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ISSN Online: 2377-424X

ISBN Print: 978-1-56700-474-8

ISBN Online: 978-1-56700-473-1

International Heat Transfer Conference 16
August, 10-15, 2018, Beijing, China

NUMERICAL ANAYLSYIS AND EXPERIMENTAL VERIFICATION OF MICROCHANNEL HEAT EXCHANGER CHARACTERISTICS USING POROUS MEDIA APPROACH

Get access (open in a dialog) DOI: 10.1615/IHTC16.her.023818
pages 4867-4876

Résumé

There is a limitation in the numerical analysis for the full scale microchannel heat exchanger (MCHE) due to the difference in size of the overall heat exchanger with respect to the minimum scale in the microchannel characteristic geometry. For this reason, most numerical studies have been carried out confined to unit cells of some characteristic structures or channels. This limited analysis restricts the ability to evaluate the overall performance of the entire heat exchanger. To overcome these limitations, this study introduced an analytical method for predicting the heat transfer performance and internal pressure drop of the full scale MCHE by implementing a porous media approach.
To simplify and reduce the number of numerical grids of the entire MCHE model, internal micro-channel array and a wavy fin structure are assumed as the integrated porous media respectively. In the flow analysis for porous media, inertial and viscous resistances were calculated through unit channel and fin geometry, and the pressure loss was calculated using Darcy law. For single-phase flow analysis in MCHE, deionized water was used as working fluid. MCHE characteristics such as friction factor and overall heat transfer coefficient were analyzed as varying the flow rate and temperature of inlet water and fin inlet velocity. Experimental verification was carried out through a simple loop to confirm the reliability of the numerical results. As a result, tendencies between experimental and numerical results are reasonably matched in each other and it can be seen that the numerical method using a porous medium makes it possible to analyze the full scale MCHE and is suitable for the overall performance prediction.