Experimental Investigation on Heat Transfer Characteristics of an Open Loop Pulsating Heat Pipe(OLPHP) with Fin

Rahman, Lutfor; Saha, Pijush Kanti; Mir, Fariha; Totini, Asfia Tanjim; Nawrin, Sumaiya; Ali, Mohammad

doi:10.1016/j.proeng.2015.05.018

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…The reason was that the heat transfer phenomenon was more intense resulting in the improvement of the heat transfer effect. This result was consistent with the experimental results in literature[15,17]. However, when Di/Do was 0.6, there was an opposite trend that the Reff increases with the increase of Q.…”

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confidence: 92%

“…Taslimifar [15] studied the influence of working fluid, heat flux power, inclination angle, and other variables on the start-up performance and steady-state performance of open PHPs. The influence of sensible heat, finned or not and solid nanoparticles were studied in the literature [16][17][18]. Therefore, there are no geometric parameters such as length and diameters to study…”

Section: Introductionmentioning

confidence: 99%

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Heat transfer performance and prediction of open pulsating heat pipe for self-cooling cutting tool

Bao

Xing

et al. 2022

Int J Adv Manuf Technol

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The pulsating heat pipe (PHP) can be used to transfer massive heat to reduce the thermal damage to the cutting tool when machining difficult-to-cut materials. To select a better open PHP, the heat transfer performance was experimentally investigated in this study. The operating characteristics of different types of working fluids under different heat flux were analyzed compared with the closed PHP. Visual experiments were established to verify the experimental characteristics. The effects of heat flux, length, ratio of inner/outer diameter, and inclination angle on equivalent thermal resistance were analyzed. Based on the experimental data and four boosting integrating learning methods, a model for heat transfer performance prediction was proposed. The prediction model based on the CatBoost method had better goodness-of-fit and the best prediction effect. The R 2 , MAPE, and RMSE of the validation set were the best, which are 0.9258, 7.2564, and 0.1057 respectively. In addition, the contribution of input parameters to the output results was evaluated, while L, Di/Do, and Pr were the top three variables. Sub-tree structures used to explain the prediction model were also presented. This proposed prediction model can be used to select the most suitable open PHP before designing PHP self-cooling tools.

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confidence: 92%