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开发人工神经网络方法来预测纳米流体在多孔封闭体内的热传输分析
Saleem Nasir1,2, Abdallah S Berrouk3,4, Taza Gul5
1Mechanical Engineering Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. saleem.nasir@ku.ac.ae.
Scientific reports
|December 5, 2023
概括
混合纳米流体 (Ag+MgO/H2O) 在多孔腔中显著增强热传输. 人工神经网络 (BRT-ANN) 准确预测这些热性能改进,帮助热传输系统设计.
科学领域:
- 热传递是一种传递热量的过程.
- 流体动力学 流体动力学
- 材料科学是一种材料科学.
背景情况:
- 混合纳米流体 (Ag+MgO/H2O) 与传统流体相比,提供了增强的热性能.
- 了解多孔介质中的热传输对于优化热系统至关重要.
研究的目的:
- 为了研究热传输对混合 (Ag+MgO) 纳米流体在多孔腔内的热传输影响.
- 利用人工神经网络 (贝叶斯规范化方法 - BRT-ANN) 进行建模和预测.
主要方法:
- 一个数值方案生成了一个数据集用于训练BRT-ANN模型.
- 经过训练的BRT-ANN模型在不同的参数下预测了热量和流量传输特征.
- 模拟分析了磁场,孔隙性和纳米粒子体积分数的影响.
主要成果:
- BRT-ANN模型准确预测了热量和流量传输,并与数值模拟进行了验证.
- 分析了等热体,流线和传热分布.
- 混合纳米流体 (Ag+MgO/H2O) 与基础流体相比显示出优越的热性能.
结论:
- 人工神经网络 (BRT-ANN) 是预测多孔腔中的混合纳米流体行为的有效工具.
- 该研究强调了Ag+MgO/H2O纳米流体在传热应用中增强热性能方面的潜力.
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