基于有限元的机器学习算法研究通过拉伸表面的陀螺纳米流体流动

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Priyanka Chandra, Raja Das
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引用次数: 3

摘要

基于机器学习的Levenberg-Marquardt算法与反向传播神经网络(BLM - NN)在本研究中以动态方式使用,以检查包含回旋微生物的纳米流体的二维边界层流动,这些微生物流过可拉伸的垂直倾斜表面(NGM - ISSFM),浸入多孔介质中。通过在MATLAB中改变流动模型的五个关键参数,采用一种经过广泛验证的有限元方法(FEM)来生成BLM - NN的参考数据集。这种创新方法的主要目标是最大限度地减少较长的执行时间(对于更多的元素)和更昂贵的数字计算机要求,这是选择FEM的主要障碍,并且为了获得整个函数而不是其他数值方法通常产生的离散解。为了估计NGM - ISSFM模型在不同场景下的结果,我们对BLM - NN进行了训练、测试和验证。几个使用基于MSE指标的BLM - NN实现通过描述性统计显示了性能的准确性和有效性。结果表明,随着普朗特数的增加,微生物的温度分布和密度分布急剧下降,这意味着需要低普朗特数的流体来提高传热速率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Finite-element-based machine-learning algorithm for studying gyrotactic-nanofluid flow via stretching surface

Finite-element-based machine-learning algorithm for studying gyrotactic-nanofluid flow via stretching surface

The Levenberg–Marquardt algorithm with back-propagated neural network (BLM-NN) based on machine learning is used in a dynamic fashion in this study to examine the 2D boundary layer flow of a nanofluid comprising gyrotactic microorganisms flowing across a stretchable vertically inclined surface (NGM-ISSFM), immersed in a porous medium. An extensively verified finite-element method (FEM) is used to produce the reference data set for BLM-NN by altering five crucial parameters of the flow model in MATLAB. The main objective of this innovative approach is to minimize longer execution times (for larger number of elements) and more expensive digital computer requirements that are the key barriers to opting the FEM, and in order to obtain the entire function instead of the discrete solution that other numerical methods typically produce. To estimate the NGM-ISSFM model's result for diverse scenario, BLM-NN is trained, tested, and validated. Several BLM-NN implementations using MSE-based indices have shown the performance's veracity and validity through descriptive statistics. The results show that when the Prandtl number increases, the temperature profile and density profile of microorganisms fall dramatically, implying that a fluid with a low Prandtl number is required to enhance the rate of heat transmission.

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来源期刊
International Journal for Numerical Methods in Fluids
International Journal for Numerical Methods in Fluids 物理-计算机:跨学科应用
CiteScore
3.70
自引率
5.60%
发文量
111
审稿时长
8 months
期刊介绍: The International Journal for Numerical Methods in Fluids publishes refereed papers describing significant developments in computational methods that are applicable to scientific and engineering problems in fluid mechanics, fluid dynamics, micro and bio fluidics, and fluid-structure interaction. Numerical methods for solving ancillary equations, such as transport and advection and diffusion, are also relevant. The Editors encourage contributions in the areas of multi-physics, multi-disciplinary and multi-scale problems involving fluid subsystems, verification and validation, uncertainty quantification, and model reduction. Numerical examples that illustrate the described methods or their accuracy are in general expected. Discussions of papers already in print are also considered. However, papers dealing strictly with applications of existing methods or dealing with areas of research that are not deemed to be cutting edge by the Editors will not be considered for review. The journal publishes full-length papers, which should normally be less than 25 journal pages in length. Two-part papers are discouraged unless considered necessary by the Editors.
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