人工神经网络分析三角形形状几何学混合对流的影响,使用水基Al2O3纳米流体
M N Hudha1, Md Jahid Hasan2, T Bairagi1
1Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
PloS one
|September 13, 2024
概括
本研究使用人工神经网络 (ANN) 来分析三角腔中的磁动力学 (MHD) 混合对流. ANN准确地预测了水/Al2O3纳米流体中的热传递,显示了更高固体体积分的热传递改善.
科学领域:
- 流体动力学 流体动力学
- 热传递热量转移的方法
- 人工智能的人工智能
背景情况:
- 磁动力学 (MHD) 混合对流在各种工程应用中至关重要.
- 与基础流体相比,纳米流体提供了增强的热性能.
- 三角形腔呈现出一种独特的几何结构,用于研究流体流动和热传递.
研究的目的:
- 使用人工神经网络 (ANN) 调查三角腔中的流体流动和热传递.
- 为了分析磁动力学 (MHD) 水/Al2O3纳米流体的混合对流.
- 使用ANN预测复杂的相互作用,以获得新的几何配置.
主要方法:
- 对于模拟,使用有限元分析.
- 用莱文伯格-马奎特反向传播的人工神经网络 (ANN) 用于预测.
- 参数研究包括理查森数 (Ri),哈特曼数 (Ha),雷诺兹数 (Re) 和固体体积分数 (φ).
主要成果:
- 增加固体体积分增加了所有测试条件的热传递.
- 较高的Ri和Re值增加了努塞尔特数,而较高的Ha值则减少了它.
- ANN 证明了高精度,平均平方误差为 1.05200e-6 和 R 值为 0.999988.
结论:
- 在复杂的几何形状中,ANN是预测MHD混合对流的强大工具.
- 纳米流体的特性显著影响热传递特性.
- 这项研究为优化三角腔中的热传输提供了宝贵的见解.
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