Enhanced turbulent convective heat transfer in helical twisted Multilobe tubes
Enhancing heat transfer performance has been the main interest in the thermal engineering field. Various 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 t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
2023
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| Online Access: | http://scholars.utp.edu.my/id/eprint/34121/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142907873&doi=10.1016%2fj.ijheatmasstransfer.2022.123687&partnerID=40&md5=c8b65cce18546a20d36b1e94076b55ec |
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| Summary: | Enhancing heat transfer performance has been the main interest in the thermal engineering field. Various 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 models. 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) models 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 solutions. 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. © 2022 |
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