4.7 Article

Enhanced turbulent convective heat transfer in helical twisted Multilobe tubes

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2022.123687

关键词

Convective heat transfer enhancement; Multi objectives optimization; Neural network model; Pareto front solution; Performance index

向作者/读者索取更多资源

This study evaluates the heat transfer performance in a helical twisted Multilobe tube and proposes an enhancement method by combining twisting and Multilobe strategies. The results from experiments and numerical simulations reveal that this combination improves convective heat transfer performance. Furthermore, the variation of Multilobe geometries has a negligible effect on the heat transfer performance.
Enhancing heat transfer performance has been the main interest in the thermal engineering field. Vari-ous enhancement methods have been proposed, including twisted and Multilobe tubes. Nevertheless, no study investigating the enhancement by combining both strategies has been reported. This study is thus conducted to numerically evaluate the turbulent convective heat transfer performance of Newtonian fluid flow in a helical twisted Multilobe tube. The model is validated against experimentally measured data of similar configurations. The effects of Multilobe geometries and Reynolds number were evaluated. The results revealed that combination of twisting and Multilobe profile enhance the secondary flow and, in turn, increases the convective heat transfer performance for straight geometries by up to 6.76%, while the addition of twisting of the tubes has a marginal effect on the heat transfer performance in helical mod-els. Furthermore, the variation of the number of lobes does not lead to significant changes in the heat transfer performance (less than 2% difference). Overall, bilobe cross-section shows superior performance in terms of overall performance when it is combined with a helical tube (1.08) or twisting (1.002) only, while pentalobe cross-section has better performance index in sophisticated flow with both helical tube and twisting of tube (1.037). Additionally, correlations are developed to predict the friction factor and Nusselt number in straight and helical tube. To find optimum configurations, Neural Network (NN) mod-els are developed based on the CFD result. By using multi objectives optimization, it was found that the circular straight pipe configurations with and without a twist are the ones closest to the optimum solu-tions. Meanwhile, helical pipe without a twist is the closest to the optimum solutions. These observations are aligned with the insights obtained from the CFD analysis. Crown Copyright (c) 2022 Published by Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据