在MHD中模拟交叉扩散,Williamson纳米流体通过Morlet波形神经网络在非线性拉伸表面上的流动
Khalid Arif1, Syed Tauseef Saeed2, Muhammad Naeem Aslam3
1Department of Mathematics and Statistics, The University of Lahore, Lahore, 54770, Pakistan.
Scientific reports
|July 27, 2025
概括
这项研究引入了一种混合计算方法 (MWNNs-PSO-NNA) 来分析磁动力学 (MHD) 威廉森纳米流体流. 这种新技术准确地模拟了复杂的流体动力学,显示了工程应用的巨大潜力.
科学领域:
- 流体动力学 流体动力学
- 纳米技术纳米技术
- 计算科学 计算科学
背景情况:
- 磁动力学 (MHD) 描述了磁场中的流体运动.
- 与传统流体相比,纳米流体提供了增强的热性能.
- 威廉姆森纳米流体模型非牛顿流体行为.
研究的目的:
- 开发和验证一种新的混合计算方法,用于分析威廉姆森纳米流体的MHD流.
- 为了研究索雷特和杜福效应在多孔介质中的影响.
- 评估各种参数对速度,温度和度概况的影响.
主要方法:
- 类似性转换用于将部分微分方程转换为普通微分方程.
- 使用Morlet波形神经网络 (MWNNs) 和用神经网络 (NNA) 进行粒子群优化 (PSO) 的混合计算方法.
- 通过100个独立运行和统计指标 (MSE,TIC) 进行验证.
主要成果:
- 该MWNNs-PSO-NNA模型表现出高精度,低MSE和TIC值.
- 增加威廉森数,磁场,多孔度和拉伸指数减少速度.
- 布朗运动和威廉森数增加了温度;索雷特和布朗运动增加了注意力集中.
结论:
- 拟议的混合模型 (MWNNs-PSO-NNA) 在复杂的流体流量问题上具有计算稳定性和有效性.
- 该研究提供了关于威廉姆森纳米流体在各种物理影响下行为的见解.
- 这些发现适用于涉及纳米流体动力学的工程和应用科学.
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