Prediction for Flow Boiling Heat Transfer in Small Diameter Tube Using Deep Learning

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  • ENOKI Koji
    電気通信大学大学院情報理工学研究科機械知能システム学専攻
  • SEI Yuichi
    電気通信大学大学院情報理工学研究科機械知能システム学専攻
  • OKAWA Tomio
    電気通信大学大学院情報理工学研究科機械知能システム学専攻
  • SAITO Kiyoshi
    電気通信大学大学院情報理工学研究科情報学専攻

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  • 人工知能の深層学習による円形微細流路内水平流の沸騰熱伝達の予測
  • ジンコウ チノウ ノ シンソウ ガクシュウ ニ ヨル エンケイ ビサイ リュウロ ナイ スイヘイリュウ ノ フットウ ネツ デンタツ ノ ヨソク

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<p>The applications of Artificial Intelligence ie AI show diversity in any fields. On the other hand, research of the predicting heat transfer regardless of single-phase or two-phase flow is still untouched. Therefore, we have confirmed usefulness using AI’s deep learning function on horizontal flow boiling heat transfer in flowing mini-channel that is actively researched. The effect of the surface tension in the mini-channel is large compared with conventional large tubes, and then the heat transfer mechanism is very complicated. For this reason, the numerical correlations of many existing researchers the prediction result is not good. However, the mechanistic correlation based on the visualization experiment, which the authors' research group published several years ago has very high precision. Therefore, in this research paper, we confirmed the effectiveness of using deep learning for predicting of the boiling heat transfer in mini-channel while comparing our correlation.</p>

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