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用机器学习算法对EMHD 3D微极三混合纳米流体流的太阳辐射纤薄板进行透分析

Shaik Jakeer¹, H Thameem Basha², Seethi Reddy Reddisekhar Reddy³

¹School of Technology, The Apollo University, Chittoor, A.P, 517127, India.

Scientific reports

|November 6, 2023

概括

这项研究分析了用于太阳能应用的新型三混合纳米流体中的热传递. 人工神经网络和数值方法揭示了诸如孔径和电场等参数如何影响流体流动和温度.

科学领域:

热传递是一种热传递.
纳米流体动力学是什么
可再生能源技术可再生能源技术

背景情况:

对可再生能源日益增长的需求需要高效的太阳能热系统.
纳米流体增强热传递,提高太阳能热系统的效率.
三混合纳米流体,如油中的Si+MgO+Ti,有望提高性能.

研究的目的:

分析电磁3D微极三混合纳米流体流的传热行为.
为了研究非里埃热量流和太阳辐射对变薄板的影响.
开发和利用人工神经网络 (ANN) 模型用于智能数值计算.

主要方法:

使用MLP向前向向后传播ANN与Levenberg-Marquardt算法进行数据分析.
在 MATLAB 中使用 Bvp4c 解析器来解决控制的非线性方程.
分析了无维参数的影响,包括孔径,粘度,电场,热放松时间,热源/散热器,热辐射,温度比和纳米粒子体积分数.

主要成果:

较高的孔径参数值降低了速度配置文件,但增加了温度配置文件.
温度概况显示与较高的电场参数值有反向关系.
螺旋粘度参数值的增加减少了微旋转配置文件.
随着纳米粒子体积分数的增加,积生成和贝詹数加剧.

结论:

该研究提供了关于三混合纳米流体在电磁和辐射影响下的复杂热传递动态的见解.
该ANN模型有效地分析流体运输特性和参数影响.
这些发现有助于优化纳米流体,以改善太阳能热能转换.

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