| 洪志伟,柴敏,郑水华.基于双组分双流体模型的纳米流体流动沸腾传热特性研究[J].高技术通讯(中文),2026,36(4):430~440 |
| 基于双组分双流体模型的纳米流体流动沸腾传热特性研究 |
| Investigation of nanofluid flow boiling heat transfer characteristics based on a two-component two-phase flow model |
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| DOI：10. 3772 / j. issn. 1002 - 0470. 2026. 04. 010 |
| 中文关键词: 纳米流体； 流动沸腾； 双组分双流体模型； 数值模拟 |
| 英文关键词: nanofluids, flow boiling, two-component two-fluid model, numerical simulation |
| 基金项目: |
| 作者 | 单位 | | 洪志伟 | (浙江工业大学机械工程学院化工机械设计研究所杭州 310023) | | 柴敏 | | | 郑水华 | |
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| 摘要点击次数: 38 |
| 全文下载次数: 29 |
| 中文摘要: |
| 采用双组分双流体模型对纳米流体在水平圆管内流动沸腾过程进行数值模拟，借助用户自定义函数编程研究了不同雷诺(Re)数、纳米流体种类及体积分数对于流动与传热的影响。结果表明，纳米颗粒的加入使得整体的温度下降，相变过程产生的蒸汽量也更少，随着颗粒体积分数的增加，这种现象更加明显。三氧化二铝(Al2O3)纳米流体有着较高的传热系数，随着颗粒体积分数的增加，传热系数增加的速率越来越大，但是随之压降也相应上升，在高Re数时，纳米流体的压降最大增加了约146%。对熵产进行分析发现摩擦熵产随着Re数的增加而增大，而热熵产却表现出相反的趋势，并且在高Re数下更为显著，随着纳米颗粒体积分数的增加，热熵产进一步减小。最后采用性能评价因子（performance evaluation criterion，PEC）对传热过程进行综合分析发现，颗粒的加入是有利于传热过程的，并且随着颗粒体积分数的增加，这种增强效应更加显著。对于纳米流体来说，Al2O3的综合传热能力最强，二氧化钛（TiO2）次之，而铜（Cu）纳米流体最弱。 |
| 英文摘要: |
| A numerical simulation of the flow boiling process in horizontal circular tubes of nanofluids was conducted using a two-component two-phase flow model. The effects of different Reynolds numbers (Re), nanofluid types and volume fractions on flow and heat transfer were investigated through user-defined function (UDF) programming. The results indicated that the addition of nanoparticles led to an overall decrease in temperature and a reduced amount of steam produced during the boiling process, with the effects becoming more pronounced as the particle volume fraction increased. The study found that Al2O3 nanofluids exhibited a higher heat transfer coefficient, which increased at an accelerating rate with the volume fraction of particles, although the pressure drop also rose correspondingly. At high Reynolds numbers, the pressure drop in nanofluids was observed to increase by up to approximately 146%. An entropy production analysis revealed that friction entropy production increased with higher Reynolds numbers, while thermal entropy production showed the opposite trend, being more significant at high Reynolds numbers, and further decreasing with the addition of nanoparticle volume fraction. Finally, a comprehensive analysis of the heat transfer process using the performance evaluation criterion (PEC) found that the inclusion of particles was beneficial to the heat transfer process, with this enhancement effect becoming more apparent as the particle volume fraction increased. Among the nanofluids studied, Al2O3 displayed the strongest overall heat transfer capabilities, followed by TiO2, while Cu nanofluids exhibited the weakest. |
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