Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature

The present work examines that how the fins attached to the inner surface of a vertically oriented pipe affect the flow as well as thermal characteristic of the nanofluid flow when exposed to sunlight-induced heating. A definite thermal boundary condition together with fully established hydrodynamic...

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Bibliographic Details
Main Authors: Assmaa Abd-Elmonem, Zill E Shams, Mariam Imtiaz, Kashif Ali, Sohail Ahmad, Wasim Jamshed, Fayza Abdel Aziz ElSeabee, Neissrien Alhubieshi, Syed M. Hussain, Hijaz Ahmad
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Energy Conversion and Management: X
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2590174524003246
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Summary:The present work examines that how the fins attached to the inner surface of a vertically oriented pipe affect the flow as well as thermal characteristic of the nanofluid flow when exposed to sunlight-induced heating. A definite thermal boundary condition together with fully established hydrodynamics and thermal conditions is applied that confirms uniform temperature along the sun-exposed pipe walls. Finite volume method, with computational efficiency and solution accuracy, is incorporated. A magnetic damping produces significant changes in the flow patterns within the pipe. A reduction in the velocity distribution is also caused by this damping force. The intensified applied magnetic field impact also changes the velocity distribution. It leads to a flattening of the velocity surface, indicating a decrease in velocity gradient within the flow field. The larger fins work as significant obstacles by altering flow direction and increasing velocity gradients near the solid surface. However, the higher Rayleigh numbers qualitatively change the thermal characteristics within the pipe. Finally, an artificial intelligence-based analysis is presented that correlates our research methodology with a Neural Fitting app. This neural network app is leveraged to enrich the accuracy of the present numerical technique and ensures a robust evaluation of the thermal signature of nanofluid flow within the pipe exposed to sunlight-induced heating.
ISSN:2590-1745