通过LM优化的神经网络方法,通过一个气对混合纳米流体动力学的AI驱动的预测
Muhammad Imran1, Wantao Jia1, Syed Tauseef Saeed2
1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, 710072, China.
Scientific reports
|October 24, 2025
概括
这项研究探讨了混合纳米流体流与化学反应和传热效应. 人工神经网络准确地预测了工程应用中的增强热量和质量转移.
科学领域:
- 流体动力学 流体动力学
- 热传递热量转移的方法
- 纳米技术 纳米技术
背景情况:
- 混合纳米流体 (HNFs) 提供增强的热性能.
- 混合对流流在许多工业过程中至关重要.
- 了解化学反应效应对于过程优化至关重要.
研究的目的:
- 为了研究化学反应和热源/散热器对HNF流在倾斜的透气表面的影响.
- 开发和验证用于HNF行为的一种混合数值和人工神经网络 (ANN) 模型.
- 分析各种参数对速度,温度和纳米粒子度的影响.
主要方法:
- 支配部分微分方程 (PDEs) 被转换为普通微分方程 (ODEs).
- 通过使用 MATLAB 的 bvp4c 解决程序,获得了数值解决方案.
- 一个优化的前人工神经网络 (FF-ANN) 使用莱文伯格-马奎特算法 (LMA) 集成用于建模.
主要成果:
- 发现孔隙性和倾斜性参数可以降低速度配置文件.
- 增加的纳米粒子度和热源/散热器效应增强了热分布.
- LMA-ANN模型表现出高预测准确度,最小的平均平方误差 (MSE).
结论:
- 高NF显示出改善热量和质量转移的巨大潜力.
- 增强ANN建模为分析复杂流体动力学提供了强大的工具.
- 该研究强调了在工程应用中将数值方法与ANN相结合的有效性.
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