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  4. Experimental investigation and ANN prediction of heat transfer enhancement in a heat exchanger tube utilizing twin corrugated twisted tapes
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Experimental investigation and ANN prediction of heat transfer enhancement in a heat exchanger tube utilizing twin corrugated twisted tapes

Author(s)
Du, Y.
Wongcharee, K.
Thianpong, C.
Chuwattanakul, V.
Chamoli, S.
Chokphoemphun, S.
Eiamsa-ard, S.
Date Issued
December 1, 2025
Type
Article
DOI
10.1007/s10973-025-15077-9
Abstract
This report introduces a novel twin-corrugated twisted tape (TC-TT) insert designed to enhance heat transfer in exchanger tubes. The key innovation lies in the twin-corrugated structure, which generates a twin-swirl flow effect. The corrugated surface synergistically increases flow disturbance and expands the effective heat transfer area. The studied parameters were twist ratios (y/w = 3.0, 3.5, and 4.0) and corrugation angles (θ = 45°, 60°, 75°, and 90°) at 6,000 ≤ Re ≤ 20,000. The results show that using twin-corrugated twisted tapes increases the average Nusselt number by roughly 60–135% compared to a plain tube and by 16–35% compared to a conventional single-twisted tape, confirming the effectiveness of this structural modification. This enhancement is primarily due to the combination of double swirling-flows and enhanced effective heat transfer generated by the corrugated surface. Reducing the corrugation angle (θ) and twist ratio (y/w) led to increases in the Nusselt number (Nu), friction factor (f), and thermal performance factor (TPF). Within the studied range, the Nusselt number, friction factor, and thermal performance factor reached maximum values of 5.18, 0.153, and 1.44, respectively, at a twist ratio of 3.0, a corrugation angle of 45°, and Re = 6,000. Regression analysis was utilized to develop correlations for the Nu and f, considering the Re, Pr, y/w, and θ as influencing variables. The proposed correlations for predicting the friction factor and Nusselt number have errors within ±3% and ±2%, respectively. In addition, an artificial neural network (ANN) was developed for predicting the thermal performance values occurring below the experimental study range. The optimal state ANN model shows remarkable prediction accuracy with R2 of 0.965.
Citation
Journal of Thermal Analysis and Calorimetry, 150(27), 22849-22869, 2025
Subjects

Artificial neural net...

Heat exchanger

Heat transfer

Thermal performance

Twin corrugated twist...

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