使用AI预测MHD混合对流在半圆形腔内与混合纳米流体使用AI混合对流
Prosenjit Das1, Mohammad Arif Hasan Mamun1
1Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.
Heliyon
|October 10, 2024
概括
这项研究使用混合纳米流体在半圆封闭中数量分析磁动力混合对流. 更高的气速度和更大的气尺寸显著提高了热传输,纳米粒子组成也发挥着关键作用.
科学领域:
- 流体动力学和热传递 流体动力学和热传递
- 纳米材料科学 科学 纳米材料科学
- 磁性水电动力学 (MHD) 是一个学科.
背景情况:
- 调查了半圆形外内的磁动力学 (MHD) 混合对流.
- 专注于使用各种纳米流体和混合纳米流体,包括Al2O3-TiO2-SWCNT-水的热传递增强.
- 检查旋转的内部气对流体流动和热性能的影响.
研究的目的:
- 为了数值分析MHD混合对流传热转移在一个半圆形的外与旋转的气.
- 评估不同纳米流体和混合纳米流体的性能,包括Al2O3-水,TiO2-水,SWCNT-水和Al2O3-TiO2-SWCNT-水.
- 开发和验证一个人工神经网络 (ANN) 模型,用于预测传热结果.
主要方法:
- 磁动力混合对流的数值模拟.
- 使用了Al2O3-水,TiO2-水,SWCNT-水和Al2O3-TiO2-SWCNT-水混合纳米流体.
- 开发了一种用于预测的人工神经网络 (ANN) 模型,实现了高精度 (97.34%的训练,97.41%的测试为平均Nusselt数).
主要成果:
- 热传递在更高的气旋转速度 (21.12%对于 Ω=10) 和更大的气尺寸 (66.14%对于 SWCNT-水) 的情况下显著增加.
- 混合纳米流体中SWCNT和Al2O3的较高度提高了传热性能.
- 增加的哈特曼数减少了热传递,而更高的理查森数则增强了SWCNT-水纳米流体的热传递.
结论:
- 混合纳米流体,特别是那些含有较高SWCNT和Al2O3的纳米流体,提供了卓越的传热增强.
- 气旋转速度和尺寸是优化这种配置中的对流热传输的关键参数.
- 开发的ANN模型准确地预测了传热性能,为设计和优化提供了有价值的工具.
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