A Detailed Review of Colburn and Friction Factors for Various Types of Fins Used in Compact Plate‐Fin Heat Exchangers

ABSTRACT Compact plate‐fin heat exchangers (PFHEs) are commonly utilized in diverse industries, including aerospace, automotive, processing, and space. Various fin configurations, such as Plain fins, Wavy fins, Lance and Offset fins (OSFs), Perforated fins, louvered fins, and pin fins, are employed in compact heat exchangers (CHEs). This analysis centers on a review of recent studies on heat transfer and pressure drop data in PFHEs for both the sensible heat transfer and the phase‐change cases. This review encompasses thermal design parameters, including the Nusselt number, hydraulic diameter, Reynolds number ( Re ), Colburn, and friction factors for PFHEs for the various types of fins. The gaps and uncertainties remain concerns on the Colburn ( j ) and friction factor ( f ) data, although many correlations are available in the open domain. As functions of the Re other geometric parameters, including the fin height ( F h ), fin thickness ( F t ), fin pitch ( F p ), fin length ( F l ), fin hole diameter ( F d ) in pin fins, fin hole diameter ( H d ), and fin hole pitch ( H p ) in perforated wavy fins, this review seeks to determine the most appropriate j and f factor correlations, which are crucial parameters for all types of PFHE fins. This review intends to unify both experimental and numerical data available in the literature. It details the pros and cons of various j and f data, along with their correlations, for readers or designers who require this information. A relative assessment of the j and f correlation data from different authors is made for plain, wavy, louvered, pin, and OSFs employed in CHEs. Moreover, gaps have been identified, indicating the necessity for additional j and f factors data in compact PFHEs. Very limited data are available for these types of fins with respect to two‐phase flows, which are also reviewed and listed. This review will assist practitioners involved in the design of CHEs in significantly reducing the number of iterations by allowing them to choose suitable correlations from this review.