoltaic
Energy
Energy and exergy
Photovoltaic thermal collector
Photovoltaic thermals
Reynold number
Thermal collectors
Thermal devices
Turbulators
Exergy
spellingShingle Boosted regression tree
Energy
Exergy
Photovoltaic thermal collector
Solar energy
Turbulator
Energy efficiency
Machine learning
Photovoltaic effects
Reynolds number
Solar energy
Solar panels
Solar power generation
Boosted regression trees
Concentrating photovoltaic
Energy
Energy and exergy
Photovoltaic thermal collector
Photovoltaic thermals
Reynold number
Thermal collectors
Thermal devices
Turbulators
Exergy
Wang G.
Hai T.
Paw J.K.S.
Pasupuleti J.
Abdalla A.N.
Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator
description The current research was carried out with the aim of numerically investigating the effect of employing a perforated twisted tube turbulator on the energy and exergy yields of a concentrating photovoltaic thermal (PVT) device. The simulations were performed in 4 different Reynolds numbers (Re) (i.e. 500, 1000, 1500 and 2000) and considering 4 different twist distance (TD) (i.e. L/25, L/50, L/70, and L/100) for the non-perforated turbulator and three different perforated turbulators (with 1, 2, and 3 holes) with TD = L/100. Among the examined cases, the best and worst performance belonged to the PVT device with perforated turbulator and without a turbulator, respectively. For the PVT device with non-perforated turbulator, the lowest PV panel temperature, the highest water outlet temperature, and the highest energy and exergy efficiencies occurred at the highest Re (i.e. 2000) and the lowest TD (i.e. L/100). Also, it was revealed that among the examined perforated turbulators, the best performance belongs to the turbulator with 3 holes in each pitch. In this case, the temperature of the PV panel, the overall energy efficiency and the overall exergy efficiency of the PVT device are respectively 3 �C lower, 7.43% higher and 3.21% higher than the case without turbulator. As another novelty, a new ensemble machine learning model, namely boosted regression tree (BRT) was developed to simulation of the overall energy and exergy efficiencies based on the Reynolds number and volume fraction. The outcomes revealed the promising accuracy for both targets in terms various statistical metrics. � 2023 Elsevier Ltd
author2 58839436200
author_facet 58839436200
Wang G.
Hai T.
Paw J.K.S.
Pasupuleti J.
Abdalla A.N.
format Article
author Wang G.
Hai T.
Paw J.K.S.
Pasupuleti J.
Abdalla A.N.
author_sort Wang G.
title Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator
title_short Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator
title_full Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator
title_fullStr Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator
title_full_unstemmed Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator
title_sort numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator
publisher Elsevier Ltd
publishDate 2024
_version_ 1814061098184212480
score 13.214268