Numerical and artificial intelligent analysis of double pipe heat exchanger exposed to solar irradiation for sustainable energy solutions
Supplying heat from sunlight directly to the walls of both the inner and outer pipes of the heat exchanger provides a clean and renewable energy source that enhances the system's thermal performance and supports sustainable energy utilization. This study presents a comprehensive numerical and a...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025020663 |
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| Summary: | Supplying heat from sunlight directly to the walls of both the inner and outer pipes of the heat exchanger provides a clean and renewable energy source that enhances the system's thermal performance and supports sustainable energy utilization. This study presents a comprehensive numerical and artificial intelligent analysis of two-dimensional solar-assisted double pipe heat exchanger under parallel flow configuration. Direct utilization of solar heat flux into the thermal boundary of a double pipe heat exchanger is a feature that is rarely addressed in the existing literature. The model incorporates the absorption of a net heat flux from solar irradiation at the pipe walls and operates under steady-state conditions with constant fluid properties. Boundary conditions include prescribed inlet velocities and temperatures for both inner and outer pipes, no-slip adiabatic walls with uniform solar heat flux, and Neumann-type outflow conditions at the exits. Governing equations are discretized using the finite volume method (FVM) on a structured mesh. A radial basis function neural network (RBFNN) is employed to enhance prediction and generalization of heat transfer performance across various ranges of physical parameters. Results demonstrate that increasing nanoparticle concentration and solar flux significantly enhance the thermal efficiency of solar-assisted heat exchanger. On the other hand, the RBFNN model exhibits excellent predictive capability with high accuracy and minimal error. |
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| ISSN: | 2590-1230 |