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Updated: May 26, 2025

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在Eyring-Powell流体模型中基于MHD辐射热传输的多层神经网络评估

Muflih Alhazmi¹, Zahoor Shah², Muhammad Asif Zahoor Raja³

¹Department of Mathematics, College of Science, Northern Border University, Arar, Saudi Arabia.

|February 24, 2025

概括

人工智能 (AI) 准确地模拟了Eyring Powell流体中的磁动力学 (MHD) 辐射热,使用具有贝叶斯规范化的多层神经网络 (MNNs-BRP). 这种人工智能方法为复杂的流体动力学研究提供了可靠和精确的方法.

科学领域:

流体动力学流体动力学
人工智能的人工智能
热传递是一种热传递.

背景情况:

科学研究中的复杂物理模型从人工智能 (AI) 解决方案中受益.
磁动力学 (MHD) 辐射热传递在非均加热的埃林威尔流体 (EPF-MHD-RHS) 中提出了一个复杂的挑战.
评估流体动力学现象需要强大而准确的预测模型.

研究的目的:

使用多层神经网络与贝叶斯规范化程序 (MNNs-BRP) 结合,用于评估EPF-MHD-RHS中的MHD辐射热量.
分析诸如混合对流,普兰特尔数和热辐射等参数对动量和热传递的影响.
开发一种高度准确和可靠的基于人工智能的流体动力学预测模型.

主要方法:

使用相似性转换,管理部分微分方程 (PDEs) 被转换为普通微分方程 (ODEs).
使用MNNs-BRP模型创建和训练,测试和验证了一个数据集.
模型性能使用错误历史图,平均平方误差 (MSE) 分析和回归分析进行评估.

主要成果:

多国企业-BRP模型显示出高准确性,其中小微企业位于 [10^-09 - 10^-04] 区间.
在八种不同的条件下实现了精确的性能,平均误差约为3.25E-13.
流体温度随着普兰特尔数的增加而下降,随着热量排放而增加;速度受到混合对流,热量产生/吸收,磁场和分层参数的影响.

结论:

开发的基于人工智能的模型为评估复杂的流体动力学问题提供了强大的,高度准确的解决方案.
人工智能与计算策略的整合为流体力学科学研究提供了新的范式.
该研究证实了人工智能驱动的预测模型在科学建模中的增强能力和可靠性.

关键词:

人工智能的人工智能是人工智能.贝叶斯规范化程序是贝叶斯的规范化程序.艾林 - 威尔流体磁性水力动力学磁性水力动力学多层多层的神经网络的神经网络的神经网络

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