一个机器学习分析混合纳米流体在两个同轴圆柱之间流动
Kiran Batool1, Sadia Shakir2, Saima Zainab2
1Department of Mathematics, The Women University Multan, Multan, 60000, Pakistan. kiranbatool.60088@gmail.com.
Scientific reports
|October 1, 2025
概括
这项研究表明,与单颗粒液体相比,混合纳米流体可提高7.09%的热传递. 人工神经网络准确地预测磁动力热系统的性能.
科学领域:
- 流体动力学 流体动力学
- 热传递是一种热传递.
- 纳米技术纳米技术
背景情况:
- 在有限的几何结构中研究热传递和流体流动对于热管理至关重要.
- 混合纳米流体比传统流体提供了增强的热物理性能.
- 磁动力学在控制流体行为和传热方面发挥着重要作用.
研究的目的:
- 分析混合纳米流体 (水中的磁铁/多壁碳纳米管) 的稳定,不可压缩,传热和轴对称流动.
- 探索旋转,磁场和不同流量参数对混合纳米流体的影响.
- 开发和验证使用人工神经网络计算高效的预测模型.
主要方法:
- 使用 MATLAB 的 bvp4c 解决器,对边界值问题的数值解决.
- 通过Hamilton-Crosser混合模型评估有效的热物理性质.
- 开发一个莱文伯格-马奎特训练的人工神经网络,用于快速预测.
主要成果:
- 增加的磁强度和布林克曼数由于粘性和朱尔加热而提高温度.
- 较高的雷诺兹数增加了压力梯度.
- 混合纳米流体与单颗粒纳米流体相比,显示出7.09%的更高热传递和增加的壁切应力.
结论:
- 混合纳米流体展示了卓越的传热性能.
- 综合数值和人工智能方法提供了磁动力学热系统的准确和高效分析.
- 开发的神经网络准确地预测系统行为,使得快速的参数研究.
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