Investigations of the curvature-driven effect on falling-film heat and mass transfer via numerical and machine-learning methods

Heat exchangers with curved structure are widely used in energy-intensive industries because of their compact design and high thermal efficiency. However, the curved characteristic brings up complexity and difficulty in accurately simulating heat transfer performance, while the influence of curvature has not yet been fully understood. In this study, a three-dimensional numerical model is developed to examine falling film flow and heat transfer characteristics on curved tube, with the corresponding variations in heat transfer coefficient (HTC) analyzed. Results show that curvature leads to circumferential asymmetry in liquid film thickness distribution, where the inner-side films are thicker than the outer-side films due to centrifugal force effects, while the axial thickness distribution remains nearly unaffected. Accordingly, curvature exhibits a directional dependence on HTC, with little axial influence but significant circumferential effects. The optimal curvature is explored to obtain the highest HTC improvement, and further increases in curvature lead to the reduction of HTC because of the occurrence of dry patches on outer-side and accumulation effect on inner-side. The fundamental relationships between other key parameters and HTC are also discussed under different curvatures. Additionally, an artificial neural network (ANN) model is established to predict heat transfer performance of curved tube. The ANN model demonstrates reliable performance prediction with a root mean square error of 0.0089, and parameter sensitivity analysis confirms that curvature has the greatest influence on HTC. These findings offer valuable insights for optimizing curved tube design, supporting the development of efficient heat exchangers, and contributing to sustainable energy utilization technologies. • Numerical case is set to study the effect of curvature on curved-tube heat transfer. • Curvature leads to inner-outer asymmetry of film thickness on circumference. • Optimal curvature is found for enhancing heat transfer of more than 35 %. • Curvature does not alter the influence mechanisms of other design parameters. • Heat transfer coefficient is most sensitive to curvature based on accurate ANN model.

Authors

Institutions

Publication Details

Journal
International Communications in Heat and Mass Transfer
Published
2025-11-15
DOI
https://doi.org/10.1016/j.icheatmasstransfer.2025.110045
Citations
1
Primary Topic
Fluid Dynamics and Thin Films
Type
article
Field-Weighted Citation Impact
1.33
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Investigations of the curvature-driven effect on falling-film heat and mass transfer via numerical and machine-learning methods

Zhuohang Zhang, Trevor Hocksun Kwan, Jielin Luo, Rong Jiang et al.
1 citations
International Communications in Heat and Mass Transfer
Fluid Dynamics and Thin Films
1.33
article

Investigations of the curvature-driven effect on falling-film heat and mass transfer via numerical and machine-learning methods

Zhuohang Zhang, Trevor Hocksun Kwan, Jielin Luo, Rong Jiang, Zihan Huang, Ding Zhang, Jiale Huang, Tian Hua Jiang, Shupeng Xie
article en
1 citations

Abstract

Heat exchangers with curved structure are widely used in energy-intensive industries because of their compact design and high thermal efficiency. However, the curved characteristic brings up complexity and difficulty in accurately simulating heat transfer performance, while the influence of curvature has not yet been fully understood. In this study, a three-dimensional numerical model is developed to examine falling film flow and heat transfer characteristics on curved tube, with the corresponding variations in heat transfer coefficient (HTC) analyzed. Results show that curvature leads to circumferential asymmetry in liquid film thickness distribution, where the inner-side films are thicker than the outer-side films due to centrifugal force effects, while the axial thickness distribution remains nearly unaffected. Accordingly, curvature exhibits a directional dependence on HTC, with little axial influence but significant circumferential effects. The optimal curvature is explored to obtain the highest HTC improvement, and further increases in curvature lead to the reduction of HTC because of the occurrence of dry patches on outer-side and accumulation effect on inner-side. The fundamental relationships between other key parameters and HTC are also discussed under different curvatures. Additionally, an artificial neural network (ANN) model is established to predict heat transfer performance of curved tube. The ANN model demonstrates reliable performance prediction with a root mean square error of 0.0089, and parameter sensitivity analysis confirms that curvature has the greatest influence on HTC. These findings offer valuable insights for optimizing curved tube design, supporting the development of efficient heat exchangers, and contributing to sustainable energy utilization technologies. • Numerical case is set to study the effect of curvature on curved-tube heat transfer. • Curvature leads to inner-outer asymmetry of film thickness on circumference. • Optimal curvature is found for enhancing heat transfer of more than 35 %. • Curvature does not alter the influence mechanisms of other design parameters. • Heat transfer coefficient is most sensitive to curvature based on accurate ANN model.

International Communications in Heat and Mass TransferVol. 170
Sun Yat-sen University (CN), Ningbo University of Technology (CN), Separation Systems (United States) (US)
Openalex Percentile: Top 16%
Fluid Dynamics and Thin Films
1.33
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.