書誌事項
- タイトル別名
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- Prediction for Flow Boiling Heat Transfer in Small Diameter Tube Using Deep Learning
- 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>
収録刊行物
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- 混相流
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混相流 31 (4), 412-421, 2017
日本混相流学会
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詳細情報 詳細情報について
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- CRID
- 1390001204437592704
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- NII論文ID
- 130006316071
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- NII書誌ID
- AN10088286
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- ISSN
- 18815790
- 09142843
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- NDL書誌ID
- 028747145
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可