人工神经网络范式的磁热行为在触角的过度波动混合-纳米流体流动
Tazeen Athar1, Hamid Qureshi2, Taseer Muhammad3
1Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology University, Islamabad, Pakistan.
Scientific reports
|December 30, 2025
概括
这项研究研究了使用人工智能 (AI) 的磁性水力动力 (MHD) 触点-过度波动混合-纳米流体流动. 这项研究提高了纸张生产和金属冷却等应用中的热传递.
科学领域:
- 流体动力学 流体动力学
- 纳米技术 纳米技术
- 磁性水电动力学 (MHD) 是一个学科.
背景情况:
- 混合纳米流体,如乙烯基醇与铜和化纳米粒子,对于先进的传热应用至关重要.
- 在各种物理条件下了解这种流体的流动行为对于优化工业过程至关重要.
研究的目的:
- 为了分析一个磁性水力动力学 (MHD) 触点-超标混合-纳米流体在指数式拉伸的表面上的流动特征.
- 调查边界滑动,朱尔加热,热辐射和对流条件对热交换率的影响.
- 应用基于人工智能 (AI) 的机器学习 (ML),使用莱文伯格-马卡特算法 (LMA) 建模流体动力学.
主要方法:
- 使用部分微分方程 (PDEs) 进行流体流动的数学建模.
- 通过相似性转换将PDE转换为普通微分方程 (ODE).
- 使用人工智能 (AI) 方法,特别是机器学习 (ML) 莱文伯格-马卡特算法 (LMA),用于数值模拟和分析.
主要成果:
- 增加的磁性和弹性-粘性阻力比率导致流体流量减少.
- 更高的洛伦茨参数和环境温度差异增强了温度概况.
- 人工神经网络-莱文伯格-马奎特算法 (ANN-LMA) 证明了高精度,具有出色的回归匹配和快速收.
结论:
- 这项研究成功地模拟了MHD触点-超标混合-纳米流体流,突出了关键参数对传热的影响.
- 应用ANN-LMA为近似复杂流体动力学模型提供了一种新且高效的方法.
- 这些发现适用于纸张生产,金属板冷却和晶体生长等行业.
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