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Predication of entropy generation rate in a concentrating photovoltaic thermal system with twisted tube turbulator using Boosted regression tree algorithm

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posted on 2024-04-30, 00:07 authored by G Wang, JKS Paw, J Pasupuleti, CT Yaw, Talal YusafTalal Yusaf, AN Abdalla, Y Cai
Efficient energy conversion and utilization remain paramount in addressing the growing energy demand and environmental concerns. Concentrating photovoltaic thermal (CPVT) systems have emerged as promising solutions by integrating photovoltaic (PV) cells with thermal components for simultaneous electricity and heat generation. In this paper, we propose the application of the Boosted Regression Tree (BRT) algorithm to predict the entropy generation rate in a CPVT system equipped with a perforated twisted tube turbulator. Brief introduction of numerical analysis of local and global rates of frictional (S˙fr) and thermal (S˙th) irreversibilities in a CPVT system equipped with a perforated twisted tube turbulator. The results approve the efficacy of the BRT algorithm in predicting the entropy generation rate. Through comprehensive simulations and data analysis, we establish a predictive model that considers factors such as solar irradiance, fluid flow rate, tube geometry, and turbulator characteristics. The BRT model exhibits remarkable accuracy in capturing the nuanced interplay of these factors, enabling reliable estimations of entropy generation rate.

History

Volume

53

Start Page

1

End Page

13

Number of Pages

13

eISSN

2214-157X

ISSN

2214-157X

Publisher

Elsevier BV

Additional Rights

CC BY-NC-ND 4.0 DEED

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2023-11-04

Era Eligible

  • Yes

Journal

Case Studies in Thermal Engineering

Article Number

103729

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