PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
November 20, 2025ASME Journal of Heat and Mass Transfer0 citationsOpen Access

Numerical formulation of Colburn j factor and Fanning friction f factor correlations for offset strip fins in compact heat exchangers using Computational Fluid Dynamics

View Full Paper
TSTanish SamantaGTGokula Krishna TavvaCRChennu Ranganayakulu

Key Points

  • The new correlations for j and f factors improve predictions in compact heat exchangers.
  • 96% of j factor and 100% of f factor values aligned within ±15% of numerical findings.
  • Analysis employs computational fluid dynamics techniques across varied Reynolds numbers.
  • The design tool offers a reliable method for optimizing heat exchanger configurations.

Abstract

Abstract This paper presents the development of continuous correlations for the Colburn j factor and Fanning friction f factor in rectangular offset strip fins used in compact heat exchangers, based on a numerical study conducted using computational fluid dynamics. The simulations span Reynolds numbers from 300 to 6000 encompassing laminar, transitional, and turbulent regimes. Key geometric fin parameters are varied to analyse their impact on the heat transfer efficiency and pressure drop across the fin. The computational method is validated through comparison of the numerical results against established experimental datasets 2, 3, 4, 7. The new correlations demonstrate strong predictive capability, with 96% of j factor and 100% of f factor values falling within ±15% of the values predicted by the present numerical study, showing mean deviations of 7% and 6%, respectively. In contrast to existing single-regime models, the proposed correlations provide continuous predictions across flow regimes while capturing higher-order geometric effects. The novel correlations presented in this paper offer a reliable design tool for optimizing compact heat exchanger configurations, minimizing the need for extensive experimental iteration, and expanding applicability to a broader design space.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Samanta et al. (2025) studied this question.

synapsesocial.com/papers/6924f084c0ce034ddc3502e5https://doi.org/10.1115/1.4070448
Ask AI
Helpful
Bookmark
Share
View Full Paper