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一个深度神经网络模型用于达西-福克海默混合纳米流体流中的热传输,具有激活能量
Mohammad Ayman-Mursaleen1, Syed Tauseef Saeed2, Saja Mohammad Almohammadi3
1Department of Mathematics, Faculty of Science, University of Ostrava, Mlýnská 702/5, 702 00, Ostrava, Czechia. ayman.mursaleen@osu.cz.
Scientific reports
|February 11, 2026
概括
本研究介绍了一种基于发动机油的混合纳米流体,用于工业应用中的增强热管理. 一个新的神经网络模型准确地预测了热量和质量转移,将计算时间减少了45%.
科学领域:
- 磁性水电动力学 (MHD) 是一个学科.
- 纳米流体的热传递
- 非牛顿流体动力学的流体动力学.
背景情况:
- 工业换热器需要高效的热管理,使用稳定的流体,如发动机油.
- 非牛顿流体,如卡森型流体,在工业应用中很常见.
- 混合纳米流体与基础流体相比,提供了增强的热性能.
研究的目的:
- 为了研究卡森型混合纳米流体 (含有Al2O3-TiO2纳米粒子的发动机油) 的MHD流量和热/质量转移.
- 为了结合热辐射的影响,达西-福克海默多孔介质,磁场和激活能量.
- 开发和验证一个优化的Morlet波形神经网络与粒子群优化和神经网络算法 (MWNN-PSO-NNA) 预测流体行为.
主要方法:
- 使用相似性转换,管理方程被转换为ODE.
- 使用Bvp4c解决器获得了数值解决方案.
- 使用Bvp4c解决方案来训练MWNN-PSO-NNA模型进行预测.
主要成果:
- 增加的磁场降低了速度;热辐射提高了温度 (15-25%).
- 更高的激活能量提高了物种度和热反应 (~30%).
- 混合纳米流体显示出优越的热性能;MWNN-PSO-NNA实现了>99%的精度,计算时间减少了45%.
结论:
- 该MWNN-PSO-NNA框架准确地预测了复杂的非牛顿混合纳米流体行为.
- 混合纳米流体配方对工业滑,热控制和冷却系统具有前景.
- 这项研究开创了基于发动机油的卡森混合纳米流体与达西-福克海默效应以及优化的MWNN-PSO-NNA框架的整合.
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