Aqueous solutions are increasingly introduced as the working fluids of pulsating heat pipe (PHP), to balance and optimise certain thermo-physical properties of pure water. In this paper, a first attempt is conducted to predict the heat transfer performance of closed PHP with pure water and available aqueous solutions based on a fully connected feed-forward artificial neural network (ANN). The notable dimensionless numbers, Kutateladze number (Ku), Bond number (Bo), Morton number (Mo), Jackob number (Ja), Prandtl number (Pr), Laplace number (La), d/L_e and number of turns (N) were selected as the inputs of the ANN model. It was found that the prediction of proposed ANN model posed a good agreement with experimental data with the MSE and correlation coefficient of 0.026 and 0.979, respectively. For 81.02% of the collected data, the absolute deviation of prediction for thermal resistance was within 25%. For 95% of collected data, the prediction of thermal resistance and temperature difference between the evaporation and condensation section fell within ±0.37 K/W, and ±17.1 K, respectively. The evaluation of evaporation and condensation section temperatures was also presented based on the proposed ANN model.

中文翻译：

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水和水溶液封闭脉动热管新型人工神经网络模型的开发

越来越多地引入水溶液作为脉动热管 (PHP) 的工作流体，以平衡和优化纯水的某些热物理特性。本文首次尝试基于全连接前馈人工神经网络 (ANN) 预测封闭式 PHP 与纯水和可用水溶液的传热性能。值得注意的无量纲数，库塔拉泽数（Ku），邦德数（Bo），莫顿数（Mo），杰克布数（Ja），普朗特数（Pr），拉普拉斯数（La），d / Le_和匝数（ñ) 被选为 ANN 模型的输入。结果表明，所提出的人工神经网络模型的预测与实验数据具有很好的一致性，MSE 和相关系数分别为 0.026 和 0.979。81.02%的采集数据，热阻预测的绝对偏差在25%以内。对于 95% 的收集数据，蒸发和冷凝段之间的热阻和温差预测值分别在 ±0.37 K/W 和 ±17.1 K 范围内。基于所提出的人工神经网络模型，还提出了蒸发和冷凝段温度的评估。