PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 17, 2025Results in Engineering7 citationsOpen Access

A high-accuracy CFD-based correlation for single-phase turbulent flow in axial multi-tube heat exchangers

View Full Paper
SASamer AliRTRani TaherJFJalal Faraj

Key Points

Key points are not available for this paper at this time.

Abstract

This study presents a CFD-based investigation of a counter-flow multi-tube heat exchanger (MTHX) featuring a 4 m -long shell of 40 mm diameter and configurations of 2 or 3 inner tubes with diameters D i = 5.77 , 7.07, and 10.0 mm . A total of 60 RANS–SST simulations were performed over turbulent flow regimes, with hot-side Reynolds numbers ranging from 7 , 000 to 24 , 000 and cold-side Reynolds numbers from 4 , 000 to 17 , 000 . The computed overall heat transfer coefficients U span 1 , 000 – 2 , 500 W m − 2 K − 1 . A mesh-independence study confirmed discretization uncertainty below 0.089%. The resulting dataset was used to construct a compact six-parameter correlation that expresses U as a function of the Reynolds numbers, tube-to-tube spacing ratio, and tube count. Cross-validation yielded a mean absolute percentage error (MAPE) of 1.1% and an R 2 of 0.998. Propagated uncertainty analysis shows a typical 95% confidence half-width of 13 W m − 2 K − 1 . Benchmarking against classical and recent literature models—including Dittus–Boelter, Gnielinski, Sieder–Tate, and a recent overall- U correlation—demonstrates that the proposed model halves the median prediction error to 1%. Sensitivity analysis identifies the hot-side hydraulic diameter and Reynolds number as the most influential parameters. The proposed correlation thus offers a high-fidelity yet computationally efficient tool for the design of axial multi-tube exchangers under turbulent liquid conditions, removing the need for repeated CFD simulations across varying geometries and flow regimes.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ali et al. (2025) studied this question.

synapsesocial.com/papers/6a62999bd5329adc8402ae0dhttps://doi.org/10.1016/j.rineng.2025.107219
Ask AI
Helpful
Bookmark
Share
View Full Paper